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Sample records for significant positive regression

  1. Polynomial regression analysis and significance test of the regression function

    International Nuclear Information System (INIS)

    Gao Zhengming; Zhao Juan; He Shengping

    2012-01-01

    In order to analyze the decay heating power of a certain radioactive isotope per kilogram with polynomial regression method, the paper firstly demonstrated the broad usage of polynomial function and deduced its parameters with ordinary least squares estimate. Then significance test method of polynomial regression function is derived considering the similarity between the polynomial regression model and the multivariable linear regression model. Finally, polynomial regression analysis and significance test of the polynomial function are done to the decay heating power of the iso tope per kilogram in accord with the authors' real work. (authors)

  2. Significance testing in ridge regression for genetic data

    Directory of Open Access Journals (Sweden)

    De Iorio Maria

    2011-09-01

    Full Text Available Abstract Background Technological developments have increased the feasibility of large scale genetic association studies. Densely typed genetic markers are obtained using SNP arrays, next-generation sequencing technologies and imputation. However, SNPs typed using these methods can be highly correlated due to linkage disequilibrium among them, and standard multiple regression techniques fail with these data sets due to their high dimensionality and correlation structure. There has been increasing interest in using penalised regression in the analysis of high dimensional data. Ridge regression is one such penalised regression technique which does not perform variable selection, instead estimating a regression coefficient for each predictor variable. It is therefore desirable to obtain an estimate of the significance of each ridge regression coefficient. Results We develop and evaluate a test of significance for ridge regression coefficients. Using simulation studies, we demonstrate that the performance of the test is comparable to that of a permutation test, with the advantage of a much-reduced computational cost. We introduce the p-value trace, a plot of the negative logarithm of the p-values of ridge regression coefficients with increasing shrinkage parameter, which enables the visualisation of the change in p-value of the regression coefficients with increasing penalisation. We apply the proposed method to a lung cancer case-control data set from EPIC, the European Prospective Investigation into Cancer and Nutrition. Conclusions The proposed test is a useful alternative to a permutation test for the estimation of the significance of ridge regression coefficients, at a much-reduced computational cost. The p-value trace is an informative graphical tool for evaluating the results of a test of significance of ridge regression coefficients as the shrinkage parameter increases, and the proposed test makes its production computationally feasible.

  3. A Demonstration of Regression False Positive Selection in Data Mining

    Science.gov (United States)

    Pinder, Jonathan P.

    2014-01-01

    Business analytics courses, such as marketing research, data mining, forecasting, and advanced financial modeling, have substantial predictive modeling components. The predictive modeling in these courses requires students to estimate and test many linear regressions. As a result, false positive variable selection ("type I errors") is…

  4. Significance tests to determine the direction of effects in linear regression models.

    Science.gov (United States)

    Wiedermann, Wolfgang; Hagmann, Michael; von Eye, Alexander

    2015-02-01

    Previous studies have discussed asymmetric interpretations of the Pearson correlation coefficient and have shown that higher moments can be used to decide on the direction of dependence in the bivariate linear regression setting. The current study extends this approach by illustrating that the third moment of regression residuals may also be used to derive conclusions concerning the direction of effects. Assuming non-normally distributed variables, it is shown that the distribution of residuals of the correctly specified regression model (e.g., Y is regressed on X) is more symmetric than the distribution of residuals of the competing model (i.e., X is regressed on Y). Based on this result, 4 one-sample tests are discussed which can be used to decide which variable is more likely to be the response and which one is more likely to be the explanatory variable. A fifth significance test is proposed based on the differences of skewness estimates, which leads to a more direct test of a hypothesis that is compatible with direction of dependence. A Monte Carlo simulation study was performed to examine the behaviour of the procedures under various degrees of associations, sample sizes, and distributional properties of the underlying population. An empirical example is given which illustrates the application of the tests in practice. © 2014 The British Psychological Society.

  5. Spontaneous regression in an ulcerated CK7 positive Merkel cell carcinoma

    Directory of Open Access Journals (Sweden)

    Anza Khader

    2015-01-01

    Full Text Available Merkel cell carcinoma is an aggressive and frequently lethal tumor of the elderly, associated with sun exposure and immunosuppression which is less common in the dark-skinned. We report the case of a 40-year-old woman who presented with multiple slowly progressive, mildly itchy ulcerated plaques of size ranging from 2 × 3 cm to 5 × 7 cm on the left knee of 1 year duration. Skin biopsy showed diffuse dermal infiltration by small round cells with molding of cells and lymphocyte infiltration. The cells stained positive for cytokeratin (CK 20, CK7, neuron-specific enolase, and chromogranin. The skin lesions underwent spontaneous regression within 1 month of skin biopsy and have not recurred during the past 2 years. The immune mechanisms triggered by biopsy possibly explain the spontaneous regression.

  6. Estimation of lung tumor position from multiple anatomical features on 4D-CT using multiple regression analysis.

    Science.gov (United States)

    Ono, Tomohiro; Nakamura, Mitsuhiro; Hirose, Yoshinori; Kitsuda, Kenji; Ono, Yuka; Ishigaki, Takashi; Hiraoka, Masahiro

    2017-09-01

    To estimate the lung tumor position from multiple anatomical features on four-dimensional computed tomography (4D-CT) data sets using single regression analysis (SRA) and multiple regression analysis (MRA) approach and evaluate an impact of the approach on internal target volume (ITV) for stereotactic body radiotherapy (SBRT) of the lung. Eleven consecutive lung cancer patients (12 cases) underwent 4D-CT scanning. The three-dimensional (3D) lung tumor motion exceeded 5 mm. The 3D tumor position and anatomical features, including lung volume, diaphragm, abdominal wall, and chest wall positions, were measured on 4D-CT images. The tumor position was estimated by SRA using each anatomical feature and MRA using all anatomical features. The difference between the actual and estimated tumor positions was defined as the root-mean-square error (RMSE). A standard partial regression coefficient for the MRA was evaluated. The 3D lung tumor position showed a high correlation with the lung volume (R = 0.92 ± 0.10). Additionally, ITVs derived from SRA and MRA approaches were compared with ITV derived from contouring gross tumor volumes on all 10 phases of the 4D-CT (conventional ITV). The RMSE of the SRA was within 3.7 mm in all directions. Also, the RMSE of the MRA was within 1.6 mm in all directions. The standard partial regression coefficient for the lung volume was the largest and had the most influence on the estimated tumor position. Compared with conventional ITV, average percentage decrease of ITV were 31.9% and 38.3% using SRA and MRA approaches, respectively. The estimation accuracy of lung tumor position was improved by the MRA approach, which provided smaller ITV than conventional ITV. © 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

  7. Reducing false-positive incidental findings with ensemble genotyping and logistic regression based variant filtering methods.

    Science.gov (United States)

    Hwang, Kyu-Baek; Lee, In-Hee; Park, Jin-Ho; Hambuch, Tina; Choe, Yongjoon; Kim, MinHyeok; Lee, Kyungjoon; Song, Taemin; Neu, Matthew B; Gupta, Neha; Kohane, Isaac S; Green, Robert C; Kong, Sek Won

    2014-08-01

    As whole genome sequencing (WGS) uncovers variants associated with rare and common diseases, an immediate challenge is to minimize false-positive findings due to sequencing and variant calling errors. False positives can be reduced by combining results from orthogonal sequencing methods, but costly. Here, we present variant filtering approaches using logistic regression (LR) and ensemble genotyping to minimize false positives without sacrificing sensitivity. We evaluated the methods using paired WGS datasets of an extended family prepared using two sequencing platforms and a validated set of variants in NA12878. Using LR or ensemble genotyping based filtering, false-negative rates were significantly reduced by 1.1- to 17.8-fold at the same levels of false discovery rates (5.4% for heterozygous and 4.5% for homozygous single nucleotide variants (SNVs); 30.0% for heterozygous and 18.7% for homozygous insertions; 25.2% for heterozygous and 16.6% for homozygous deletions) compared to the filtering based on genotype quality scores. Moreover, ensemble genotyping excluded > 98% (105,080 of 107,167) of false positives while retaining > 95% (897 of 937) of true positives in de novo mutation (DNM) discovery in NA12878, and performed better than a consensus method using two sequencing platforms. Our proposed methods were effective in prioritizing phenotype-associated variants, and an ensemble genotyping would be essential to minimize false-positive DNM candidates. © 2014 WILEY PERIODICALS, INC.

  8. Strategies for Testing Statistical and Practical Significance in Detecting DIF with Logistic Regression Models

    Science.gov (United States)

    Fidalgo, Angel M.; Alavi, Seyed Mohammad; Amirian, Seyed Mohammad Reza

    2014-01-01

    This study examines three controversial aspects in differential item functioning (DIF) detection by logistic regression (LR) models: first, the relative effectiveness of different analytical strategies for detecting DIF; second, the suitability of the Wald statistic for determining the statistical significance of the parameters of interest; and…

  9. Significant association between renal function and amyloid-positive area in renal biopsy specimens in AL amyloidosis

    Directory of Open Access Journals (Sweden)

    Kuroda Takeshi

    2012-09-01

    Full Text Available Abstract Background The kidney is a major target organ for systemic amyloidosis that often affects the kidney including proteinura, and elevated serum creatinine (Cr. The correlation between amount of amyloid deposits and clinical parameters is not known. The aim of this study was to clarify correlation the amyloid area in all renal biopsy specimen and clinical parameters. Methods Fifty-eight patients with an established diagnosis of AL amyloidosis participated in the study. All patients showed amyloid deposits in renal biopsies. We retrospectively investigated the correlation between clinical data and amyloid occupied area in whole renal biopsy specimens. Results The area occupied by amyloid was less than 10% in 57 of the 58 patients, and was under 2% in 40. For statistical analyses, %amyloid-positive areas were transformed to common logarithmic values (Log10%amyloid. Cr showed significant correlation with Log10%amyloid and estimated glomerular filtration rate (eGFR showed the significant negative correlation. Patient age, cleatinine clearance (Ccr, blood urea nitorogen, and urinary protein was not significantly correlated with Log10%amyloid. The correlation with other clinical factors such as sex, and serum concentrations of total protein, albumin, immunoglobulins, compliments was evaluated. None of these factors significantly correlated with Log10%amyloid. According to sex- and age- adjusted multiple linear regression analysis, Log10%amyloid had significant positive association with Cr and significant negative association with eGFR. Conclusion There is significant association between amyloid-positive area in renal tissue and renal function, especially Cr and eGFR. The level of Cr and eGFR may be a marker of amount of amyloid in renal tissue.

  10. Calculating the true level of predictors significance when carrying out the procedure of regression equation specification

    Directory of Open Access Journals (Sweden)

    Nikita A. Moiseev

    2017-01-01

    Full Text Available The paper is devoted to a new randomization method that yields unbiased adjustments of p-values for linear regression models predictors by incorporating the number of potential explanatory variables, their variance-covariance matrix and its uncertainty, based on the number of observations. This adjustment helps to control type I errors in scientific studies, significantly decreasing the number of publications that report false relations to be authentic ones. Comparative analysis with such existing methods as Bonferroni correction and Shehata and White adjustments explicitly shows their imperfections, especially in case when the number of observations and the number of potential explanatory variables are approximately equal. Also during the comparative analysis it was shown that when the variance-covariance matrix of a set of potential predictors is diagonal, i.e. the data are independent, the proposed simple correction is the best and easiest way to implement the method to obtain unbiased corrections of traditional p-values. However, in the case of the presence of strongly correlated data, a simple correction overestimates the true pvalues, which can lead to type II errors. It was also found that the corrected p-values depend on the number of observations, the number of potential explanatory variables and the sample variance-covariance matrix. For example, if there are only two potential explanatory variables competing for one position in the regression model, then if they are weakly correlated, the corrected p-value will be lower than when the number of observations is smaller and vice versa; if the data are highly correlated, the case with a larger number of observations will show a lower corrected p-value. With increasing correlation, all corrections, regardless of the number of observations, tend to the original p-value. This phenomenon is easy to explain: as correlation coefficient tends to one, two variables almost linearly depend on each

  11. Significant positive relationship between serum magnesium and muscle quality in maintenance hemodialysis patients.

    Science.gov (United States)

    Okazaki, Hisanori; Ishimura, Eiji; Okuno, Senji; Norimine, Kyoko; Yamakawa, Kenjiro; Yamakawa, Tomoyuki; Shoji, Shigeichi; Nishizawa, Yoshiki; Inaba, Masaaki

    2013-01-01

    Serum magnesium (Mg) levels have been associated with muscle performance in the general population. We hypothesized that serum Mg would be associated with muscle quality in hemodialysis patients. A total of 310 patients were examined (age: 58 ± 12 years, hemodialysis duration: 6.4 ± 6.0 years, 60.6% men, and 36.1% diabetics). Arm lean mass was measured by dual energy X-ray absorptiometry (DXA) on the dominant side. Arm muscle quality was defined as the ratio of the handgrip strength to the arm lean mass of the same side (kg/kg). Serum Mg was 1.15 ± 0.16 mmol/L (2.8 ± 0.4 mg/dL), being higher than the reference range of normal subjects. There was a significant negative correlation between muscle quality and age (r = -0.326, p<0.0001) and duration of hemodialysis (r = -0.253, p<0.0001). The muscle quality of the diabetics was significantly lower than that of the non-diabetics (p<0.001). There was a significant, positive correlation between muscle quality and serum Mg (r = 0.118, p<0.05), but not serum calcium or phosphate. In multiple regression analysis, age, gender, hemodialysis duration, diabetes, and serum Mg (β = 0.129, p<0.05) were significantly and independently associated with muscle quality (R(2) = 0.298, p<0.0001). These results demonstrated that a lower serum Mg concentration was significantly associated with poor muscle quality in hemodialysis patients. Further studies are needed to explore the mechanism by which lower serum Mg affects muscle quality.

  12. Primary tumor regression speed after radiotherapy and its prognostic significance in nasopharyngeal carcinoma: a retrospective study

    International Nuclear Information System (INIS)

    Zhang, Ning; Liu, Dong-Sheng; Chen, Yong; Liang, Shao-Bo; Deng, Yan-Ming; Lu, Rui-Liang; Chen, Hai-Yang; Zhao, Hai; Lv, Zhi-Qian; Liang, Shao-Qiang; Yang, Lin

    2014-01-01

    To observe the primary tumor (PT) regression speed after radiotherapy (RT) in nasopharyngeal carcinoma (NPC) and evaluate its prognostic significance. One hundred and eighty-eight consecutive newly diagnosed NPC patients were reviewed retrospectively. All patients underwent magnetic resonance imaging and fiberscope examination of the nasopharynx before RT, during RT when the accumulated dose was 46–50 Gy, at the end of RT, and 3–4 months after RT. Of 188 patients, 40.4% had complete response of PT (CRPT), 44.7% had partial response of PT (PRPT), and 14.9% had stable disease of PT (SDPT) at the end of RT. The 5-year overall survival (OS) rates for patients with CRPT, PRPT, and SDPT at the end of RT were 84.0%, 70.7%, and 44.3%, respectively (P < 0.001, hazard ratio [HR] = 2.177, 95% confidence interval [CI] = 1.480-3.202). The 5-year failure-free survival (FFS) and distant metastasis-free survival (DMFS) rates also differed significantly (87.8% vs. 74.3% vs. 52.7%, P = 0.001, HR = 2.148, 95% CI, 1.384-3.333; 91.7% vs. 84.7% vs. 66.1%, P = 0.004, HR = 2.252, 95% CI = 1.296-3.912). The 5-year local relapse–free survival (LRFS) rates were not significantly different (95.8% vs. 86.0% vs. 81.8%, P = 0.137, HR = 1.975, 95% CI, 0.976-3.995). By multivariate analyses, the PT regression speed at the end of RT was the only independent prognostic factor of OS, FFS, and DMFS (P < 0.001, P = 0.001, and P = 0.004, respectively). The 5-year FFS rates for patients with CRPT during RT and CRPT only at the end of RT were 80.2% and 97.1%, respectively (P = 0.033). For patients with persistent PT at the end of RT, the 5-year LRFS rates of patients without and with boost irradiation were 87.1% and 84.6%, respectively (P = 0.812). PT regression speed at the end of RT was an independent prognostic factor of OS, FFS, and DMFS in NPC patients. Immediate strengthening treatment may be provided to patients with poor tumor regression at the end of RT

  13. Effect of removing the common mode errors on linear regression analysis of noise amplitudes in position time series of a regional GPS network & a case study of GPS stations in Southern California

    Science.gov (United States)

    Jiang, Weiping; Ma, Jun; Li, Zhao; Zhou, Xiaohui; Zhou, Boye

    2018-05-01

    The analysis of the correlations between the noise in different components of GPS stations has positive significance to those trying to obtain more accurate uncertainty of velocity with respect to station motion. Previous research into noise in GPS position time series focused mainly on single component evaluation, which affects the acquisition of precise station positions, the velocity field, and its uncertainty. In this study, before and after removing the common-mode error (CME), we performed one-dimensional linear regression analysis of the noise amplitude vectors in different components of 126 GPS stations with a combination of white noise, flicker noise, and random walking noise in Southern California. The results show that, on the one hand, there are above-moderate degrees of correlation between the white noise amplitude vectors in all components of the stations before and after removal of the CME, while the correlations between flicker noise amplitude vectors in horizontal and vertical components are enhanced from un-correlated to moderately correlated by removing the CME. On the other hand, the significance tests show that, all of the obtained linear regression equations, which represent a unique function of the noise amplitude in any two components, are of practical value after removing the CME. According to the noise amplitude estimates in two components and the linear regression equations, more accurate noise amplitudes can be acquired in the two components.

  14. Methods for significance testing of categorical covariates in logistic regression models after multiple imputation: power and applicability analysis

    NARCIS (Netherlands)

    Eekhout, I.; Wiel, M.A. van de; Heymans, M.W.

    2017-01-01

    Background. Multiple imputation is a recommended method to handle missing data. For significance testing after multiple imputation, Rubin’s Rules (RR) are easily applied to pool parameter estimates. In a logistic regression model, to consider whether a categorical covariate with more than two levels

  15. Is adult gait less susceptible than paediatric gait to hip joint centre regression equation error?

    Science.gov (United States)

    Kiernan, D; Hosking, J; O'Brien, T

    2016-03-01

    Hip joint centre (HJC) regression equation error during paediatric gait has recently been shown to have clinical significance. In relation to adult gait, it has been inferred that comparable errors with children in absolute HJC position may in fact result in less significant kinematic and kinetic error. This study investigated the clinical agreement of three commonly used regression equation sets (Bell et al., Davis et al. and Orthotrak) for adult subjects against the equations of Harrington et al. The relationship between HJC position error and subject size was also investigated for the Davis et al. set. Full 3-dimensional gait analysis was performed on 12 healthy adult subjects with data for each set compared to Harrington et al. The Gait Profile Score, Gait Variable Score and GDI-kinetic were used to assess clinical significance while differences in HJC position between the Davis and Harrington sets were compared to leg length and subject height using regression analysis. A number of statistically significant differences were present in absolute HJC position. However, all sets fell below the clinically significant thresholds (GPS <1.6°, GDI-Kinetic <3.6 points). Linear regression revealed a statistically significant relationship for both increasing leg length and increasing subject height with decreasing error in anterior/posterior and superior/inferior directions. Results confirm a negligible clinical error for adult subjects suggesting that any of the examined sets could be used interchangeably. Decreasing error with both increasing leg length and increasing subject height suggests that the Davis set should be used cautiously on smaller subjects. Copyright © 2016 Elsevier B.V. All rights reserved.

  16. PA positioning significantly reduces testicular dose during sacroiliac joint radiography

    Energy Technology Data Exchange (ETDEWEB)

    Mekis, Nejc [Faculty of Health Sciences, University of Ljubljana (Slovenia); Mc Entee, Mark F., E-mail: mark.mcentee@ucd.i [School of Medicine and Medical Science, University College Dublin 4 (Ireland); Stegnar, Peter [Jozef Stefan International Postgraduate School, Ljubljana (Slovenia)

    2010-11-15

    Radiation dose to the testes in the antero-posterior (AP) and postero-anterior (PA) projection of the sacroiliac joint (SIJ) was measured with and without a scrotal shield. Entrance surface dose, the dose received by the testicles and the dose area product (DAP) was used. DAP measurements revealed the dose received by the phantom in the PA position is 12.6% lower than the AP (p {<=} 0.009) with no statistically significant reduction in image quality (p {<=} 0.483). The dose received by the testes in the PA projection in SIJ imaging is 93.1% lower than the AP projection when not using protection (p {<=} 0.020) and 94.9% lower with protection (p {<=} 0.019). The dose received by the testicles was not changed by the use of a scrotal shield in the AP position (p {<=} 0.559); but was lowered by its use in the PA (p {<=} 0.058). Use of the PA projection in SIJ imaging significantly lowers, the dose received by the testes compared to the AP projection without significant loss of image quality.

  17. The Health Significance of Positive Emotions in Adulthood and Later Life.

    Science.gov (United States)

    Ong, Anthony D; Mroczek, Daniel K; Riffin, Catherine

    2011-08-01

    A growing body of literature supports a link between positive emotions and health in older adults. In this article, we review evidence of the effects of positive emotions on downstream biological processes and meaningful clinical endpoints, such as adult morbidity and mortality. We then present relevant predictions from lifespan theories that suggest changes in cognition and motivation may play an important role in explaining how positive emotions are well maintained in old age, despite pervasive declines in cognitive processes. We conclude by discussing how the application of psychological theory can inform greater understanding of the adaptive significance of positive emotions in adulthood and later life.

  18. PA positioning significantly reduces testicular dose during sacroiliac joint radiography

    International Nuclear Information System (INIS)

    Mekis, Nejc; Mc Entee, Mark F.; Stegnar, Peter

    2010-01-01

    Radiation dose to the testes in the antero-posterior (AP) and postero-anterior (PA) projection of the sacroiliac joint (SIJ) was measured with and without a scrotal shield. Entrance surface dose, the dose received by the testicles and the dose area product (DAP) was used. DAP measurements revealed the dose received by the phantom in the PA position is 12.6% lower than the AP (p ≤ 0.009) with no statistically significant reduction in image quality (p ≤ 0.483). The dose received by the testes in the PA projection in SIJ imaging is 93.1% lower than the AP projection when not using protection (p ≤ 0.020) and 94.9% lower with protection (p ≤ 0.019). The dose received by the testicles was not changed by the use of a scrotal shield in the AP position (p ≤ 0.559); but was lowered by its use in the PA (p ≤ 0.058). Use of the PA projection in SIJ imaging significantly lowers, the dose received by the testes compared to the AP projection without significant loss of image quality.

  19. Least Squares Adjustment: Linear and Nonlinear Weighted Regression Analysis

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg

    2007-01-01

    This note primarily describes the mathematics of least squares regression analysis as it is often used in geodesy including land surveying and satellite positioning applications. In these fields regression is often termed adjustment. The note also contains a couple of typical land surveying...... and satellite positioning application examples. In these application areas we are typically interested in the parameters in the model typically 2- or 3-D positions and not in predictive modelling which is often the main concern in other regression analysis applications. Adjustment is often used to obtain...... the clock error) and to obtain estimates of the uncertainty with which the position is determined. Regression analysis is used in many other fields of application both in the natural, the technical and the social sciences. Examples may be curve fitting, calibration, establishing relationships between...

  20. An empirical study on open position risk assessment using VAR and regression analysis: A case study of Iranian banking industry

    Directory of Open Access Journals (Sweden)

    Elmira Mahmoudzadeh

    2012-10-01

    Full Text Available During the past few years, there have been tremendous fluctuations on different currencies. For instance, European common currency, Euro, has be fluctuated between 0.60 to 0.9 against US dollar. Therefore, it is important to study the behavior of currency valuations using different techniques. In this paper, we present an empirical study to measure the impact of different items on risk of foreign currency using value at risk (VaR and regression methods. The proposed model of this paper investigates whether the risk of open positions of six foreign currencies including US dollar, Euro, British Pound, Switzerland Frank, Norwegian Kroner and United Emirate Dirham increase during the time horizon. The proposed study of this paper uses historical daily prices of these currencies for a fiscal year of 2011 in one of private banks located in Iran and measures the relative risk. The results of the implementation of two methods of VaR and linear regression indicate that the risk of open positions increases during the time horizon.

  1. This research is to study the factors which influence the business success of small business ‘processed rotan’. The data employed in the study are primary data within the period of July to August 2013, 30 research observations through census method. Method of analysis used in the study is multiple linear regressions. The results of analysis showed that the factors of labor, innovation and promotion have positive and significant influence on the business success of small business ‘processed rotan’ simultaneously. The analysis also showed that partially labor has positive and significant influence on the business success, yet innovation and promotion have insignificant and positive influence on the business success.

    OpenAIRE

    Nasution, Inggrita Gusti Sari; Muchtar, Yasmin Chairunnisa

    2013-01-01

    This research is to study the factors which influence the business success of small business ‘processed rotan’. The data employed in the study are primary data within the period of July to August 2013, 30 research observations through census method. Method of analysis used in the study is multiple linear regressions. The results of analysis showed that the factors of labor, innovation and promotion have positive and significant influence on the business success of small busine...

  2. Improving sensitivity of linear regression-based cell type-specific differential expression deconvolution with per-gene vs. global significance threshold.

    Science.gov (United States)

    Glass, Edmund R; Dozmorov, Mikhail G

    2016-10-06

    The goal of many human disease-oriented studies is to detect molecular mechanisms different between healthy controls and patients. Yet, commonly used gene expression measurements from blood samples suffer from variability of cell composition. This variability hinders the detection of differentially expressed genes and is often ignored. Combined with cell counts, heterogeneous gene expression may provide deeper insights into the gene expression differences on the cell type-specific level. Published computational methods use linear regression to estimate cell type-specific differential expression, and a global cutoff to judge significance, such as False Discovery Rate (FDR). Yet, they do not consider many artifacts hidden in high-dimensional gene expression data that may negatively affect linear regression. In this paper we quantify the parameter space affecting the performance of linear regression (sensitivity of cell type-specific differential expression detection) on a per-gene basis. We evaluated the effect of sample sizes, cell type-specific proportion variability, and mean squared error on sensitivity of cell type-specific differential expression detection using linear regression. Each parameter affected variability of cell type-specific expression estimates and, subsequently, the sensitivity of differential expression detection. We provide the R package, LRCDE, which performs linear regression-based cell type-specific differential expression (deconvolution) detection on a gene-by-gene basis. Accounting for variability around cell type-specific gene expression estimates, it computes per-gene t-statistics of differential detection, p-values, t-statistic-based sensitivity, group-specific mean squared error, and several gene-specific diagnostic metrics. The sensitivity of linear regression-based cell type-specific differential expression detection differed for each gene as a function of mean squared error, per group sample sizes, and variability of the proportions

  3. Mortality risk prediction in burn injury: Comparison of logistic regression with machine learning approaches.

    Science.gov (United States)

    Stylianou, Neophytos; Akbarov, Artur; Kontopantelis, Evangelos; Buchan, Iain; Dunn, Ken W

    2015-08-01

    Predicting mortality from burn injury has traditionally employed logistic regression models. Alternative machine learning methods have been introduced in some areas of clinical prediction as the necessary software and computational facilities have become accessible. Here we compare logistic regression and machine learning predictions of mortality from burn. An established logistic mortality model was compared to machine learning methods (artificial neural network, support vector machine, random forests and naïve Bayes) using a population-based (England & Wales) case-cohort registry. Predictive evaluation used: area under the receiver operating characteristic curve; sensitivity; specificity; positive predictive value and Youden's index. All methods had comparable discriminatory abilities, similar sensitivities, specificities and positive predictive values. Although some machine learning methods performed marginally better than logistic regression the differences were seldom statistically significant and clinically insubstantial. Random forests were marginally better for high positive predictive value and reasonable sensitivity. Neural networks yielded slightly better prediction overall. Logistic regression gives an optimal mix of performance and interpretability. The established logistic regression model of burn mortality performs well against more complex alternatives. Clinical prediction with a small set of strong, stable, independent predictors is unlikely to gain much from machine learning outside specialist research contexts. Copyright © 2015 Elsevier Ltd and ISBI. All rights reserved.

  4. Multiple Regression Analysis of mRNA-miRNA Associations in Colorectal Cancer Pathway

    Science.gov (United States)

    Wang, Fengfeng; Wong, S. C. Cesar; Chan, Lawrence W. C.; Cho, William C. S.; Yip, S. P.; Yung, Benjamin Y. M.

    2014-01-01

    Background. MicroRNA (miRNA) is a short and endogenous RNA molecule that regulates posttranscriptional gene expression. It is an important factor for tumorigenesis of colorectal cancer (CRC), and a potential biomarker for diagnosis, prognosis, and therapy of CRC. Our objective is to identify the related miRNAs and their associations with genes frequently involved in CRC microsatellite instability (MSI) and chromosomal instability (CIN) signaling pathways. Results. A regression model was adopted to identify the significantly associated miRNAs targeting a set of candidate genes frequently involved in colorectal cancer MSI and CIN pathways. Multiple linear regression analysis was used to construct the model and find the significant mRNA-miRNA associations. We identified three significantly associated mRNA-miRNA pairs: BCL2 was positively associated with miR-16 and SMAD4 was positively associated with miR-567 in the CRC tissue, while MSH6 was positively associated with miR-142-5p in the normal tissue. As for the whole model, BCL2 and SMAD4 models were not significant, and MSH6 model was significant. The significant associations were different in the normal and the CRC tissues. Conclusion. Our results have laid down a solid foundation in exploration of novel CRC mechanisms, and identification of miRNA roles as oncomirs or tumor suppressor mirs in CRC. PMID:24895601

  5. Does the Magnitude of the Link between Unemployment and Crime Depend on the Crime Level? A Quantile Regression Approach

    Directory of Open Access Journals (Sweden)

    Horst Entorf

    2015-07-01

    Full Text Available Two alternative hypotheses – referred to as opportunity- and stigma-based behavior – suggest that the magnitude of the link between unemployment and crime also depends on preexisting local crime levels. In order to analyze conjectured nonlinearities between both variables, we use quantile regressions applied to German district panel data. While both conventional OLS and quantile regressions confirm the positive link between unemployment and crime for property crimes, results for assault differ with respect to the method of estimation. Whereas conventional mean regressions do not show any significant effect (which would confirm the usual result found for violent crimes in the literature, quantile regression reveals that size and importance of the relationship are conditional on the crime rate. The partial effect is significantly positive for moderately low and median quantiles of local assault rates.

  6. A Matlab program for stepwise regression

    Directory of Open Access Journals (Sweden)

    Yanhong Qi

    2016-03-01

    Full Text Available The stepwise linear regression is a multi-variable regression for identifying statistically significant variables in the linear regression equation. In present study, we presented the Matlab program of stepwise regression.

  7. Estimates of statistical significance for comparison of individual positions in multiple sequence alignments

    Directory of Open Access Journals (Sweden)

    Sadreyev Ruslan I

    2004-08-01

    Full Text Available Abstract Background Profile-based analysis of multiple sequence alignments (MSA allows for accurate comparison of protein families. Here, we address the problems of detecting statistically confident dissimilarities between (1 MSA position and a set of predicted residue frequencies, and (2 between two MSA positions. These problems are important for (i evaluation and optimization of methods predicting residue occurrence at protein positions; (ii detection of potentially misaligned regions in automatically produced alignments and their further refinement; and (iii detection of sites that determine functional or structural specificity in two related families. Results For problems (1 and (2, we propose analytical estimates of P-value and apply them to the detection of significant positional dissimilarities in various experimental situations. (a We compare structure-based predictions of residue propensities at a protein position to the actual residue frequencies in the MSA of homologs. (b We evaluate our method by the ability to detect erroneous position matches produced by an automatic sequence aligner. (c We compare MSA positions that correspond to residues aligned by automatic structure aligners. (d We compare MSA positions that are aligned by high-quality manual superposition of structures. Detected dissimilarities reveal shortcomings of the automatic methods for residue frequency prediction and alignment construction. For the high-quality structural alignments, the dissimilarities suggest sites of potential functional or structural importance. Conclusion The proposed computational method is of significant potential value for the analysis of protein families.

  8. Determinants of LSIL Regression in Women from a Colombian Cohort

    International Nuclear Information System (INIS)

    Molano, Monica; Gonzalez, Mauricio; Gamboa, Oscar; Ortiz, Natasha; Luna, Joaquin; Hernandez, Gustavo; Posso, Hector; Murillo, Raul; Munoz, Nubia

    2010-01-01

    Objective: To analyze the role of Human Papillomavirus (HPV) and other risk factors in the regression of cervical lesions in women from the Bogota Cohort. Methods: 200 HPV positive women with abnormal cytology were included for regression analysis. The time of lesion regression was modeled using methods for interval censored survival time data. Median duration of total follow-up was 9 years. Results: 80 (40%) women were diagnosed with Atypical Squamous Cells of Undetermined Significance (ASCUS) or Atypical Glandular Cells of Undetermined Significance (AGUS) while 120 (60%) were diagnosed with Low Grade Squamous Intra-epithelial Lesions (LSIL). Globally, 40% of the lesions were still present at first year of follow up, while 1.5% was still present at 5 year check-up. The multivariate model showed similar regression rates for lesions in women with ASCUS/AGUS and women with LSIL (HR= 0.82, 95% CI 0.59-1.12). Women infected with HR HPV types and those with mixed infections had lower regression rates for lesions than did women infected with LR types (HR=0.526, 95% CI 0.33-0.84, for HR types and HR=0.378, 95% CI 0.20-0.69, for mixed infections). Furthermore, women over 30 years had a higher lesion regression rate than did women under 30 years (HR1.53, 95% CI 1.03-2.27). The study showed that the median time for lesion regression was 9 months while the median time for HPV clearance was 12 months. Conclusions: In the studied population, the type of infection and the age of the women are critical factors for the regression of cervical lesions.

  9. Reporting instructions significantly impact false positive rates when reading chest radiographs

    Energy Technology Data Exchange (ETDEWEB)

    Robinson, John W.; Brennan, Patrick C.; Mello-Thoms, Claudia; Lewis, Sarah J. [The University of Sydney, Medical Image Optimisation and Perception Group, Discipline of Medical Radiation Sciences, Faculty of Health Sciences, Lidcombe, NSW (Australia)

    2016-10-15

    To determine the impact of specific reporting tasks on the performance of radiologists when reading chest radiographs. Ten experienced radiologists read a set of 40 postero-anterior (PA) chest radiographs: 21 nodule free and 19 with a proven solitary nodule. There were two reporting conditions: an unframed task (UFT) to report any abnormality and a framed task (FT) reporting only lung nodule/s. Jackknife free-response operating characteristic (JAFROC) figure of merit (FOM), specificity, location sensitivity and number of true positive (TP), false positive (FP), true negative (TN) and false negative (FN) decisions were used for analysis. JAFROC FOM for tasks showed a significant reduction in performance for framed tasks (P = 0.006) and an associated decrease in specificity (P = 0.011) but no alteration to the location sensitivity score. There was a significant increase in number of FP decisions made during framed versus unframed tasks for nodule-containing (P = 0.005) and nodule-free (P = 0.011) chest radiographs. No significant differences in TP were recorded. Radiologists report more FP decisions when given specific reporting instructions to search for nodules on chest radiographs. The relevance of clinical history supplied to radiologists is called into question and may induce a negative effect. (orig.)

  10. Reporting instructions significantly impact false positive rates when reading chest radiographs

    International Nuclear Information System (INIS)

    Robinson, John W.; Brennan, Patrick C.; Mello-Thoms, Claudia; Lewis, Sarah J.

    2016-01-01

    To determine the impact of specific reporting tasks on the performance of radiologists when reading chest radiographs. Ten experienced radiologists read a set of 40 postero-anterior (PA) chest radiographs: 21 nodule free and 19 with a proven solitary nodule. There were two reporting conditions: an unframed task (UFT) to report any abnormality and a framed task (FT) reporting only lung nodule/s. Jackknife free-response operating characteristic (JAFROC) figure of merit (FOM), specificity, location sensitivity and number of true positive (TP), false positive (FP), true negative (TN) and false negative (FN) decisions were used for analysis. JAFROC FOM for tasks showed a significant reduction in performance for framed tasks (P = 0.006) and an associated decrease in specificity (P = 0.011) but no alteration to the location sensitivity score. There was a significant increase in number of FP decisions made during framed versus unframed tasks for nodule-containing (P = 0.005) and nodule-free (P = 0.011) chest radiographs. No significant differences in TP were recorded. Radiologists report more FP decisions when given specific reporting instructions to search for nodules on chest radiographs. The relevance of clinical history supplied to radiologists is called into question and may induce a negative effect. (orig.)

  11. Impact of aortic prosthesis-patient mismatch on left ventricular mass regression.

    Science.gov (United States)

    Alassal, Mohamed A; Ibrahim, Bedir M; Elsadeck, Nabil

    2014-06-01

    Prostheses used for aortic valve replacement may be small in relation to body size, causing prosthesis-patient mismatch and delaying left ventricular mass regression. This study examined the effect of prosthesis-patient mismatch on regression of left ventricular mass after aortic valve replacement. We prospectively studied 96 patients undergoing aortic valve replacement between 2007 and 2012. Mean and peak gradients and indexed effective orifice area were measured by transthoracic echocardiography at 3 and 6 months postoperatively. Patient-prosthesis mismatch was defined as indexed effective orifice area ≤0.85 cm(2)·m(-2). Moderate prosthesis-patient mismatch was present in 25% of patients. There were no significant differences in demographic and operative data between patients with and without prosthesis-patient mismatch. Left ventricular dimensions, posterior wall thickness, transvalvular gradients, and left ventricular mass decreased significantly after aortic valve replacement in both groups. The interventricular septal diameter and left ventricular mass index regression, and left ventricular ejection fraction were better in patients without prosthesis-patient mismatch. There was a significant positive correlation between the postoperative indexed effective orifice area of each valve prosthesis and the rate of left ventricular mass regression. Prosthesis-patient mismatch leads to higher transprosthetic gradients and impaired left ventricular mass regression. A small-sized valve prosthesis does not necessarily result in prosthesis-patient mismatch, and may be perfectly adequate in patient with small body size. © The Author(s) 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

  12. Financial Aid and First-Year Collegiate GPA: A Regression Discontinuity Approach

    Science.gov (United States)

    Curs, Bradley R.; Harper, Casandra E.

    2012-01-01

    Using a regression discontinuity design, we investigate whether a merit-based financial aid program has a causal effect on the first-year grade point average of first-time out-of-state freshmen at the University of Oregon. Our results indicate that merit-based financial aid has a positive and significant effect on first-year collegiate grade point…

  13. Evaluation of significance of positive familial history in prevalence of hypertension in Isfahan

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    Tavassoli A

    1997-09-01

    Full Text Available Hypertension is one of the most important modifiable risk factors of vascular heart disease. Control of hypertension in different age groups has a significant effect upon the control and prevention of vascular heart disease. A familial pattern is observed in the distribution of blood pressure in different societies. Family history of hypertension has a profound effect on the future risk of developing hypertension. The blood pressure of approximately 8150 inhabitants of Isfahan aged above 18 years was measured during 1993-94. Blood pressure measurements were performed according to the standards set by WHO i.e., on two separate occasions, in the sitting position, and from both arms. A questionnaire was completed consisting of 26 questions, including questions regarding history of hypertension in first and second-degree relatives. Cases with a blood pressure of 140/90 mmHg or more, were referred to the Cardiovascular Research Center of Isfahan for further evaluation. Mean systolic and diastolic blood pressure was higher in cases with a positive family history of hypertension. In this study, 37.4% of the men with hypertension and 45.4% of hypertensive women had positive history of hypertension in first-degree relatives. The association between positive family history and hypertension was not significant in men (P=0.62, but it was significant in women (P=0.000. This difference was less pronounced in the older age groups, which could be explained by the illiteracy of most of the older cases and their ignorance of the existence of hypertension in family members. After correcting for the effects of confounding factors, it appears that positive family history has a stronger association with the development of hypertension in women. Moreover, positive family history is a strong prognostic factor in the likelihood of hypertension in the children of affected cases. These findings emphasize the importance of routine blood pressure measurement in children and

  14. Adolescents' Perceptions of Their Fathers' Involvement: Significance to School Attitudes.

    Science.gov (United States)

    Flouri, Eirini; Buchanan, Ann; Bream, Victoria

    2002-01-01

    Based on data from 2,722 British adolescents, this study explores whether perceived father involvement can be associated with school attitudes. Multiple regression analysis showed that both father involvement and mother involvement contributed significantly and independently to positive school attitudes. Furthermore, the association between father…

  15. Malignant progressive tumor cell clone exhibits significant up-regulation of cofilin-2 and 27-kDa modified form of cofilin-1 compared to regressive clone.

    Science.gov (United States)

    Kuramitsu, Yasuhiro; Wang, Yufeng; Okada, Futoshi; Baron, Byron; Tokuda, Kazuhiro; Kitagawa, Takao; Akada, Junko; Nakamura, Kazuyuki

    2013-09-01

    QR-32 is a regressive murine fibrosarcoma cell clone which cannot grow when they are transplanted in mice; QRsP-11 is a progressive malignant tumor cell clone derived from QR-32 which shows strong tumorigenicity. A recent study showed there to be differentially expressed up-regulated and down-regulated proteins in these cells, which were identified by proteomic differential display analyses by using two-dimensional gel electrophoresis and mass spectrometry. Cofilins are small proteins of less than 20 kDa. Their function is the regulation of actin assembly. Cofilin-1 is a small ubiquitous protein, and regulates actin dynamics by means of binding to actin filaments. Cofilin-1 plays roles in cell migration, proliferation and phagocytosis. Cofilin-2 is also a small protein, but it is mainly expressed in skeletal and cardiac muscles. There are many reports showing the positive correlation between the level of cofilin-1 and cancer progression. We have also reported an increased expression of cofilin-1 in pancreatic cancer tissues compared to adjacent paired normal tissues. On the other hand, cofilin-2 was significantly less expressed in pancreatic cancer tissues. Therefore, the present study investigated the comparison of the levels of cofilin-1 and cofilin-2 in regressive QR-32 and progressive QRsP-11cells by western blotting. Cofilin-2 was significantly up-regulated in QRsP-11 compared to QR-32 cells (p<0.001). On the other hand, the difference of the intensities of the bands of cofilin-1 (18 kDa) in QR-32 and QRsP-11 was not significant. However, bands of 27 kDa showed a quite different intensity between QR-32 and QRsP-11, with much higher intensities in QRsP-11 compared to QR-32 (p<0.001). These results suggested that the 27-kDa protein recognized by the antibody against cofilin-1 is a possible biomarker for progressive tumor cells.

  16. Multicollinearity and Regression Analysis

    Science.gov (United States)

    Daoud, Jamal I.

    2017-12-01

    In regression analysis it is obvious to have a correlation between the response and predictor(s), but having correlation among predictors is something undesired. The number of predictors included in the regression model depends on many factors among which, historical data, experience, etc. At the end selection of most important predictors is something objective due to the researcher. Multicollinearity is a phenomena when two or more predictors are correlated, if this happens, the standard error of the coefficients will increase [8]. Increased standard errors means that the coefficients for some or all independent variables may be found to be significantly different from In other words, by overinflating the standard errors, multicollinearity makes some variables statistically insignificant when they should be significant. In this paper we focus on the multicollinearity, reasons and consequences on the reliability of the regression model.

  17. Regression in autistic spectrum disorders.

    Science.gov (United States)

    Stefanatos, Gerry A

    2008-12-01

    A significant proportion of children diagnosed with Autistic Spectrum Disorder experience a developmental regression characterized by a loss of previously-acquired skills. This may involve a loss of speech or social responsitivity, but often entails both. This paper critically reviews the phenomena of regression in autistic spectrum disorders, highlighting the characteristics of regression, age of onset, temporal course, and long-term outcome. Important considerations for diagnosis are discussed and multiple etiological factors currently hypothesized to underlie the phenomenon are reviewed. It is argued that regressive autistic spectrum disorders can be conceptualized on a spectrum with other regressive disorders that may share common pathophysiological features. The implications of this viewpoint are discussed.

  18. A significant positive correlation between endogenous trans-zeatin content and total arsenic in arsenic hyperaccumulator Pteris cretica var. nervosa.

    Science.gov (United States)

    Zhang, Xuemei; Yang, Xiaoyan; Wang, Hongbin; Li, Qinchun; Wang, Haijuan; Li, Yanyan

    2017-04-01

    A pot experiment was conducted to compare the content of endogenous trans-zeatin (Z), plant arsenic (As) uptake and physiological indices in the fronds of As-hyperaccumulator (Pteris cretica var. nervosa) and non-hyperaccumulator (Pteris ensiformis). Furthermore, a stepwise regression method was used to study the relationship among determined indices, and the time-course effect of main indices was also investigated under 100mg/kg As stress with time extension. In the 100-200mg/kg As treatments, plant height showed no significant difference and endogenous Z content significantly increased in P. cretica var. nervosa compared to the control, but a significant decrease of height and endogenous Z was observed in P. ensiformis. The concentrations of As (III) and As (V) increased significantly in the fronds of two plants, but this increase was much higher in P. cretica var. nervosa. Compared to the control, the contents of chlorophyll and soluble protein were significantly increased in P. cretica var. nervosa but decreased in P. ensiformis in the 200mg/kg As treatment, respectively. A significant positive correlation was found between the contents of endogenous Z and total As in P. cretica var. nervosa, but such a correlation was not found in P. ensiformis. Additionally, in the time-course effect experiment, a peak value of each index was appeared in the 43rd day in two plants, except for chlorophyll in P. ensiformis, but this value was significantly higher in P. cretica var. nervosa than that in P. ensiformis. In conclusion, a higher endogenous Z content contributed to As accumulation of P. cretica var. nervosa under As stress. Copyright © 2016 Elsevier Inc. All rights reserved.

  19. Vector regression introduced

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    Mok Tik

    2014-06-01

    Full Text Available This study formulates regression of vector data that will enable statistical analysis of various geodetic phenomena such as, polar motion, ocean currents, typhoon/hurricane tracking, crustal deformations, and precursory earthquake signals. The observed vector variable of an event (dependent vector variable is expressed as a function of a number of hypothesized phenomena realized also as vector variables (independent vector variables and/or scalar variables that are likely to impact the dependent vector variable. The proposed representation has the unique property of solving the coefficients of independent vector variables (explanatory variables also as vectors, hence it supersedes multivariate multiple regression models, in which the unknown coefficients are scalar quantities. For the solution, complex numbers are used to rep- resent vector information, and the method of least squares is deployed to estimate the vector model parameters after transforming the complex vector regression model into a real vector regression model through isomorphism. Various operational statistics for testing the predictive significance of the estimated vector parameter coefficients are also derived. A simple numerical example demonstrates the use of the proposed vector regression analysis in modeling typhoon paths.

  20. Differentiating regressed melanoma from regressed lichenoid keratosis.

    Science.gov (United States)

    Chan, Aegean H; Shulman, Kenneth J; Lee, Bonnie A

    2017-04-01

    Distinguishing regressed lichen planus-like keratosis (LPLK) from regressed melanoma can be difficult on histopathologic examination, potentially resulting in mismanagement of patients. We aimed to identify histopathologic features by which regressed melanoma can be differentiated from regressed LPLK. Twenty actively inflamed LPLK, 12 LPLK with regression and 15 melanomas with regression were compared and evaluated by hematoxylin and eosin staining as well as Melan-A, microphthalmia transcription factor (MiTF) and cytokeratin (AE1/AE3) immunostaining. (1) A total of 40% of regressed melanomas showed complete or near complete loss of melanocytes within the epidermis with Melan-A and MiTF immunostaining, while 8% of regressed LPLK exhibited this finding. (2) Necrotic keratinocytes were seen in the epidermis in 33% regressed melanomas as opposed to all of the regressed LPLK. (3) A dense infiltrate of melanophages in the papillary dermis was seen in 40% of regressed melanomas, a feature not seen in regressed LPLK. In summary, our findings suggest that a complete or near complete loss of melanocytes within the epidermis strongly favors a regressed melanoma over a regressed LPLK. In addition, necrotic epidermal keratinocytes and the presence of a dense band-like distribution of dermal melanophages can be helpful in differentiating these lesions. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  1. Logistic regression for dichotomized counts.

    Science.gov (United States)

    Preisser, John S; Das, Kalyan; Benecha, Habtamu; Stamm, John W

    2016-12-01

    Sometimes there is interest in a dichotomized outcome indicating whether a count variable is positive or zero. Under this scenario, the application of ordinary logistic regression may result in efficiency loss, which is quantifiable under an assumed model for the counts. In such situations, a shared-parameter hurdle model is investigated for more efficient estimation of regression parameters relating to overall effects of covariates on the dichotomous outcome, while handling count data with many zeroes. One model part provides a logistic regression containing marginal log odds ratio effects of primary interest, while an ancillary model part describes the mean count of a Poisson or negative binomial process in terms of nuisance regression parameters. Asymptotic efficiency of the logistic model parameter estimators of the two-part models is evaluated with respect to ordinary logistic regression. Simulations are used to assess the properties of the models with respect to power and Type I error, the latter investigated under both misspecified and correctly specified models. The methods are applied to data from a randomized clinical trial of three toothpaste formulations to prevent incident dental caries in a large population of Scottish schoolchildren. © The Author(s) 2014.

  2. Utility of gastric aspirates for diagnosing tuberculosis in children in a low prevalence area: predictors of positive cultures and significance of non-tuberculous mycobacteria.

    Science.gov (United States)

    Kordy, Faisal; Richardson, Susan E; Stephens, Derek; Lam, Ray; Jamieson, Frances; Kitai, Ian

    2015-01-01

    In countries with low rates of tuberculosis (TB), yields of gastric aspirates (GAs) for Mycobacterium tuberculosis (MTB) culture are low. The significance of non-tuberculous mycobacteria (NTM) isolated from GA is uncertain. We reviewed clinical, microbiologic and radiologic data for children who underwent GA between 1999 and 2011 at Sick Kids, Toronto. Radiologic features of cases were compared with those of age matched controls. 785 GAs were obtained from 285 patients of whom 20 (7%) had positive MTB cultures: in 15 patients the GA was the only positive culture for MTB. Of 15 culture-positive patients who underwent exactly 3 GAs, MTB was isolated from the first lavage in 10 (67%), only from the second in 3 (20%) and only from the third in 2 (13%). On univariate analysis, miliary disease and intrathoracic lymphadenopathy were associated with a positive GA MTB culture. On multiple conditional logistic regression analysis, adenopathy remained significant (OR 10.2 [95% CI 2.0-51.4] p =0.005). Twelve patients had NTM isolated, most commonly M. avium complex: none had evidence of invasive NTM disease during a median duration of 12 months of follow-up. Causal pathogens different from the GA NTM culture were isolated from biopsies or bronchoalveolar lavage in 3. GAs continue to be important for TB diagnosis in children. Three GAs have a yield better than 1. Those with miliary or disseminated TB and intrathoracic lymphadenopathy have highest yields. NTM isolates from GA are likely unimportant and can be clinically misleading.

  3. Linear regression in astronomy. I

    Science.gov (United States)

    Isobe, Takashi; Feigelson, Eric D.; Akritas, Michael G.; Babu, Gutti Jogesh

    1990-01-01

    Five methods for obtaining linear regression fits to bivariate data with unknown or insignificant measurement errors are discussed: ordinary least-squares (OLS) regression of Y on X, OLS regression of X on Y, the bisector of the two OLS lines, orthogonal regression, and 'reduced major-axis' regression. These methods have been used by various researchers in observational astronomy, most importantly in cosmic distance scale applications. Formulas for calculating the slope and intercept coefficients and their uncertainties are given for all the methods, including a new general form of the OLS variance estimates. The accuracy of the formulas was confirmed using numerical simulations. The applicability of the procedures is discussed with respect to their mathematical properties, the nature of the astronomical data under consideration, and the scientific purpose of the regression. It is found that, for problems needing symmetrical treatment of the variables, the OLS bisector performs significantly better than orthogonal or reduced major-axis regression.

  4. Effect of folic acid on appetite in children: ordinal logistic and fuzzy logistic regressions.

    Science.gov (United States)

    Namdari, Mahshid; Abadi, Alireza; Taheri, S Mahmoud; Rezaei, Mansour; Kalantari, Naser; Omidvar, Nasrin

    2014-03-01

    Reduced appetite and low food intake are often a concern in preschool children, since it can lead to malnutrition, a leading cause of impaired growth and mortality in childhood. It is occasionally considered that folic acid has a positive effect on appetite enhancement and consequently growth in children. The aim of this study was to assess the effect of folic acid on the appetite of preschool children 3 to 6 y old. The study sample included 127 children ages 3 to 6 who were randomly selected from 20 preschools in the city of Tehran in 2011. Since appetite was measured by linguistic terms, a fuzzy logistic regression was applied for modeling. The obtained results were compared with a statistical ordinal logistic model. After controlling for the potential confounders, in a statistical ordinal logistic model, serum folate showed a significantly positive effect on appetite. A small but positive effect of folate was detected by fuzzy logistic regression. Based on fuzzy regression, the risk for poor appetite in preschool children was related to the employment status of their mothers. In this study, a positive association was detected between the levels of serum folate and improved appetite. For further investigation, a randomized controlled, double-blind clinical trial could be helpful to address causality. Copyright © 2014 Elsevier Inc. All rights reserved.

  5. Spontaneous regression of primary cutaneous diffuse large B-cell lymphoma, leg type with significant T-cell immune response

    Directory of Open Access Journals (Sweden)

    Paul M. Graham, DO

    2018-05-01

    Full Text Available We report a case of histologically confirmed primary cutaneous diffuse large B-cell lymphoma, leg type (PCDLBCL-LT that subsequently underwent spontaneous regression in the absence of systemic treatment. The case showed an atypical lymphoid infiltrate that was CD20+ and MUM-1+ and CD10–. A subsequent biopsy of the spontaneously regressed lesion showed fibrosis associated with a lymphocytic infiltrate comprising reactive T cells. PCDLBCL-LT is a cutaneous B-cell lymphoma with a poor prognosis, which is usually treated with chemotherapy. We describe a case of clinical and histologic spontaneous regression in a patient with PCDLBCL-LT who had a negative systemic workup but a recurrence over a year after his initial presentation. Key words: B cell, lymphoma, primary cutaneous diffuse large B-cell lymphoma, leg type, regression

  6. Better Autologistic Regression

    Directory of Open Access Journals (Sweden)

    Mark A. Wolters

    2017-11-01

    Full Text Available Autologistic regression is an important probability model for dichotomous random variables observed along with covariate information. It has been used in various fields for analyzing binary data possessing spatial or network structure. The model can be viewed as an extension of the autologistic model (also known as the Ising model, quadratic exponential binary distribution, or Boltzmann machine to include covariates. It can also be viewed as an extension of logistic regression to handle responses that are not independent. Not all authors use exactly the same form of the autologistic regression model. Variations of the model differ in two respects. First, the variable coding—the two numbers used to represent the two possible states of the variables—might differ. Common coding choices are (zero, one and (minus one, plus one. Second, the model might appear in either of two algebraic forms: a standard form, or a recently proposed centered form. Little attention has been paid to the effect of these differences, and the literature shows ambiguity about their importance. It is shown here that changes to either coding or centering in fact produce distinct, non-nested probability models. Theoretical results, numerical studies, and analysis of an ecological data set all show that the differences among the models can be large and practically significant. Understanding the nature of the differences and making appropriate modeling choices can lead to significantly improved autologistic regression analyses. The results strongly suggest that the standard model with plus/minus coding, which we call the symmetric autologistic model, is the most natural choice among the autologistic variants.

  7. Random regression models for detection of gene by environment interaction

    Directory of Open Access Journals (Sweden)

    Meuwissen Theo HE

    2007-02-01

    Full Text Available Abstract Two random regression models, where the effect of a putative QTL was regressed on an environmental gradient, are described. The first model estimates the correlation between intercept and slope of the random regression, while the other model restricts this correlation to 1 or -1, which is expected under a bi-allelic QTL model. The random regression models were compared to a model assuming no gene by environment interactions. The comparison was done with regards to the models ability to detect QTL, to position them accurately and to detect possible QTL by environment interactions. A simulation study based on a granddaughter design was conducted, and QTL were assumed, either by assigning an effect independent of the environment or as a linear function of a simulated environmental gradient. It was concluded that the random regression models were suitable for detection of QTL effects, in the presence and absence of interactions with environmental gradients. Fixing the correlation between intercept and slope of the random regression had a positive effect on power when the QTL effects re-ranked between environments.

  8. Prognostic significance of urokinase plasminogen activator and plasminogen activator inhibitor-1 mRNA expression in lymph node- and hormone receptor-positive breast cancer

    International Nuclear Information System (INIS)

    Leissner, Philippe; Verjat, Thibault; Bachelot, Thomas; Paye, Malick; Krause, Alexander; Puisieux, Alain; Mougin, Bruno

    2006-01-01

    One of the most thoroughly studied systems in relation to its prognostic relevance in patients with breast cancer, is the plasminogen activation system that comprises of, among others, the urokinase Plasminogen Activator (uPA) and its main inhibitor, the Plasminogen Activator Inhibitor-1 (PAI-1). In this study, we investigated the prognostic value of uPA and PAI-1 at the mRNA level in lymph node- and hormone receptor-positive breast cancer. The study included a retrospective series of 87 patients with hormone-receptor positive and axillary lymph node-positive breast cancer. All patients received radiotherapy, adjuvant anthracycline-based chemotherapy and five years of tamoxifen treatment. The median patient age was 54 and the median follow-up time was 79 months. Distant relapse occurred in 30 patients and 22 patients died from breast cancer during follow-up. We investigated the prognostic value of uPA and PAI-1 at the mRNA level as measured by real-time quantitative RT-PCR. uPA and PAI-1 gene expression was not found to be correlated with any of the established clinical and pathological factors. Metastasis-free Survival (MFS) and Breast Cancer specific Survival (BCS) were significantly shorter in patients expressing high levels of PAI-1 mRNA (p < 0.0001; p < 0.0001; respectively). In Cox multivariate analysis, the level of PAI-1 mRNA appeared to be the strongest prognostic factor for MFS (Hazard Ratio (HR) = 10.12; p = 0.0002) and for BCS (HR = 13.17; p = 0.0003). Furthermore, uPA gene expression was not significantly associated neither with MFS (p = 0.41) nor with BCS (p = 0.19). In a Cox-multivariate regression analysis, uPA expression did not demonstrate significant independent prognostic value. These findings indicate that high PAI-1 mRNA expression represents a strong and independent unfavorable prognostic factor for the development of metastases and for breast cancer specific survival in a population of hormone receptor- and lymph node-positive breast cancer

  9. Prognostic significance of urokinase plasminogen activator and plasminogen activator inhibitor-1 mRNA expression in lymph node- and hormone receptor-positive breast cancer

    Directory of Open Access Journals (Sweden)

    Krause Alexander

    2006-08-01

    Full Text Available Abstract Background One of the most thoroughly studied systems in relation to its prognostic relevance in patients with breast cancer, is the plasminogen activation system that comprises of, among others, the urokinase Plasminogen Activator (uPA and its main inhibitor, the Plasminogen Activator Inhibitor-1 (PAI-1. In this study, we investigated the prognostic value of uPA and PAI-1 at the mRNA level in lymph node- and hormone receptor-positive breast cancer. Methods The study included a retrospective series of 87 patients with hormone-receptor positive and axillary lymph node-positive breast cancer. All patients received radiotherapy, adjuvant anthracycline-based chemotherapy and five years of tamoxifen treatment. The median patient age was 54 and the median follow-up time was 79 months. Distant relapse occurred in 30 patients and 22 patients died from breast cancer during follow-up. We investigated the prognostic value of uPA and PAI-1 at the mRNA level as measured by real-time quantitative RT-PCR. Results uPA and PAI-1 gene expression was not found to be correlated with any of the established clinical and pathological factors. Metastasis-free Survival (MFS and Breast Cancer specific Survival (BCS were significantly shorter in patients expressing high levels of PAI-1 mRNA (p PAI-1 mRNA appeared to be the strongest prognostic factor for MFS (Hazard Ratio (HR = 10.12; p = 0.0002 and for BCS (HR = 13.17; p = 0.0003. Furthermore, uPA gene expression was not significantly associated neither with MFS (p = 0.41 nor with BCS (p = 0.19. In a Cox-multivariate regression analysis, uPA expression did not demonstrate significant independent prognostic value. Conclusion These findings indicate that high PAI-1 mRNA expression represents a strong and independent unfavorable prognostic factor for the development of metastases and for breast cancer specific survival in a population of hormone receptor- and lymph node-positive breast cancer patients.

  10. Glycophospholipid Formulation with NADH and CoQ10 Significantly Reduces Intractable Fatigue in Western Blot-Positive ‘Chronic Lyme Disease’ Patients: Preliminary Report

    Directory of Open Access Journals (Sweden)

    Garth L. Nicolson

    2012-03-01

    Full Text Available Background: An open label 8-week preliminary study was conducted in a small number of patients to determine if a combination oral supplement containing a mixture of phosphoglycolipids, coenzyme Q10 and microencapsulated NADH and other nutrients could affect fatigue levels in long-term, Western blot-positive, multi-symptom ‘chronic Lyme disease’ patients (also called ‘post-treatment Lyme disease’ or ‘post Lyme syndrome’ with intractable fatigue. Methods: The subjects in this study were 6 males (mean age = 45.1 ± 12.4 years and 10 females (mean age = 54.6 ± 7.4 years with ‘chronic Lyme disease’ (determined by multiple symptoms and positive Western blot analysis that had been symptomatic with chronic fatigue for an average of 12.7 ± 6.6 years. They had been seen by multiple physicians (13.3 ± 7.6 and had used many other remedies, supplements and drugs (14.4 ± 7.4 without fatigue relief. Fatigue was monitored at 0, 7, 30 and 60 days using a validated instrument, the Piper Fatigue Scale.Results: Patients in this preliminary study responded to the combination test supplement, showing a 26% reduction in overall fatigue by the end of the 8-week trial (p< 0.0003. Analysis of subcategories of fatigue indicated that there were significant improvements in the ability to complete tasks and activities as well as significant improvements in mood and cognitive abilities. Regression analysis of the data indicated that reductions in fatigue were consistent and occurred with a high degree of confidence (R2= 0.998. Functional Foods in Health and Disease 2012, 2(3:35-47 Conclusions: The combination supplement was a safe and effective method to significantly reduce intractable fatigue in long-term patients with Western blot-positive ‘chronic Lyme disease.’

  11. A novel hybrid method of beta-turn identification in protein using binary logistic regression and neural network.

    Science.gov (United States)

    Asghari, Mehdi Poursheikhali; Hayatshahi, Sayyed Hamed Sadat; Abdolmaleki, Parviz

    2012-01-01

    From both the structural and functional points of view, β-turns play important biological roles in proteins. In the present study, a novel two-stage hybrid procedure has been developed to identify β-turns in proteins. Binary logistic regression was initially used for the first time to select significant sequence parameters in identification of β-turns due to a re-substitution test procedure. Sequence parameters were consisted of 80 amino acid positional occurrences and 20 amino acid percentages in sequence. Among these parameters, the most significant ones which were selected by binary logistic regression model, were percentages of Gly, Ser and the occurrence of Asn in position i+2, respectively, in sequence. These significant parameters have the highest effect on the constitution of a β-turn sequence. A neural network model was then constructed and fed by the parameters selected by binary logistic regression to build a hybrid predictor. The networks have been trained and tested on a non-homologous dataset of 565 protein chains. With applying a nine fold cross-validation test on the dataset, the network reached an overall accuracy (Qtotal) of 74, which is comparable with results of the other β-turn prediction methods. In conclusion, this study proves that the parameter selection ability of binary logistic regression together with the prediction capability of neural networks lead to the development of more precise models for identifying β-turns in proteins.

  12. Retro-regression--another important multivariate regression improvement.

    Science.gov (United States)

    Randić, M

    2001-01-01

    We review the serious problem associated with instabilities of the coefficients of regression equations, referred to as the MRA (multivariate regression analysis) "nightmare of the first kind". This is manifested when in a stepwise regression a descriptor is included or excluded from a regression. The consequence is an unpredictable change of the coefficients of the descriptors that remain in the regression equation. We follow with consideration of an even more serious problem, referred to as the MRA "nightmare of the second kind", arising when optimal descriptors are selected from a large pool of descriptors. This process typically causes at different steps of the stepwise regression a replacement of several previously used descriptors by new ones. We describe a procedure that resolves these difficulties. The approach is illustrated on boiling points of nonanes which are considered (1) by using an ordered connectivity basis; (2) by using an ordering resulting from application of greedy algorithm; and (3) by using an ordering derived from an exhaustive search for optimal descriptors. A novel variant of multiple regression analysis, called retro-regression (RR), is outlined showing how it resolves the ambiguities associated with both "nightmares" of the first and the second kind of MRA.

  13. Modified Regression Correlation Coefficient for Poisson Regression Model

    Science.gov (United States)

    Kaengthong, Nattacha; Domthong, Uthumporn

    2017-09-01

    This study gives attention to indicators in predictive power of the Generalized Linear Model (GLM) which are widely used; however, often having some restrictions. We are interested in regression correlation coefficient for a Poisson regression model. This is a measure of predictive power, and defined by the relationship between the dependent variable (Y) and the expected value of the dependent variable given the independent variables [E(Y|X)] for the Poisson regression model. The dependent variable is distributed as Poisson. The purpose of this research was modifying regression correlation coefficient for Poisson regression model. We also compare the proposed modified regression correlation coefficient with the traditional regression correlation coefficient in the case of two or more independent variables, and having multicollinearity in independent variables. The result shows that the proposed regression correlation coefficient is better than the traditional regression correlation coefficient based on Bias and the Root Mean Square Error (RMSE).

  14. A PANEL REGRESSION ANALYSIS OF HUMAN CAPITAL RELEVANCE IN SELECTED SCANDINAVIAN AND SE EUROPEAN COUNTRIES

    Directory of Open Access Journals (Sweden)

    Filip Kokotovic

    2016-06-01

    Full Text Available The study of human capital relevance to economic growth is becoming increasingly important taking into account its relevance in many of the Sustainable Development Goals proposed by the UN. This paper conducted a panel regression analysis of selected SE European countries and Scandinavian countries using the Granger causality test and pooled panel regression. In order to test the relevance of human capital on economic growth, several human capital proxy variables were identified. Aside from the human capital proxy variables, other explanatory variables were selected using stepwise regression while the dependant variable was GDP. This paper concludes that there are significant structural differences in the economies of the two observed panels. Of the human capital proxy variables observed, for the panel of SE European countries only life expectancy was statistically significant and it had a negative impact on economic growth, while in the panel of Scandinavian countries total public expenditure on education had a statistically significant positive effect on economic growth. Based upon these results and existing studies, this paper concludes that human capital has a far more significant impact on economic growth in more developed economies.

  15. The epidemiology of smear positive pulmonary tuberculosis at ...

    African Journals Online (AJOL)

    Currently, data regarding the magnitude of TB and associated factors have been released at different health facilities as part of service auditing. However ... Logistic regression model was used to analyze the association between TB positivity and potential associated variables; p < 0.05 was considered to be significant.

  16. Using the Coefficient of Determination "R"[superscript 2] to Test the Significance of Multiple Linear Regression

    Science.gov (United States)

    Quinino, Roberto C.; Reis, Edna A.; Bessegato, Lupercio F.

    2013-01-01

    This article proposes the use of the coefficient of determination as a statistic for hypothesis testing in multiple linear regression based on distributions acquired by beta sampling. (Contains 3 figures.)

  17. Minimotif Miner 3.0: database expansion and significantly improved reduction of false-positive predictions from consensus sequences.

    Science.gov (United States)

    Mi, Tian; Merlin, Jerlin Camilus; Deverasetty, Sandeep; Gryk, Michael R; Bill, Travis J; Brooks, Andrew W; Lee, Logan Y; Rathnayake, Viraj; Ross, Christian A; Sargeant, David P; Strong, Christy L; Watts, Paula; Rajasekaran, Sanguthevar; Schiller, Martin R

    2012-01-01

    Minimotif Miner (MnM available at http://minimotifminer.org or http://mnm.engr.uconn.edu) is an online database for identifying new minimotifs in protein queries. Minimotifs are short contiguous peptide sequences that have a known function in at least one protein. Here we report the third release of the MnM database which has now grown 60-fold to approximately 300,000 minimotifs. Since short minimotifs are by their nature not very complex we also summarize a new set of false-positive filters and linear regression scoring that vastly enhance minimotif prediction accuracy on a test data set. This online database can be used to predict new functions in proteins and causes of disease.

  18. Significant difference in p53 and p21 protein immunoreactivity in HPV 16 positive and HPV negative breast carcinomas

    International Nuclear Information System (INIS)

    Hennig, E.M.; Norwegian Radium Hospital, Oslo; Kvinnsland, S.; Holm, R.; Nesland, J.M.

    1999-01-01

    Human papillomavirus (HPV) 16 has previously been found in 19/41 breast carcinomas (46%) in women with a history of HPV 16 positive CIN III lesions. There was no significant difference in distribution of histological subtypes, mean or median tumour diameter or number of regional lymph node metastases in the HPV positive and HPV negative breast carcinoma groups. P53, p21 and c-erbB-2 proteins were analyzed by immunohistochemistry in the HPV 16 positive and HPV negative breast carcinomas. There was a significant difference in p53 and p21 protein immunoreactivity between HPV 16 positive and HPV negative breast carcinomas (p=0.0091 and p=0.0040), with a significant less detectable p53 and p21 protein immunoreactivity in the HPV 16 positive cases. There was also a significant difference in the coexpression of p53/p21 between the HPV 16 positive and HPV 16 negative breast carcinomas (p=0.002). No significant difference in immunostaining for c-erbB-2 protein in the two groups was found (p=0.15), or for the coexpression of p53/c-erbB-2 (p=0.19). The significantly lower expression of p53 and p21 proteins in HPV 16 positive than in HPV 16 negative breast carcinomas supports the hypothesis of inactivation and degradation of wild-type p53 proteins by HPV 16 E6 and that p53 mutation is not necessary for transformation in the HPV 16 positive cases. (orig.)

  19. Prognostic Significance of Progesterone Receptor–Positive Tumor Cells Within Immunohistochemically Defined Luminal A Breast Cancer

    Science.gov (United States)

    Prat, Aleix; Cheang, Maggie Chon U.; Martín, Miguel; Parker, Joel S.; Carrasco, Eva; Caballero, Rosalía; Tyldesley, Scott; Gelmon, Karen; Bernard, Philip S.; Nielsen, Torsten O.; Perou, Charles M.

    2013-01-01

    Purpose Current immunohistochemical (IHC)-based definitions of luminal A and B breast cancers are imperfect when compared with multigene expression-based assays. In this study, we sought to improve the IHC subtyping by examining the pathologic and gene expression characteristics of genomically defined luminal A and B subtypes. Patients and Methods Gene expression and pathologic features were collected from primary tumors across five independent cohorts: British Columbia Cancer Agency (BCCA) tamoxifen-treated only, Grupo Español de Investigación en Cáncer de Mama 9906 trial, BCCA no systemic treatment cohort, PAM50 microarray training data set, and a combined publicly available microarray data set. Optimal cutoffs of percentage of progesterone receptor (PR) –positive tumor cells to predict survival were derived and independently tested. Multivariable Cox models were used to test the prognostic significance. Results Clinicopathologic comparisons among luminal A and B subtypes consistently identified higher rates of PR positivity, human epidermal growth factor receptor 2 (HER2) negativity, and histologic grade 1 in luminal A tumors. Quantitative PR gene and protein expression were also found to be significantly higher in luminal A tumors. An empiric cutoff of more than 20% of PR-positive tumor cells was statistically chosen and proved significant for predicting survival differences within IHC-defined luminal A tumors independently of endocrine therapy administration. Finally, no additional prognostic value within hormonal receptor (HR) –positive/HER2-negative disease was observed with the use of the IHC4 score when intrinsic IHC-based subtypes were used that included the more than 20% PR-positive tumor cells and vice versa. Conclusion Semiquantitative IHC expression of PR adds prognostic value within the current IHC-based luminal A definition by improving the identification of good outcome breast cancers. The new proposed IHC-based definition of luminal A

  20. Ridge Regression Signal Processing

    Science.gov (United States)

    Kuhl, Mark R.

    1990-01-01

    The introduction of the Global Positioning System (GPS) into the National Airspace System (NAS) necessitates the development of Receiver Autonomous Integrity Monitoring (RAIM) techniques. In order to guarantee a certain level of integrity, a thorough understanding of modern estimation techniques applied to navigational problems is required. The extended Kalman filter (EKF) is derived and analyzed under poor geometry conditions. It was found that the performance of the EKF is difficult to predict, since the EKF is designed for a Gaussian environment. A novel approach is implemented which incorporates ridge regression to explain the behavior of an EKF in the presence of dynamics under poor geometry conditions. The basic principles of ridge regression theory are presented, followed by the derivation of a linearized recursive ridge estimator. Computer simulations are performed to confirm the underlying theory and to provide a comparative analysis of the EKF and the recursive ridge estimator.

  1. Dual Regression

    OpenAIRE

    Spady, Richard; Stouli, Sami

    2012-01-01

    We propose dual regression as an alternative to the quantile regression process for the global estimation of conditional distribution functions under minimal assumptions. Dual regression provides all the interpretational power of the quantile regression process while avoiding the need for repairing the intersecting conditional quantile surfaces that quantile regression often produces in practice. Our approach introduces a mathematical programming characterization of conditional distribution f...

  2. Diagnostic Algorithm to Reflect Regressive Changes of Human Papilloma Virus in Tissue Biopsies

    Science.gov (United States)

    Lhee, Min Jin; Cha, Youn Jin; Bae, Jong Man; Kim, Young Tae

    2014-01-01

    Purpose Landmark indicators have not yet to be developed to detect the regression of cervical intraepithelial neoplasia (CIN). We propose that quantitative viral load and indicative histological criteria can be used to differentiate between atypical squamous cells of undetermined significance (ASCUS) and a CIN of grade 1. Materials and Methods We collected 115 tissue biopsies from women who tested positive for the human papilloma virus (HPV). Nine morphological parameters including nuclear size, perinuclear halo, hyperchromasia, typical koilocyte (TK), abortive koilocyte (AK), bi-/multi-nucleation, keratohyaline granules, inflammation, and dyskeratosis were examined for each case. Correlation analyses, cumulative logistic regression, and binary logistic regression were used to determine optimal cut-off values of HPV copy numbers. The parameters TK, perinuclear halo, multi-nucleation, and nuclear size were significantly correlated quantitatively to HPV copy number. Results An HPV loading number of 58.9 and AK number of 20 were optimal to discriminate between negative and subtle findings in biopsies. An HPV loading number of 271.49 and AK of 20 were optimal for discriminating between equivocal changes and obvious koilocytosis. Conclusion We propose that a squamous epithelial lesion with AK of >20 and quantitative HPV copy number between 58.9-271.49 represents a new spectrum of subtle pathological findings, characterized by AK in ASCUS. This can be described as a distinct entity and called "regressing koilocytosis". PMID:24532500

  3. The effect of emphasis and position on word identification by adult cochlear implant listeners

    DEFF Research Database (Denmark)

    Morris, David Jackson; Magnusson, Lennart; Jönsson, Radoslava

    2013-01-01

    sentence framework. It was found that emphasised stimuli were not identified more accurately than unemphasised stimuli. A regression analysis revealed a significant main effect for words drawn from the initial position in a sentence, however there was no interaction between original word position...

  4. Resting venous plasma adrenalin in 70-year-old men correlated positively to survival in a population study: the significance of the physical working capacity

    DEFF Research Database (Denmark)

    Christensen, Niels Juel; Schultz-Larsen, K

    1994-01-01

    in a comprehensive medical examination. INTERVENTIONS. Plasma NA and A were measured in blood samples collected after the subjects had rested in the supine position for 15 min. The subjects have now been followed for 7 years. MAIN OUTCOME MEASURES. Seven years later, 115 men and 63 women had died. RESULTS. Cox...... of physical working capacity was included in the Cox regression analysis, both plasma NA and plasma A became insignificant, whereas a strong positive correlation appeared between physical working capacity and survival (P

  5. Multiple regression analysis of anthropometric measurements influencing the cephalic index of male Japanese university students.

    Science.gov (United States)

    Hossain, Md Golam; Saw, Aik; Alam, Rashidul; Ohtsuki, Fumio; Kamarul, Tunku

    2013-09-01

    Cephalic index (CI), the ratio of head breadth to head length, is widely used to categorise human populations. The aim of this study was to access the impact of anthropometric measurements on the CI of male Japanese university students. This study included 1,215 male university students from Tokyo and Kyoto, selected using convenient sampling. Multiple regression analysis was used to determine the effect of anthropometric measurements on CI. The variance inflation factor (VIF) showed no evidence of a multicollinearity problem among independent variables. The coefficients of the regression line demonstrated a significant positive relationship between CI and minimum frontal breadth (p regression analysis showed a greater likelihood for minimum frontal breadth (p regression analysis revealed bizygomatic breadth, head circumference, minimum frontal breadth, head height and morphological facial height to be the best predictor craniofacial measurements with respect to CI. The results suggest that most of the variables considered in this study appear to influence the CI of adult male Japanese students.

  6. Tightness of M-estimators for multiple linear regression in time series

    DEFF Research Database (Denmark)

    Johansen, Søren; Nielsen, Bent

    We show tightness of a general M-estimator for multiple linear regression in time series. The positive criterion function for the M-estimator is assumed lower semi-continuous and sufficiently large for large argument: Particular cases are the Huber-skip and quantile regression. Tightness requires...

  7. Logistic regression analysis of conventional ultrasonography, strain elastosonography, and contrast-enhanced ultrasound characteristics for the differentiation of benign and malignant thyroid nodules.

    Science.gov (United States)

    Pang, Tiantian; Huang, Leidan; Deng, Yingyuan; Wang, Tianfu; Chen, Siping; Gong, Xuehao; Liu, Weixiang

    2017-01-01

    The aim of the study is to screen the significant sonographic features by logistic regression analysis and fit a model to diagnose thyroid nodules. A total of 525 pathological thyroid nodules were retrospectively analyzed. All the nodules underwent conventional ultrasonography (US), strain elastosonography (SE), and contrast -enhanced ultrasound (CEUS). Those nodules' 12 suspicious sonographic features were used to assess thyroid nodules. The significant features of diagnosing thyroid nodules were picked out by logistic regression analysis. All variables that were statistically related to diagnosis of thyroid nodules, at a level of p regression analysis model. The significant features in the logistic regression model of diagnosing thyroid nodules were calcification, suspected cervical lymph node metastasis, hypoenhancement pattern, margin, shape, vascularity, posterior acoustic, echogenicity, and elastography score. According to the results of logistic regression analysis, the formula that could predict whether or not thyroid nodules are malignant was established. The area under the receiver operating curve (ROC) was 0.930 and the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 83.77%, 89.56%, 87.05%, 86.04%, and 87.79% respectively.

  8. Are learning strategies linked to academic performance among adolescents in two States in India? A tobit regression analysis.

    Science.gov (United States)

    Areepattamannil, Shaljan

    2014-01-01

    The results of the fourth cycle of the Program for International Student Assessment (PISA) revealed that an unacceptably large number of adolescent students in two states in India-Himachal Pradesh and Tamil Nadu-have failed to acquire basic skills in reading, mathematics, and science (Walker, 2011). Drawing on data from the PISA 2009 database and employing multivariate left-censored to bit regression as a data analytic strategy, the present study, therefore, examined whether or not the learning strategies-memorization, elaboration, and control strategies-of adolescent students in Himachal Pradesh (N = 1,616; Mean age = 15.81 years) and Tamil Nadu (N = 3,210; Mean age = 15.64 years) were linked to their performance on the PISA 2009 reading, mathematics, and science assessments. Tobit regression analyses, after accounting for student demographic characteristics, revealed that the self-reported use of control strategies was significantly positively associated with reading, mathematical, and scientific literacy of adolescents in Himachal Pradesh and Tamil Nadu. While the self-reported use of elaboration strategies was not significantly associated with reading literacy among adolescents in Himachal Pradesh and Tamil Nadu, it was significantly positively associated with mathematical literacy among adolescents in Himachal Pradesh and Tamil Nadu. Moreover, the self-reported use of elaboration strategies was significantly and positively linked to scientific literacy among adolescents in Himachal Pradesh alone. The self-reported use of memorization strategies was significantly negatively associated with reading, mathematical, and scientific literacy in Tamil Nadu, while it was significantly negatively associated with mathematical and scientific literacy alone in Himachal Pradesh. Implications of these findings are discussed.

  9. [Multiple linear regression analysis of X-ray measurement and WOMAC scores of knee osteoarthritis].

    Science.gov (United States)

    Ma, Yu-Feng; Wang, Qing-Fu; Chen, Zhao-Jun; Du, Chun-Lin; Li, Jun-Hai; Huang, Hu; Shi, Zong-Ting; Yin, Yue-Shan; Zhang, Lei; A-Di, Li-Jiang; Dong, Shi-Yu; Wu, Ji

    2012-05-01

    To perform Multiple Linear Regression analysis of X-ray measurement and WOMAC scores of knee osteoarthritis, and to analyze their relationship with clinical and biomechanical concepts. From March 2011 to July 2011, 140 patients (250 knees) were reviewed, including 132 knees in the left and 118 knees in the right; ranging in age from 40 to 71 years, with an average of 54.68 years. The MB-RULER measurement software was applied to measure femoral angle, tibial angle, femorotibial angle, joint gap angle from antero-posterir and lateral position of X-rays. The WOMAC scores were also collected. Then multiple regression equations was applied for the linear regression analysis of correlation between the X-ray measurement and WOMAC scores. There was statistical significance in the regression equation of AP X-rays value and WOMAC scores (Pregression equation of lateral X-ray value and WOMAC scores (P>0.05). 1) X-ray measurement of knee joint can reflect the WOMAC scores to a certain extent. 2) It is necessary to measure the X-ray mechanical axis of knee, which is important for diagnosis and treatment of osteoarthritis. 3) The correlation between tibial angle,joint gap angle on antero-posterior X-ray and WOMAC scores is significant, which can be used to assess the functional recovery of patients before and after treatment.

  10. Regression: A Bibliography.

    Science.gov (United States)

    Pedrini, D. T.; Pedrini, Bonnie C.

    Regression, another mechanism studied by Sigmund Freud, has had much research, e.g., hypnotic regression, frustration regression, schizophrenic regression, and infra-human-animal regression (often directly related to fixation). Many investigators worked with hypnotic age regression, which has a long history, going back to Russian reflexologists.…

  11. Independent predictive factors for significant liver histological changes in patients with HBeAg-positive high-viral-load chronic HBV infection and a normal alanine aminotransferase level

    Directory of Open Access Journals (Sweden)

    LI Qiang

    2016-07-01

    Full Text Available Objective To investigate the independent predictive factors for significant liver histological changes (SLHCs in patients with HBeAg-positive high-viral-load chronic hepatitis B virus (HBV infection and a normal alanine aminotransferase (ALT level. MethodsA retrospective analysis was performed on the clinical data of 116 previously untreated patients with HBeAg-positive high-viral-load (HBV DNA≥105 copies/ml chronic HBV infection and a normal ALT level (<50 U/L who were hospitalized in Shanghai Public Health Clinical Center Affiliated to Fudan University from June 2013 to August 2015. The definition of SLHCs was inflammation ≥G2 and/or fibrosis≥S2. The t-test or Mann-Whitney U rank sum test was used for comparison of continuous data between groups, and the chi-square test was used for comparison of categorical data between groups. Univariate and multivariate regression analyses were used to determine independent predictive factors for SLHCs. ResultsOf all the 116 patients, 47(40.5% had SLHCs. The multivariate analysis showed that age (OR=2.828, P<0.05, ALT (OR=1.011, P<0.05, and gamma-glutamyl transpeptidase (GGT (OR=1.089, P<0.05 were independent predictors for SLHCs in patients with HBeAg-positive high-viral-load chronic HBV infection and a normal ALT level. The patients aged ≤30 years had a significantly lower incidence rate of SLHCs than those aged>30 years (21.6% vs 49.4%, χ2=6.42, P=0.015, the patients with ALT ≤30 U/L had a significantly lower incidence rate of SLHCs than those with 30 U/L<ALT≤50 U/L (17.6% vs 50.0%, χ2=19.86, P<0.001, and the patients with GGT≤40 U/L had a significantly lower incidence rate of SLHCs than those with GGT>40 U/L (28.8% vs 66.7%, χ2=28.63, P<0.001. ConclusionIn patients with HBeAg-positive high-viral-load chronic HBV infection and a normal ALT level, those with an age of>30 years, ALT>30 U/L, and GGT>40 U/L tend to develop SLHCs and need liver biopsy.

  12. Advanced statistics: linear regression, part I: simple linear regression.

    Science.gov (United States)

    Marill, Keith A

    2004-01-01

    Simple linear regression is a mathematical technique used to model the relationship between a single independent predictor variable and a single dependent outcome variable. In this, the first of a two-part series exploring concepts in linear regression analysis, the four fundamental assumptions and the mechanics of simple linear regression are reviewed. The most common technique used to derive the regression line, the method of least squares, is described. The reader will be acquainted with other important concepts in simple linear regression, including: variable transformations, dummy variables, relationship to inference testing, and leverage. Simplified clinical examples with small datasets and graphic models are used to illustrate the points. This will provide a foundation for the second article in this series: a discussion of multiple linear regression, in which there are multiple predictor variables.

  13. Correlation of results obtained by in-vivo optical spectroscopy with measured blood oxygen saturation using a positive linear regression fit

    Science.gov (United States)

    McCormick, Patrick W.; Lewis, Gary D.; Dujovny, Manuel; Ausman, James I.; Stewart, Mick; Widman, Ronald A.

    1992-05-01

    Near infrared light generated by specialized instrumentation was passed through artificially oxygenated human blood during simultaneous sampling by a co-oximeter. Characteristic absorption spectra were analyzed to calculate the ratio of oxygenated to reduced hemoglobin. A positive linear regression fit between diffuse transmission oximetry and measured blood oxygenation over the range 23% to 99% (r2 equals .98, p signal was observed in the patient over time. The procedure was able to be performed clinically without difficulty; rSO2 values recorded continuously demonstrate the usefulness of the technique. Using the same instrumentation, arterial input and cerebral response functions, generated by IV tracer bolus, were deconvoluted to measure mean cerebral transit time. Date collected over time provided a sensitive index of changes in cerebral blood flow as a result of therapeutic maneuvers.

  14. Is past life regression therapy ethical?

    Science.gov (United States)

    Andrade, Gabriel

    2017-01-01

    Past life regression therapy is used by some physicians in cases with some mental diseases. Anxiety disorders, mood disorders, and gender dysphoria have all been treated using life regression therapy by some doctors on the assumption that they reflect problems in past lives. Although it is not supported by psychiatric associations, few medical associations have actually condemned it as unethical. In this article, I argue that past life regression therapy is unethical for two basic reasons. First, it is not evidence-based. Past life regression is based on the reincarnation hypothesis, but this hypothesis is not supported by evidence, and in fact, it faces some insurmountable conceptual problems. If patients are not fully informed about these problems, they cannot provide an informed consent, and hence, the principle of autonomy is violated. Second, past life regression therapy has the great risk of implanting false memories in patients, and thus, causing significant harm. This is a violation of the principle of non-malfeasance, which is surely the most important principle in medical ethics.

  15. Ordinary least square regression, orthogonal regression, geometric mean regression and their applications in aerosol science

    International Nuclear Information System (INIS)

    Leng Ling; Zhang Tianyi; Kleinman, Lawrence; Zhu Wei

    2007-01-01

    Regression analysis, especially the ordinary least squares method which assumes that errors are confined to the dependent variable, has seen a fair share of its applications in aerosol science. The ordinary least squares approach, however, could be problematic due to the fact that atmospheric data often does not lend itself to calling one variable independent and the other dependent. Errors often exist for both measurements. In this work, we examine two regression approaches available to accommodate this situation. They are orthogonal regression and geometric mean regression. Comparisons are made theoretically as well as numerically through an aerosol study examining whether the ratio of organic aerosol to CO would change with age

  16. Prenatal diagnosis of Caudal Regression Syndrome : a case report

    Directory of Open Access Journals (Sweden)

    Celikaslan Nurgul

    2001-12-01

    Full Text Available Abstract Background Caudal regression is a rare syndrome which has a spectrum of congenital malformations ranging from simple anal atresia to absence of sacral, lumbar and possibly lower thoracic vertebrae, to the most severe form which is known as sirenomelia. Maternal diabetes, genetic predisposition and vascular hypoperfusion have been suggested as possible causative factors. Case presentation We report a case of caudal regression syndrome diagnosed in utero at 22 weeks' of gestation. Prenatal ultrasound examination revealed a sudden interruption of the spine and "frog-like" position of lower limbs. Termination of pregnancy and autopsy findings confirmed the diagnosis. Conclusion Prenatal ultrasonographic diagnosis of caudal regression syndrome is possible at 22 weeks' of gestation by ultrasound examination.

  17. Detection of epistatic effects with logic regression and a classical linear regression model.

    Science.gov (United States)

    Malina, Magdalena; Ickstadt, Katja; Schwender, Holger; Posch, Martin; Bogdan, Małgorzata

    2014-02-01

    To locate multiple interacting quantitative trait loci (QTL) influencing a trait of interest within experimental populations, usually methods as the Cockerham's model are applied. Within this framework, interactions are understood as the part of the joined effect of several genes which cannot be explained as the sum of their additive effects. However, if a change in the phenotype (as disease) is caused by Boolean combinations of genotypes of several QTLs, this Cockerham's approach is often not capable to identify them properly. To detect such interactions more efficiently, we propose a logic regression framework. Even though with the logic regression approach a larger number of models has to be considered (requiring more stringent multiple testing correction) the efficient representation of higher order logic interactions in logic regression models leads to a significant increase of power to detect such interactions as compared to a Cockerham's approach. The increase in power is demonstrated analytically for a simple two-way interaction model and illustrated in more complex settings with simulation study and real data analysis.

  18. MEMPREDIKSI FINANCIAL DISTRESS DENGAN BINARY LOGIT REGRESSION PERUSAHAAN TELEKOMUNIKASI

    Directory of Open Access Journals (Sweden)

    Tiara Widya Antikasari

    2017-04-01

    Full Text Available In this globalization era, sub–sector telecommunication industry has rapid development as time goes by with the number of customers’ growth. However, its growth is not balanced with operational revenue development. Therefore, it is important to analyze the financial distress in telecommunication companies in order to avoid bankruptcy. This research aimed to investigate the effect of financial ratios to predict probability of financial distress. Financial ratios indicator used profitability ratio, liquidity ratio, activity ratio, and leverage ratio. The population in this research was telecommunication companies listed in the Indonesia Stock Exchange periods 2009-2016. Based on purposive sampling method, the criteria of financial distress in this study was measured by using net operation negative two years, while statistic analysis used was logistic regression with a significance level of 10%. The result was that liquidity ratio (current ratio and activity ratio (total asset turnover ratio had a negative significant value, and profitability ratio(return on asset and leverage ratio (debt to total asset had positive significant value to predict financial distress.

  19. Reduced Rank Regression

    DEFF Research Database (Denmark)

    Johansen, Søren

    2008-01-01

    The reduced rank regression model is a multivariate regression model with a coefficient matrix with reduced rank. The reduced rank regression algorithm is an estimation procedure, which estimates the reduced rank regression model. It is related to canonical correlations and involves calculating...

  20. Infant feeding methods among HIV-positive mothers in Yei County ...

    African Journals Online (AJOL)

    2016-08-03

    Aug 3, 2016 ... a mother is HIV-positive, exclusive replacement feeding. (e.g. with infant formula) is usually recommended provided it is affordable and safe. This is often not ... logistic regression model was used and odds ratio obtained for the factors that have significant association with choice of exclusive breast feeding, ...

  1. Quantile Regression Methods

    DEFF Research Database (Denmark)

    Fitzenberger, Bernd; Wilke, Ralf Andreas

    2015-01-01

    if the mean regression model does not. We provide a short informal introduction into the principle of quantile regression which includes an illustrative application from empirical labor market research. This is followed by briefly sketching the underlying statistical model for linear quantile regression based......Quantile regression is emerging as a popular statistical approach, which complements the estimation of conditional mean models. While the latter only focuses on one aspect of the conditional distribution of the dependent variable, the mean, quantile regression provides more detailed insights...... by modeling conditional quantiles. Quantile regression can therefore detect whether the partial effect of a regressor on the conditional quantiles is the same for all quantiles or differs across quantiles. Quantile regression can provide evidence for a statistical relationship between two variables even...

  2. Geographically Weighted Logistic Regression Applied to Credit Scoring Models

    Directory of Open Access Journals (Sweden)

    Pedro Henrique Melo Albuquerque

    Full Text Available Abstract This study used real data from a Brazilian financial institution on transactions involving Consumer Direct Credit (CDC, granted to clients residing in the Distrito Federal (DF, to construct credit scoring models via Logistic Regression and Geographically Weighted Logistic Regression (GWLR techniques. The aims were: to verify whether the factors that influence credit risk differ according to the borrower’s geographic location; to compare the set of models estimated via GWLR with the global model estimated via Logistic Regression, in terms of predictive power and financial losses for the institution; and to verify the viability of using the GWLR technique to develop credit scoring models. The metrics used to compare the models developed via the two techniques were the AICc informational criterion, the accuracy of the models, the percentage of false positives, the sum of the value of false positive debt, and the expected monetary value of portfolio default compared with the monetary value of defaults observed. The models estimated for each region in the DF were distinct in their variables and coefficients (parameters, with it being concluded that credit risk was influenced differently in each region in the study. The Logistic Regression and GWLR methodologies presented very close results, in terms of predictive power and financial losses for the institution, and the study demonstrated viability in using the GWLR technique to develop credit scoring models for the target population in the study.

  3. Multiple linear regression analysis

    Science.gov (United States)

    Edwards, T. R.

    1980-01-01

    Program rapidly selects best-suited set of coefficients. User supplies only vectors of independent and dependent data and specifies confidence level required. Program uses stepwise statistical procedure for relating minimal set of variables to set of observations; final regression contains only most statistically significant coefficients. Program is written in FORTRAN IV for batch execution and has been implemented on NOVA 1200.

  4. The Health Significance of Positive Emotions in Adulthood and Later Life

    OpenAIRE

    Ong, Anthony D.; Mroczek, Daniel K.; Riffin, Catherine

    2011-01-01

    A growing body of literature supports a link between positive emotions and health in older adults. In this article, we review evidence of the effects of positive emotions on downstream biological processes and meaningful clinical endpoints, such as adult morbidity and mortality. We then present relevant predictions from lifespan theories that suggest changes in cognition and motivation may play an important role in explaining how positive emotions are well maintained in old age, despite perva...

  5. A Monte Carlo simulation study comparing linear regression, beta regression, variable-dispersion beta regression and fractional logit regression at recovering average difference measures in a two sample design.

    Science.gov (United States)

    Meaney, Christopher; Moineddin, Rahim

    2014-01-24

    In biomedical research, response variables are often encountered which have bounded support on the open unit interval--(0,1). Traditionally, researchers have attempted to estimate covariate effects on these types of response data using linear regression. Alternative modelling strategies may include: beta regression, variable-dispersion beta regression, and fractional logit regression models. This study employs a Monte Carlo simulation design to compare the statistical properties of the linear regression model to that of the more novel beta regression, variable-dispersion beta regression, and fractional logit regression models. In the Monte Carlo experiment we assume a simple two sample design. We assume observations are realizations of independent draws from their respective probability models. The randomly simulated draws from the various probability models are chosen to emulate average proportion/percentage/rate differences of pre-specified magnitudes. Following simulation of the experimental data we estimate average proportion/percentage/rate differences. We compare the estimators in terms of bias, variance, type-1 error and power. Estimates of Monte Carlo error associated with these quantities are provided. If response data are beta distributed with constant dispersion parameters across the two samples, then all models are unbiased and have reasonable type-1 error rates and power profiles. If the response data in the two samples have different dispersion parameters, then the simple beta regression model is biased. When the sample size is small (N0 = N1 = 25) linear regression has superior type-1 error rates compared to the other models. Small sample type-1 error rates can be improved in beta regression models using bias correction/reduction methods. In the power experiments, variable-dispersion beta regression and fractional logit regression models have slightly elevated power compared to linear regression models. Similar results were observed if the

  6. Extra-nodal extension is a significant prognostic factor in lymph node positive breast cancer.

    Directory of Open Access Journals (Sweden)

    Sura Aziz

    Full Text Available Presence of lymph node (LN metastasis is a strong prognostic factor in breast cancer, whereas the importance of extra-nodal extension and other nodal tumor features have not yet been fully recognized. Here, we examined microscopic features of lymph node metastases and their prognostic value in a population-based cohort of node positive breast cancer (n = 218, as part of the prospective Norwegian Breast Cancer Screening Program NBCSP (1996-2009. Sections were reviewed for the largest metastatic tumor diameter (TD-MET, nodal afferent and efferent vascular invasion (AVI and EVI, extra-nodal extension (ENE, number of ENE foci, as well as circumferential (CD-ENE and perpendicular (PD-ENE diameter of extra-nodal growth. Number of positive lymph nodes, EVI, and PD-ENE were significantly increased with larger primary tumor (PT diameter. Univariate survival analysis showed that several features of nodal metastases were associated with disease-free (DFS or breast cancer specific survival (BCSS. Multivariate analysis demonstrated an independent prognostic value of PD-ENE (with 3 mm as cut-off value in predicting DFS and BCSS, along with number of positive nodes and histologic grade of the primary tumor (for DFS: P = 0.01, P = 0.02, P = 0.01, respectively; for BCSS: P = 0.02, P = 0.008, P = 0.02, respectively. To conclude, the extent of ENE by its perpendicular diameter was independently prognostic and should be considered in line with nodal tumor burden in treatment decisions of node positive breast cancer.

  7. Do clinical and translational science graduate students understand linear regression? Development and early validation of the REGRESS quiz.

    Science.gov (United States)

    Enders, Felicity

    2013-12-01

    Although regression is widely used for reading and publishing in the medical literature, no instruments were previously available to assess students' understanding. The goal of this study was to design and assess such an instrument for graduate students in Clinical and Translational Science and Public Health. A 27-item REsearch on Global Regression Expectations in StatisticS (REGRESS) quiz was developed through an iterative process. Consenting students taking a course on linear regression in a Clinical and Translational Science program completed the quiz pre- and postcourse. Student results were compared to practicing statisticians with a master's or doctoral degree in statistics or a closely related field. Fifty-two students responded precourse, 59 postcourse , and 22 practicing statisticians completed the quiz. The mean (SD) score was 9.3 (4.3) for students precourse and 19.0 (3.5) postcourse (P REGRESS quiz was internally reliable (Cronbach's alpha 0.89). The initial validation is quite promising with statistically significant and meaningful differences across time and study populations. Further work is needed to validate the quiz across multiple institutions. © 2013 Wiley Periodicals, Inc.

  8. 21-Gene Recurrence Score and Locoregional Recurrence in Node-Positive/ER-Positive Breast Cancer Treated With Chemo-Endocrine Therapy.

    Science.gov (United States)

    Mamounas, Eleftherios P; Liu, Qing; Paik, Soonmyung; Baehner, Frederick L; Tang, Gong; Jeong, Jong-Hyeon; Kim, S Rim; Butler, Steven M; Jamshidian, Farid; Cherbavaz, Diana B; Sing, Amy P; Shak, Steven; Julian, Thomas B; Lembersky, Barry C; Wickerham, D Lawrence; Costantino, Joseph P; Wolmark, Norman

    2017-01-01

    The 21-gene recurrence score (RS) predicts risk of locoregional recurrence (LRR) in node-negative, estrogen receptor (ER)-positive breast cancer. We evaluated the association between RS and LRR in node-positive, ER-positive patients treated with adjuvant chemotherapy plus tamoxifen in National Surgical Adjuvant Breast and Bowel Project B-28. B-28 compared doxorubicin/cyclophosphamide (AC X 4) with AC X 4 followed by paclitaxel X 4. Tamoxifen was given to patients age 50 years or older and those younger than age 50 years with ER-positive and/or progesterone receptor-positive tumors. Lumpectomy patients received breast radiotherapy. Mastectomy patients received no radiotherapy. The present study includes 1065 ER-positive, tamoxifen-treated patients with RS assessment. Cumulative incidence functions and subdistribution hazard regression models were used for LRR to account for competing risks including distant recurrence, second primary cancers, and death from other causes. Median follow-up was 11.2 years. All statistical tests were one-sided. There were 80 LRRs (7.5%) as first events (68% local/32% regional). RS was low: 36.2%; intermediate: 34.2%; and high: 29.6%. RS was a statistically significant predictor of LRR in univariate analyses (10-year cumulative incidence of LRR = 3.3%, 7.2%, and 12.2% for low, intermediate, and high RS, respectively, P < .001). In multivariable regression analysis, RS remained an independent predictor of LRR (hazard ratio [HR] = 2.59, 95% confidence interval [CI] = 1.28 to 5.26, for a 50-point difference, P = .008) along with pathologic nodal status (HR = 1.91, 95% CI = 1.20 to 3.03, for four or more vs one to three positive nodes, P = .006) and tumor size (HR = 1.28, 95% CI = 1.05 to 1.55, for a 1 cm difference, P = .02). RS statistically significantly predicts risk of LRR in node-positive, ER-positive breast cancer patients after adjuvant chemotherapy plus tamoxifen. These findings can help in the selection of

  9. Regression Phalanxes

    OpenAIRE

    Zhang, Hongyang; Welch, William J.; Zamar, Ruben H.

    2017-01-01

    Tomal et al. (2015) introduced the notion of "phalanxes" in the context of rare-class detection in two-class classification problems. A phalanx is a subset of features that work well for classification tasks. In this paper, we propose a different class of phalanxes for application in regression settings. We define a "Regression Phalanx" - a subset of features that work well together for prediction. We propose a novel algorithm which automatically chooses Regression Phalanxes from high-dimensi...

  10. Analysis of tag-position bias in MPSS technology

    Directory of Open Access Journals (Sweden)

    Rattray Magnus

    2006-04-01

    Full Text Available Abstract Background Massively Parallel Signature Sequencing (MPSS technology was recently developed as a high-throughput technology for measuring the concentration of mRNA transcripts in a sample. It has previously been observed that the position of the signature tag in a transcript (distance from 3' end can affect the measurement, but this effect has not been studied in detail. Results We quantify the effect of tag-position bias in Classic and Signature MPSS technology using published data from Arabidopsis, rice and human. We investigate the relationship between measured concentration and tag-position using nonlinear regression methods. The observed relationship is shown to be broadly consistent across different data sets. We find that there exist different and significant biases in both Classic and Signature MPSS data. For Classic MPSS data, genes with tag-position in the middle-range have highest measured abundance on average while genes with tag-position in the high-range, far from the 3' end, show a significant decrease. For Signature MPSS data, high-range tag-position genes tend to have a flatter relationship between tag-position and measured abundance. Thus, our results confirm that the Signature MPSS method fixes a substantial problem with the Classic MPSS method. For both Classic and Signature MPSS data there is a positive correlation between measured abundance and tag-position for low-range tag-position genes. Compared with the effects of mRNA length and number of exons, tag-position bias seems to be more significant in Arabadopsis. The tag-position bias is reflected both in the measured abundance of genes with a significant tag count and in the proportion of unexpressed genes identified. Conclusion Tag-position bias should be taken into consideration when measuring mRNA transcript abundance using MPSS technology, both in Classic and Signature MPSS methods.

  11. Detection of D2-40 monoclonal antibody-labeled lymphatic vessel invasion in esophageal squamous cell carcinoma and its clinicopathologic significance

    International Nuclear Information System (INIS)

    Bai, Bing; Ma, Wei; Wang, Kai; Ha, Sita; Wang, Jian-Bo; Tan, Bing-Xu; Wang, Na-Na; Yang, Sheng-Si; Jia, Yi-Bin; Cheng, Yu-Feng

    2013-01-01

    This study aims to investigate the clinicopathologic significance of lymphatic vessel invasion (LVI) labeled by D2-40 monoclonal antibody in esophageal squamous cell carcinoma (ESCC). Immunohistochemical assay was used to detect the expression of D2-40 and LVI in 107 ESCC patients. Then, the correlation between the clinicopathologic feature and the overall survival time of the patients was analyzed. The lymph node metastasis rates were 70% and 21% in the LVI-positive and LVI-negative groups, respectively. The nodal metastasis rate was higher in the LVI-positive group than in the LVI-negative group. Multivariate regression analysis showed that LVI was related to nodal metastasis (P<0.001). The median survival time of the patients was 26 and 43 months in the LVI-positive and LVI-negative groups, respectively. Although univariate regression analysis showed significant difference between the two groups (P=0.014), multivariate regression analysis revealed that LVI was not an independent prognostic factor for overall survival in the ESCC patients (P=0.062). Lymphatic node metastasis (P=0.031), clinical stage (P=0.019), and residual tumor (P=0.026) were the independent prognostic factors. LVI labeled by D2-40 monoclonal antibody is a risk factor predictive of lymph node metastasis in ESCC patients

  12. Quality of life in breast cancer patients--a quantile regression analysis.

    Science.gov (United States)

    Pourhoseingholi, Mohamad Amin; Safaee, Azadeh; Moghimi-Dehkordi, Bijan; Zeighami, Bahram; Faghihzadeh, Soghrat; Tabatabaee, Hamid Reza; Pourhoseingholi, Asma

    2008-01-01

    Quality of life study has an important role in health care especially in chronic diseases, in clinical judgment and in medical resources supplying. Statistical tools like linear regression are widely used to assess the predictors of quality of life. But when the response is not normal the results are misleading. The aim of this study is to determine the predictors of quality of life in breast cancer patients, using quantile regression model and compare to linear regression. A cross-sectional study conducted on 119 breast cancer patients that admitted and treated in chemotherapy ward of Namazi hospital in Shiraz. We used QLQ-C30 questionnaire to assessment quality of life in these patients. A quantile regression was employed to assess the assocciated factors and the results were compared to linear regression. All analysis carried out using SAS. The mean score for the global health status for breast cancer patients was 64.92+/-11.42. Linear regression showed that only grade of tumor, occupational status, menopausal status, financial difficulties and dyspnea were statistically significant. In spite of linear regression, financial difficulties were not significant in quantile regression analysis and dyspnea was only significant for first quartile. Also emotion functioning and duration of disease statistically predicted the QOL score in the third quartile. The results have demonstrated that using quantile regression leads to better interpretation and richer inference about predictors of the breast cancer patient quality of life.

  13. Multitask Quantile Regression under the Transnormal Model.

    Science.gov (United States)

    Fan, Jianqing; Xue, Lingzhou; Zou, Hui

    2016-01-01

    We consider estimating multi-task quantile regression under the transnormal model, with focus on high-dimensional setting. We derive a surprisingly simple closed-form solution through rank-based covariance regularization. In particular, we propose the rank-based ℓ 1 penalization with positive definite constraints for estimating sparse covariance matrices, and the rank-based banded Cholesky decomposition regularization for estimating banded precision matrices. By taking advantage of alternating direction method of multipliers, nearest correlation matrix projection is introduced that inherits sampling properties of the unprojected one. Our work combines strengths of quantile regression and rank-based covariance regularization to simultaneously deal with nonlinearity and nonnormality for high-dimensional regression. Furthermore, the proposed method strikes a good balance between robustness and efficiency, achieves the "oracle"-like convergence rate, and provides the provable prediction interval under the high-dimensional setting. The finite-sample performance of the proposed method is also examined. The performance of our proposed rank-based method is demonstrated in a real application to analyze the protein mass spectroscopy data.

  14. Resting-state functional magnetic resonance imaging: the impact of regression analysis.

    Science.gov (United States)

    Yeh, Chia-Jung; Tseng, Yu-Sheng; Lin, Yi-Ru; Tsai, Shang-Yueh; Huang, Teng-Yi

    2015-01-01

    To investigate the impact of regression methods on resting-state functional magnetic resonance imaging (rsfMRI). During rsfMRI preprocessing, regression analysis is considered effective for reducing the interference of physiological noise on the signal time course. However, it is unclear whether the regression method benefits rsfMRI analysis. Twenty volunteers (10 men and 10 women; aged 23.4 ± 1.5 years) participated in the experiments. We used node analysis and functional connectivity mapping to assess the brain default mode network by using five combinations of regression methods. The results show that regressing the global mean plays a major role in the preprocessing steps. When a global regression method is applied, the values of functional connectivity are significantly lower (P ≤ .01) than those calculated without a global regression. This step increases inter-subject variation and produces anticorrelated brain areas. rsfMRI data processed using regression should be interpreted carefully. The significance of the anticorrelated brain areas produced by global signal removal is unclear. Copyright © 2014 by the American Society of Neuroimaging.

  15. Real estate value prediction using multivariate regression models

    Science.gov (United States)

    Manjula, R.; Jain, Shubham; Srivastava, Sharad; Rajiv Kher, Pranav

    2017-11-01

    The real estate market is one of the most competitive in terms of pricing and the same tends to vary significantly based on a lot of factors, hence it becomes one of the prime fields to apply the concepts of machine learning to optimize and predict the prices with high accuracy. Therefore in this paper, we present various important features to use while predicting housing prices with good accuracy. We have described regression models, using various features to have lower Residual Sum of Squares error. While using features in a regression model some feature engineering is required for better prediction. Often a set of features (multiple regressions) or polynomial regression (applying a various set of powers in the features) is used for making better model fit. For these models are expected to be susceptible towards over fitting ridge regression is used to reduce it. This paper thus directs to the best application of regression models in addition to other techniques to optimize the result.

  16. Physiologic noise regression, motion regression, and TOAST dynamic field correction in complex-valued fMRI time series.

    Science.gov (United States)

    Hahn, Andrew D; Rowe, Daniel B

    2012-02-01

    As more evidence is presented suggesting that the phase, as well as the magnitude, of functional MRI (fMRI) time series may contain important information and that there are theoretical drawbacks to modeling functional response in the magnitude alone, removing noise in the phase is becoming more important. Previous studies have shown that retrospective correction of noise from physiologic sources can remove significant phase variance and that dynamic main magnetic field correction and regression of estimated motion parameters also remove significant phase fluctuations. In this work, we investigate the performance of physiologic noise regression in a framework along with correction for dynamic main field fluctuations and motion regression. Our findings suggest that including physiologic regressors provides some benefit in terms of reduction in phase noise power, but it is small compared to the benefit of dynamic field corrections and use of estimated motion parameters as nuisance regressors. Additionally, we show that the use of all three techniques reduces phase variance substantially, removes undesirable spatial phase correlations and improves detection of the functional response in magnitude and phase. Copyright © 2011 Elsevier Inc. All rights reserved.

  17. Advanced statistics: linear regression, part II: multiple linear regression.

    Science.gov (United States)

    Marill, Keith A

    2004-01-01

    The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.

  18. Boosted beta regression.

    Directory of Open Access Journals (Sweden)

    Matthias Schmid

    Full Text Available Regression analysis with a bounded outcome is a common problem in applied statistics. Typical examples include regression models for percentage outcomes and the analysis of ratings that are measured on a bounded scale. In this paper, we consider beta regression, which is a generalization of logit models to situations where the response is continuous on the interval (0,1. Consequently, beta regression is a convenient tool for analyzing percentage responses. The classical approach to fit a beta regression model is to use maximum likelihood estimation with subsequent AIC-based variable selection. As an alternative to this established - yet unstable - approach, we propose a new estimation technique called boosted beta regression. With boosted beta regression estimation and variable selection can be carried out simultaneously in a highly efficient way. Additionally, both the mean and the variance of a percentage response can be modeled using flexible nonlinear covariate effects. As a consequence, the new method accounts for common problems such as overdispersion and non-binomial variance structures.

  19. Regression to Causality : Regression-style presentation influences causal attribution

    DEFF Research Database (Denmark)

    Bordacconi, Mats Joe; Larsen, Martin Vinæs

    2014-01-01

    of equivalent results presented as either regression models or as a test of two sample means. Our experiment shows that the subjects who were presented with results as estimates from a regression model were more inclined to interpret these results causally. Our experiment implies that scholars using regression...... models – one of the primary vehicles for analyzing statistical results in political science – encourage causal interpretation. Specifically, we demonstrate that presenting observational results in a regression model, rather than as a simple comparison of means, makes causal interpretation of the results...... more likely. Our experiment drew on a sample of 235 university students from three different social science degree programs (political science, sociology and economics), all of whom had received substantial training in statistics. The subjects were asked to compare and evaluate the validity...

  20. Impacts on CO2 Emission Allowance Prices in China: A Quantile Regression Analysis of the Shanghai Emission Trading Scheme

    Directory of Open Access Journals (Sweden)

    Jie Zhang

    2016-11-01

    Full Text Available A pilot regional carbon emission trading scheme (ETS has been implemented in China for more than two years. An investigation into the impacts of different factors on carbon dioxide (CO2 emission allowance prices provides guidance for price-making in 2017 when the nation-wide ETS of China will be established. This paper adopts a quantile regression approach to estimate the impacts of different factors in Shanghai emission trading scheme (SH-ETS, namely, economic growth, energy prices and temperature. The empirical analysis shows that: (i the economic growth in Shanghai leads to a drop in the carbon allowance prices; (ii the oil price has a slightly positive effect on the allowance prices regardless of the ordinary least squares (OLS or quantile regression method; (iii a long-run negative relationship exists between the coal price and the Shanghai emission allowances (SHEA prices, but a positive interaction under different quantiles, especially the 25%–50% quantiles; (iv temperature has a significantly positive effect at the 20%–30% quantiles and a conspicuous negative impact at the right tail of the allowances prices.

  1. Regression formulae for predicting hematologic and liver functions ...

    African Journals Online (AJOL)

    African Journal of Biomedical Research ... On the other hand platelet and white blood cell (WBC) counts in these workers correlated positively with years of service [r = 0.342 (P <0.001) and r = 0.130 (P<0.0001) ... The regression equation defining this relationship is: ALP concentration = 33.68 – 0.075 x years of service.

  2. The Role of Positive Psychological Capital and the Family Function in Prediction of Happiness in high school students

    Directory of Open Access Journals (Sweden)

    F rashidi kochi

    2016-11-01

    Full Text Available The aim of this study was to determine the role of positive psychological capital and family functioning in predicting happiness among adolescence. Correlational research method was recruited to analyze the data. The sample comprised of 290 high Scholl students that selected by the convenience sampling method. In this research Snyder’s hope, Nezami and Colleagues self-efficacy, Scheier and Carver's optimism, McMaster's family functioning and Connor and Davidson's Resiliency and Oxford happiness questionnaire used to collect data. Pearson correlation and stepwise regression were used to analyze data. The finding showed that there was a significant positive relationship between family function components and positive psychological capital with happiness. The results of stepwise regression showed that roles, Resiliency, self-efficacy, optimism and emotion expression had significant and important role in predicting happiness. Totally, explained 35% of the variance happiness. In conclusion, these findings indicate the importance roles of family and positive psychological capital in adolescence's happiness.

  3. Logistic regression model for identification of right ventricular dysfunction in patients with acute pulmonary embolism by means of computed tomography

    International Nuclear Information System (INIS)

    Staskiewicz, Grzegorz; Czekajska-Chehab, Elżbieta; Uhlig, Sebastian; Przegalinski, Jerzy; Maciejewski, Ryszard; Drop, Andrzej

    2013-01-01

    Purpose: Diagnosis of right ventricular dysfunction in patients with acute pulmonary embolism (PE) is known to be associated with increased risk of mortality. The aim of the study was to calculate a logistic regression model for reliable identification of right ventricular dysfunction (RVD) in patients diagnosed with computed tomography pulmonary angiography. Material and methods: Ninety-seven consecutive patients with acute pulmonary embolism were divided into groups with and without RVD basing upon echocardiographic measurement of pulmonary artery systolic pressure (PASP). PE severity was graded with the pulmonary obstruction score. CT measurements of heart chambers and mediastinal vessels were performed; position of interventricular septum and presence of contrast reflux into the inferior vena cava were also recorded. The logistic regression model was prepared by means of stepwise logistic regression. Results: Among the used parameters, the final model consisted of pulmonary obstruction score, short axis diameter of right ventricle and diameter of inferior vena cava. The calculated model is characterized by 79% sensitivity and 81% specificity, and its performance was significantly better than single CT-based measurements. Conclusion: Logistic regression model identifies RVD significantly better, than single CT-based measurements

  4. Regression of uveal malignant melanomas following cobalt-60 plaque. Correlates between acoustic spectrum analysis and tumor regression

    International Nuclear Information System (INIS)

    Coleman, D.J.; Lizzi, F.L.; Silverman, R.H.; Ellsworth, R.M.; Haik, B.G.; Abramson, D.H.; Smith, M.E.; Rondeau, M.J.

    1985-01-01

    Parameters derived from computer analysis of digital radio-frequency (rf) ultrasound scan data of untreated uveal malignant melanomas were examined for correlations with tumor regression following cobalt-60 plaque. Parameters included tumor height, normalized power spectrum and acoustic tissue type (ATT). Acoustic tissue type was based upon discriminant analysis of tumor power spectra, with spectra of tumors of known pathology serving as a model. Results showed ATT to be correlated with tumor regression during the first 18 months following treatment. Tumors with ATT associated with spindle cell malignant melanoma showed over twice the percentage reduction in height as those with ATT associated with mixed/epithelioid melanomas. Pre-treatment height was only weakly correlated with regression. Additionally, significant spectral changes were observed following treatment. Ultrasonic spectrum analysis thus provides a noninvasive tool for classification, prediction and monitoring of tumor response to cobalt-60 plaque

  5. Estimation of Stature from Foot Dimensions and Stature among South Indian Medical Students Using Regression Models

    Directory of Open Access Journals (Sweden)

    Rajesh D. R

    2015-01-01

    Full Text Available Background: At times fragments of soft tissues are found disposed off in the open, in ditches at the crime scene and the same are brought to forensic experts for the purpose of identification and such type of cases pose a real challenge. Objectives: This study was aimed at developing a methodology which could help in personal identification by studying the relation between foot dimensions and stature among south subjects using regression models. Material and Methods: Stature and foot length of 100 subjects (age range 18-22 years were measured. Linear regression equations for stature estimation were calculated. Result: The correlation coefficients between stature and foot lengths were found to be positive and statistically significant. Height = 98.159 + 3.746 × FLRT (r = 0.821 and Height = 91.242 + 3.284 × FLRT (r = 0.837 are the regression formulas from foot lengths for males and females respectively. Conclusion: The regression equation derived in the study can be used reliably for estimation of stature in a diverse population group thus would be of immense value in the field of personal identification especially from mutilated bodies or fragmentary remains.

  6. Multiple regression equations modelling of groundwater of Ajmer-Pushkar railway line region, Rajasthan (India).

    Science.gov (United States)

    Mathur, Praveen; Sharma, Sarita; Soni, Bhupendra

    2010-01-01

    In the present work, an attempt is made to formulate multiple regression equations using all possible regressions method for groundwater quality assessment of Ajmer-Pushkar railway line region in pre- and post-monsoon seasons. Correlation studies revealed the existence of linear relationships (r 0.7) for electrical conductivity (EC), total hardness (TH) and total dissolved solids (TDS) with other water quality parameters. The highest correlation was found between EC and TDS (r = 0.973). EC showed highly significant positive correlation with Na, K, Cl, TDS and total solids (TS). TH showed highest correlation with Ca and Mg. TDS showed significant correlation with Na, K, SO4, PO4 and Cl. The study indicated that most of the contamination present was water soluble or ionic in nature. Mg was present as MgCl2; K mainly as KCl and K2SO4, and Na was present as the salts of Cl, SO4 and PO4. On the other hand, F and NO3 showed no significant correlations. The r2 values and F values (at 95% confidence limit, alpha = 0.05) for the modelled equations indicated high degree of linearity among independent and dependent variables. Also the error % between calculated and experimental values was contained within +/- 15% limit.

  7. Building a new predictor for multiple linear regression technique-based corrective maintenance turnaround time.

    Science.gov (United States)

    Cruz, Antonio M; Barr, Cameron; Puñales-Pozo, Elsa

    2008-01-01

    This research's main goals were to build a predictor for a turnaround time (TAT) indicator for estimating its values and use a numerical clustering technique for finding possible causes of undesirable TAT values. The following stages were used: domain understanding, data characterisation and sample reduction and insight characterisation. Building the TAT indicator multiple linear regression predictor and clustering techniques were used for improving corrective maintenance task efficiency in a clinical engineering department (CED). The indicator being studied was turnaround time (TAT). Multiple linear regression was used for building a predictive TAT value model. The variables contributing to such model were clinical engineering department response time (CE(rt), 0.415 positive coefficient), stock service response time (Stock(rt), 0.734 positive coefficient), priority level (0.21 positive coefficient) and service time (0.06 positive coefficient). The regression process showed heavy reliance on Stock(rt), CE(rt) and priority, in that order. Clustering techniques revealed the main causes of high TAT values. This examination has provided a means for analysing current technical service quality and effectiveness. In doing so, it has demonstrated a process for identifying areas and methods of improvement and a model against which to analyse these methods' effectiveness.

  8. Alternative regression models to assess increase in childhood BMI.

    Science.gov (United States)

    Beyerlein, Andreas; Fahrmeir, Ludwig; Mansmann, Ulrich; Toschke, André M

    2008-09-08

    Body mass index (BMI) data usually have skewed distributions, for which common statistical modeling approaches such as simple linear or logistic regression have limitations. Different regression approaches to predict childhood BMI by goodness-of-fit measures and means of interpretation were compared including generalized linear models (GLMs), quantile regression and Generalized Additive Models for Location, Scale and Shape (GAMLSS). We analyzed data of 4967 children participating in the school entry health examination in Bavaria, Germany, from 2001 to 2002. TV watching, meal frequency, breastfeeding, smoking in pregnancy, maternal obesity, parental social class and weight gain in the first 2 years of life were considered as risk factors for obesity. GAMLSS showed a much better fit regarding the estimation of risk factors effects on transformed and untransformed BMI data than common GLMs with respect to the generalized Akaike information criterion. In comparison with GAMLSS, quantile regression allowed for additional interpretation of prespecified distribution quantiles, such as quantiles referring to overweight or obesity. The variables TV watching, maternal BMI and weight gain in the first 2 years were directly, and meal frequency was inversely significantly associated with body composition in any model type examined. In contrast, smoking in pregnancy was not directly, and breastfeeding and parental social class were not inversely significantly associated with body composition in GLM models, but in GAMLSS and partly in quantile regression models. Risk factor specific BMI percentile curves could be estimated from GAMLSS and quantile regression models. GAMLSS and quantile regression seem to be more appropriate than common GLMs for risk factor modeling of BMI data.

  9. Subset selection in regression

    CERN Document Server

    Miller, Alan

    2002-01-01

    Originally published in 1990, the first edition of Subset Selection in Regression filled a significant gap in the literature, and its critical and popular success has continued for more than a decade. Thoroughly revised to reflect progress in theory, methods, and computing power, the second edition promises to continue that tradition. The author has thoroughly updated each chapter, incorporated new material on recent developments, and included more examples and references. New in the Second Edition:A separate chapter on Bayesian methodsComplete revision of the chapter on estimationA major example from the field of near infrared spectroscopyMore emphasis on cross-validationGreater focus on bootstrappingStochastic algorithms for finding good subsets from large numbers of predictors when an exhaustive search is not feasible Software available on the Internet for implementing many of the algorithms presentedMore examplesSubset Selection in Regression, Second Edition remains dedicated to the techniques for fitting...

  10. Regression analysis with categorized regression calibrated exposure: some interesting findings

    Directory of Open Access Journals (Sweden)

    Hjartåker Anette

    2006-07-01

    Full Text Available Abstract Background Regression calibration as a method for handling measurement error is becoming increasingly well-known and used in epidemiologic research. However, the standard version of the method is not appropriate for exposure analyzed on a categorical (e.g. quintile scale, an approach commonly used in epidemiologic studies. A tempting solution could then be to use the predicted continuous exposure obtained through the regression calibration method and treat it as an approximation to the true exposure, that is, include the categorized calibrated exposure in the main regression analysis. Methods We use semi-analytical calculations and simulations to evaluate the performance of the proposed approach compared to the naive approach of not correcting for measurement error, in situations where analyses are performed on quintile scale and when incorporating the original scale into the categorical variables, respectively. We also present analyses of real data, containing measures of folate intake and depression, from the Norwegian Women and Cancer study (NOWAC. Results In cases where extra information is available through replicated measurements and not validation data, regression calibration does not maintain important qualities of the true exposure distribution, thus estimates of variance and percentiles can be severely biased. We show that the outlined approach maintains much, in some cases all, of the misclassification found in the observed exposure. For that reason, regression analysis with the corrected variable included on a categorical scale is still biased. In some cases the corrected estimates are analytically equal to those obtained by the naive approach. Regression calibration is however vastly superior to the naive method when applying the medians of each category in the analysis. Conclusion Regression calibration in its most well-known form is not appropriate for measurement error correction when the exposure is analyzed on a

  11. APPLICATION OF MULTIPLE LOGISTIC REGRESSION, BAYESIAN LOGISTIC AND CLASSIFICATION TREE TO IDENTIFY THE SIGNIFICANT FACTORS INFLUENCING CRASH SEVERITY

    Directory of Open Access Journals (Sweden)

    MILAD TAZIK

    2017-11-01

    Full Text Available Identifying cases in which road crashes result in fatality or injury of drivers may help improve their safety. In this study, datasets of crashes happened in TehranQom freeway, Iran, were examined by three models (multiple logistic regression, Bayesian logistic and classification tree to analyse the contribution of several variables to fatal accidents. For multiple logistic regression and Bayesian logistic models, the odds ratio was calculated for each variable. The model which best suited the identification of accident severity was determined based on AIC and DIC criteria. Based on the results of these two models, rollover crashes (OR = 14.58, %95 CI: 6.8-28.6, not using of seat belt (OR = 5.79, %95 CI: 3.1-9.9, exceeding speed limits (OR = 4.02, %95 CI: 1.8-7.9 and being female (OR = 2.91, %95 CI: 1.1-6.1 were the most important factors in fatalities of drivers. In addition, the results of the classification tree model have verified the findings of the other models.

  12. Refractive regression after laser in situ keratomileusis.

    Science.gov (United States)

    Yan, Mabel K; Chang, John Sm; Chan, Tommy Cy

    2018-04-26

    Uncorrected refractive errors are a leading cause of visual impairment across the world. In today's society, laser in situ keratomileusis (LASIK) has become the most commonly performed surgical procedure to correct refractive errors. However, regression of the initially achieved refractive correction has been a widely observed phenomenon following LASIK since its inception more than two decades ago. Despite technological advances in laser refractive surgery and various proposed management strategies, post-LASIK regression is still frequently observed and has significant implications for the long-term visual performance and quality of life of patients. This review explores the mechanism of refractive regression after both myopic and hyperopic LASIK, predisposing risk factors and its clinical course. In addition, current preventative strategies and therapies are also reviewed. © 2018 Royal Australian and New Zealand College of Ophthalmologists.

  13. [False positive results or what's the probability that a significant P-value indicates a true effect?

    Science.gov (United States)

    Cucherat, Michel; Laporte, Silvy

    2017-09-01

    The use of statistical test is central in the clinical trial. At the statistical level, obtaining a Pinformation about the plausibility of the existence of treatment effect. With "Pfalse positive is very high. This is the case if the power is low, if there is an inflation of the alpha risk or if the result is exploratory or chance discoveries. This possibility is important to take into consideration when interpreting the results of clinical trials in order to avoid pushing ahead significant results in appearance, but which are likely to be actually false positive results. Copyright © 2017 Société française de pharmacologie et de thérapeutique. Published by Elsevier Masson SAS. All rights reserved.

  14. Clinical significance of orthostatic dizziness in the diagnosis of benign paroxysmal positional vertigo and orthostatic intolerance.

    Science.gov (United States)

    Jeon, Eun-Ju; Park, Yong-Soo; Park, Shi-Nae; Park, Kyoung-Ho; Kim, Dong-Hyun; Nam, In-Chul; Chang, Ki-Hong

    2013-01-01

    Orthostatic dizziness (OD) and positional dizziness (PD) are considerably common conditions in dizziness clinic, whereas those two conditions are not clearly separated. We aimed to evaluate the clinical significance of simple OD and OD combined with PD for the diagnosis of benign paroxysmal positional vertigo (BPPV) and orthostatic intolerance (OI). Patients presenting with OD (n=102) were divided into two groups according to their symptoms: group PO, presenting with PD as well as OD; group O, presenting with OD. A thorough medical history, physical examination, and vestibular function tests were performed to identify the etiology of the dizziness. Orthostatic vital sign measurement (OVSM) was used to diagnose OI. The majority of patients were in group PO (87.3%). BPPV was the most common cause of OD for entire patients (36.3%) and group PO (37.1%), while OI was most common etiology for group O (38.5%). Total of 17 (16.7%) OI patients were identified by OVSM test. Orthostatic hypotension (n=10) was most frequently found, followed by orthostatic hypertension (n=5), and orthostatic tachycardia (n=2). Group O showed significantly higher percentage (38.5%) of OI than group PO (13.5%) (P=0.039). It is suggested that orthostatic testing such as OVSM or head-up tilt table test should be performed as an initial work up for the patients with simple OD. Positional tests for BPPV should be considered as an essential diagnostic test for patients with OD, even though their dizziness is not associated with PD. Copyright © 2013 Elsevier Inc. All rights reserved.

  15. Positive Selection or Free to Vary? Assessing the Functional Significance of Sequence Change Using Molecular Dynamics.

    Directory of Open Access Journals (Sweden)

    Jane R Allison

    Full Text Available Evolutionary arms races between pathogens and their hosts may be manifested as selection for rapid evolutionary change of key genes, and are sometimes detectable through sequence-level analyses. In the case of protein-coding genes, such analyses frequently predict that specific codons are under positive selection. However, detecting positive selection can be non-trivial, and false positive predictions are a common concern in such analyses. It is therefore helpful to place such predictions within a structural and functional context. Here, we focus on the p19 protein from tombusviruses. P19 is a homodimer that sequesters siRNAs, thereby preventing the host RNAi machinery from shutting down viral infection. Sequence analysis of the p19 gene is complicated by the fact that it is constrained at the sequence level by overprinting of a viral movement protein gene. Using homology modeling, in silico mutation and molecular dynamics simulations, we assess how non-synonymous changes to two residues involved in forming the dimer interface-one invariant, and one predicted to be under positive selection-impact molecular function. Interestingly, we find that both observed variation and potential variation (where a non-synonymous change to p19 would be synonymous for the overprinted movement protein does not significantly impact protein structure or RNA binding. Consequently, while several methods identify residues at the dimer interface as being under positive selection, MD results suggest they are functionally indistinguishable from a site that is free to vary. Our analyses serve as a caveat to using sequence-level analyses in isolation to detect and assess positive selection, and emphasize the importance of also accounting for how non-synonymous changes impact structure and function.

  16. Alternative regression models to assess increase in childhood BMI

    Directory of Open Access Journals (Sweden)

    Mansmann Ulrich

    2008-09-01

    Full Text Available Abstract Background Body mass index (BMI data usually have skewed distributions, for which common statistical modeling approaches such as simple linear or logistic regression have limitations. Methods Different regression approaches to predict childhood BMI by goodness-of-fit measures and means of interpretation were compared including generalized linear models (GLMs, quantile regression and Generalized Additive Models for Location, Scale and Shape (GAMLSS. We analyzed data of 4967 children participating in the school entry health examination in Bavaria, Germany, from 2001 to 2002. TV watching, meal frequency, breastfeeding, smoking in pregnancy, maternal obesity, parental social class and weight gain in the first 2 years of life were considered as risk factors for obesity. Results GAMLSS showed a much better fit regarding the estimation of risk factors effects on transformed and untransformed BMI data than common GLMs with respect to the generalized Akaike information criterion. In comparison with GAMLSS, quantile regression allowed for additional interpretation of prespecified distribution quantiles, such as quantiles referring to overweight or obesity. The variables TV watching, maternal BMI and weight gain in the first 2 years were directly, and meal frequency was inversely significantly associated with body composition in any model type examined. In contrast, smoking in pregnancy was not directly, and breastfeeding and parental social class were not inversely significantly associated with body composition in GLM models, but in GAMLSS and partly in quantile regression models. Risk factor specific BMI percentile curves could be estimated from GAMLSS and quantile regression models. Conclusion GAMLSS and quantile regression seem to be more appropriate than common GLMs for risk factor modeling of BMI data.

  17. [Milk yield and environmental factors: Multiple regression analysis of the association between milk yield and udder health, fertility data and replacement rate].

    Science.gov (United States)

    Fölsche, C; Staufenbiel, R

    2014-01-01

    The relationship between milk yield and both fertility and general animal health in dairy herds is discussed from opposing viewpoints. The hypothesis (1) that raising the herd milk yield would decrease fertility results, the number of milk cells as an indicator for udder health and the replacement rate as a global indicator for animal health as well as increasing the occurrence of specific diseases as a herd problem was compared to the opposing hypotheses that there is no relationship (2) or that there is a differentiated and changing relationship (3). A total of 743 herd examinations, considered independent, were performed in 489 herds between 1995 and 2010. The milk yield, fertility rate, milk cell count, replacement rate, categorized herd problems and management information were recorded. The relationship between the milk yield and both the fertility data and animal health was evaluated using simple and multiple regression analyses. The period between calving and the first service displayed no significant relationship to the herd milk yield. Simple regression analysis showed that the period between calving and gestation, the calving interval and the insemination number were significantly positively associated with the herd milk yield. This positive correlation was lost in multiple regression analysis. The milk cell count and replacement rate using both the simple and multiple regression analyses displayed a significant negative relationship to the milk yield. The alternative hypothesis (3) was confirmed. A higher milk yield has no negative influence on the milk cell count and the replacement rate in terms of the udder and general health. When parameterizing the fertility, the herd milk yield should be considered. Extending the resting time may increase the milk yield while preventing a decline in the insemination index.

  18. Moderation analysis using a two-level regression model.

    Science.gov (United States)

    Yuan, Ke-Hai; Cheng, Ying; Maxwell, Scott

    2014-10-01

    Moderation analysis is widely used in social and behavioral research. The most commonly used model for moderation analysis is moderated multiple regression (MMR) in which the explanatory variables of the regression model include product terms, and the model is typically estimated by least squares (LS). This paper argues for a two-level regression model in which the regression coefficients of a criterion variable on predictors are further regressed on moderator variables. An algorithm for estimating the parameters of the two-level model by normal-distribution-based maximum likelihood (NML) is developed. Formulas for the standard errors (SEs) of the parameter estimates are provided and studied. Results indicate that, when heteroscedasticity exists, NML with the two-level model gives more efficient and more accurate parameter estimates than the LS analysis of the MMR model. When error variances are homoscedastic, NML with the two-level model leads to essentially the same results as LS with the MMR model. Most importantly, the two-level regression model permits estimating the percentage of variance of each regression coefficient that is due to moderator variables. When applied to data from General Social Surveys 1991, NML with the two-level model identified a significant moderation effect of race on the regression of job prestige on years of education while LS with the MMR model did not. An R package is also developed and documented to facilitate the application of the two-level model.

  19. Functional data analysis of generalized regression quantiles

    KAUST Repository

    Guo, Mengmeng; Zhou, Lan; Huang, Jianhua Z.; Hä rdle, Wolfgang Karl

    2013-01-01

    Generalized regression quantiles, including the conditional quantiles and expectiles as special cases, are useful alternatives to the conditional means for characterizing a conditional distribution, especially when the interest lies in the tails. We develop a functional data analysis approach to jointly estimate a family of generalized regression quantiles. Our approach assumes that the generalized regression quantiles share some common features that can be summarized by a small number of principal component functions. The principal component functions are modeled as splines and are estimated by minimizing a penalized asymmetric loss measure. An iterative least asymmetrically weighted squares algorithm is developed for computation. While separate estimation of individual generalized regression quantiles usually suffers from large variability due to lack of sufficient data, by borrowing strength across data sets, our joint estimation approach significantly improves the estimation efficiency, which is demonstrated in a simulation study. The proposed method is applied to data from 159 weather stations in China to obtain the generalized quantile curves of the volatility of the temperature at these stations. © 2013 Springer Science+Business Media New York.

  20. Functional data analysis of generalized regression quantiles

    KAUST Repository

    Guo, Mengmeng

    2013-11-05

    Generalized regression quantiles, including the conditional quantiles and expectiles as special cases, are useful alternatives to the conditional means for characterizing a conditional distribution, especially when the interest lies in the tails. We develop a functional data analysis approach to jointly estimate a family of generalized regression quantiles. Our approach assumes that the generalized regression quantiles share some common features that can be summarized by a small number of principal component functions. The principal component functions are modeled as splines and are estimated by minimizing a penalized asymmetric loss measure. An iterative least asymmetrically weighted squares algorithm is developed for computation. While separate estimation of individual generalized regression quantiles usually suffers from large variability due to lack of sufficient data, by borrowing strength across data sets, our joint estimation approach significantly improves the estimation efficiency, which is demonstrated in a simulation study. The proposed method is applied to data from 159 weather stations in China to obtain the generalized quantile curves of the volatility of the temperature at these stations. © 2013 Springer Science+Business Media New York.

  1. Allostatic load in parents of children with developmental disorders: Moderating influence of positive affect

    OpenAIRE

    Song, Jieun; Mailick, Marsha R.; Ryff, Carol D.; Coe, Christopher L.; Greenberg, Jan S.; Hong, Jinkuk

    2013-01-01

    This study examines whether parents of children with developmental disorders (DD) are at risk for elevated allostatic load (AL) relative to control parents, and whether positive affect moderates difference in risk. Thirty-eight parents of children with DD and 38 matched comparison parents were analyzed. Regression analyses revealed a significant interaction between parent status and AL level: parents of children with DD had lower AL when they had higher positive affect, whereas no such associ...

  2. The prognostic significance and relationship with body composition of CCR7-positive cells in colorectal cancer.

    Science.gov (United States)

    Malietzis, George; Lee, Gui Han; Bernardo, David; Blakemore, Alexandra I F; Knight, Stella C; Moorghen, Morgan; Al-Hassi, Hafid O; Jenkins, John T

    2015-07-01

    The host local immune response (LIR) to cancer is a determinant of cancer outcome. Regulation of this local response is largely achieved through chemokine synthesis from the tumor microenvironment such as C-Chemokine-Receptor-7 (CCR7). We examined the LIR measured as CCR7 expression, in colorectal cancers (CRC) and explored relationships with body composition (BC) and survival. A study of paraffin-embedded tissue specimens was carried out in 116 patients with non-metastatic CRC. CCR7 expression was determined by immunohistochemistry. Analysis of computer tomography scans was used to calculate BC parameters. Survival analyses and multivariate regression models were used. High CCR7(+) cell density within the tumor stroma and at the margin was significantly associated with increased age, the presence of lymphovascular invasion, higher tumor stage, lymph node metastasis, high Klintrup-Makinen immune score, and myosteatosis. High CCR7(+) cell density in the tumor margin was significantly associated with shorter disease-free (DFS) and overall survival (OS) (P < 0.001). This was also significantly associated with shorter survival in multivariate analysis (HR = 8.87; 95%CI [2.51-31.3]; P < 0.01 for OS and HR = 4.72; 95%CI (1.24-12.9); P = 0.02 for DFS). Our results suggest that a specific immune microenvironment may be associated with altered host's BC and tumor behavior, and that CCR7 may serve as a novel prognostic biomarker. © 2015 Wiley Periodicals, Inc.

  3. The Institution Image and Trust and Their Effect on the Positive Word of Mouth

    Directory of Open Access Journals (Sweden)

    Soni Harsono

    2014-04-01

    Full Text Available In marketing, it is important to see how competitive a university is. Among public universities (PTN and private universities (PTS, it shows a very competitive situation recently. To overcome this problem, it requires shaping up the institution image and trust for increasing the positive word of mouth among students. This study aims to determine the effect of the institution image, trust both partially and simultaneously on the positive word of mouth by the students of private universities in Surabaya with their accreditation levels of A, B and C. The sample consists of students from six colleges with accreditation ratings A, B, and C totaling 125 students. Accidental sampling technique was done using a sampling technique of multiple regression analysis with SPSS version 17. It shows, for the college with accreditation category C, the image of the institution both partially and simulta-neously has significant positive effect on the positive word of mouth. For the college accreditation category B, the image of the institution and trust simultaneously has significant positive effect on the positive word of mouth and, finally, trust in accreditation category A has significant positive effect on the positive word of mouth and the institution image and trust simultaneously have significant positive effect on the positive word of mouth.

  4. Allostatic load in parents of children with developmental disorders: moderating influence of positive affect.

    Science.gov (United States)

    Song, Jieun; Mailick, Marsha R; Ryff, Carol D; Coe, Christopher L; Greenberg, Jan S; Hong, Jinkuk

    2014-02-01

    This study examines whether parents of children with developmental disorders are at risk of elevated allostatic load relative to control parents and whether positive affect moderates difference in risk. In all, 38 parents of children with developmental disorders and 38 matched comparison parents were analyzed. Regression analyses revealed a significant interaction between parent status and positive affect: parents of children with developmental disorders had lower allostatic load when they had higher positive affect, whereas no such association was evident for comparison parents. The findings suggest that promoting greater positive affect may lower health risks among parents of children with developmental disorders.

  5. Time-adaptive quantile regression

    DEFF Research Database (Denmark)

    Møller, Jan Kloppenborg; Nielsen, Henrik Aalborg; Madsen, Henrik

    2008-01-01

    and an updating procedure are combined into a new algorithm for time-adaptive quantile regression, which generates new solutions on the basis of the old solution, leading to savings in computation time. The suggested algorithm is tested against a static quantile regression model on a data set with wind power......An algorithm for time-adaptive quantile regression is presented. The algorithm is based on the simplex algorithm, and the linear optimization formulation of the quantile regression problem is given. The observations have been split to allow a direct use of the simplex algorithm. The simplex method...... production, where the models combine splines and quantile regression. The comparison indicates superior performance for the time-adaptive quantile regression in all the performance parameters considered....

  6. Novel three dimensional position analysis of the mandibular foramen in patients with skeletal class III mandibular prognathism

    International Nuclear Information System (INIS)

    Kang, Sang Hoon; Kim, Yeon Ho; Won, Yu Jin; Kim, Moon Key

    2016-01-01

    To analyze the relative position of the mandibular foramina (MnFs) in patients diagnosed with skeletal class III malocclusion. Computed tomography (CT) images were collected from 85 patients. The vertical lengths of each anatomic point from the five horizontal planes passing through the MnF were measured at the coronoid process, sigmoid notch, condyle, and the gonion. The distance from the anterior ramus point to the posterior ramus point on the five horizontal planes was designated the anteroposterior horizontal distance of the ramus for each plane. The perpendicular distance from each anterior ramus point to each vertical plane through the MnF was designated the horizontal distance from the anterior ramus to the Mn F. The horizontal and vertical positions were examined by regression analysis. Regression analysis showed the heights of the coronoid process, sigmoid notch, and condyle for the five horizontal planes were significantly related to the height of the MnF, with the highest significance associated with the MnF-mandibular plane (coefficients of determination (R2): 0.424, 0.597, and 0.604, respectively). The horizontal anteroposterior length of the ramus and the distance from the anterior ramus point to the MnF were significant by regression analysis. The relative position of the MnF was significantly related to the vertical heights of the sigmoid notch, coronoid process, and condyle as well as to the horizontal anteroposterior length of the ascending ramus. These findings should be clinically useful for patients with skeletal class III mandibular prognathism

  7. Novel three dimensional position analysis of the mandibular foramen in patients with skeletal class III mandibular prognathism

    Energy Technology Data Exchange (ETDEWEB)

    Kang, Sang Hoon; Kim, Yeon Ho; Won, Yu Jin; Kim, Moon Key [Dept. of Oral and Maxillofacial Surgery, National Health Insurance Service Ilsan Hospital, Goyang (Korea, Republic of)

    2016-06-15

    To analyze the relative position of the mandibular foramina (MnFs) in patients diagnosed with skeletal class III malocclusion. Computed tomography (CT) images were collected from 85 patients. The vertical lengths of each anatomic point from the five horizontal planes passing through the MnF were measured at the coronoid process, sigmoid notch, condyle, and the gonion. The distance from the anterior ramus point to the posterior ramus point on the five horizontal planes was designated the anteroposterior horizontal distance of the ramus for each plane. The perpendicular distance from each anterior ramus point to each vertical plane through the MnF was designated the horizontal distance from the anterior ramus to the Mn F. The horizontal and vertical positions were examined by regression analysis. Regression analysis showed the heights of the coronoid process, sigmoid notch, and condyle for the five horizontal planes were significantly related to the height of the MnF, with the highest significance associated with the MnF-mandibular plane (coefficients of determination (R2): 0.424, 0.597, and 0.604, respectively). The horizontal anteroposterior length of the ramus and the distance from the anterior ramus point to the MnF were significant by regression analysis. The relative position of the MnF was significantly related to the vertical heights of the sigmoid notch, coronoid process, and condyle as well as to the horizontal anteroposterior length of the ascending ramus. These findings should be clinically useful for patients with skeletal class III mandibular prognathism.

  8. Clinical significance of isolated anti-HBc positivity in cases of chronic liver disease in New Delhi, India

    Directory of Open Access Journals (Sweden)

    Manisha Jain

    2009-01-01

    Full Text Available Background: The presence of anti-HBc IgG in the absence of HBsAg is usually indicative of a past self-limiting HBV infection. But it is frequently associated with co-infection with HCV which can worsen the existing status of chronic liver disease (CLD. Objectives: The present study was planned to evaluate the significance of isolated HBc IgG positivity in patients of CLD and look for the presence of HCV co-infection in such patients. Methods: Clinical profiles and biochemical tests were done for all the 77 CLD cases included in the study. Blood samples were taken from these patients and tested by the commercially available EIA for the presence of HBsAg, anti-HBc IgG, anti-HBs and anti-HCV. HBV DNA was detected by amplifying the surface region in all the cases. Results: Isolated anti-HBc IgG positivity defined as the presence of anti-HBc IgG in absence of any other serological markers of HBV infection was detected in 28 patients . Out of 64 patients positive for anti-HBc IgG 36 had the markers of HBV, either HBsAg, HBV DNA or anti-HBs alone or in combination. There was a significant association between isolated anti-HBc IgG positivity and HCV co-infection. Conclusion: Anti-HBc IgG should be tested in all patients with CLD as it is frequently the only marker of HBV infection in such patients and they should be monitored closely as such patients can develop CLD. Presence of co-infection with HCV should be actively searched for in such patients.

  9. Financial position and adoption of electronic health records: a retrospective longitudinal study.

    Science.gov (United States)

    Shen, Jay J; Ginn, Gregory O

    2012-01-01

    Financial barriers are a major factor of slow electronic health record (EHR) adoption among US hospitals. All existing literature focuses on relationships between current or short-term financial position and EHR adoption. This study examines relationship between financial position in previous years and the current level of EHR adoption. Retrospective longitudinal data were extracted from (1) the 2009 American Hospital Association (AHA) EHR implementation survey; (2) the 2002 and 2006 Centers for Medicare & Medicaid Cost Reports; and (3) the 2002 and 2006 AHA Annual Survey containing organizational and operational data. The final sample was 2,701 acute care hospitals in the United States. General ordinal logistic regression was used for data analysis with a three-level dependent variable to measure adoption, five independent variables to measure financial position, and 11 control variables to measure structure and environment. For 2006, higher total margin was significantly and positively associated with EHR adoption, but higher asset turnover was significantly and negatively associated with EHR adoption. For 2002, higher total margin was significantly and positively associated with EHR adoption, but higher asset turnover and higher equity multiplier were both significantly and negatively associated with EHR adoption. In addition, lower net days revenue in accounts receivable was significantly and positively associated with EHR adoption. For both the 2002 and 2006 control variables, human resource intensity and bed size were significant and positively related to adoption, and percentage Medicare patients and investor ownership were significant and negatively related to adoption. Financial position does relate to EHR adoption in mid-term and long-term planning.

  10. Modeling Fire Occurrence at the City Scale: A Comparison between Geographically Weighted Regression and Global Linear Regression.

    Science.gov (United States)

    Song, Chao; Kwan, Mei-Po; Zhu, Jiping

    2017-04-08

    An increasing number of fires are occurring with the rapid development of cities, resulting in increased risk for human beings and the environment. This study compares geographically weighted regression-based models, including geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR), which integrates spatial and temporal effects and global linear regression models (LM) for modeling fire risk at the city scale. The results show that the road density and the spatial distribution of enterprises have the strongest influences on fire risk, which implies that we should focus on areas where roads and enterprises are densely clustered. In addition, locations with a large number of enterprises have fewer fire ignition records, probably because of strict management and prevention measures. A changing number of significant variables across space indicate that heterogeneity mainly exists in the northern and eastern rural and suburban areas of Hefei city, where human-related facilities or road construction are only clustered in the city sub-centers. GTWR can capture small changes in the spatiotemporal heterogeneity of the variables while GWR and LM cannot. An approach that integrates space and time enables us to better understand the dynamic changes in fire risk. Thus governments can use the results to manage fire safety at the city scale.

  11. Application of regression model on stream water quality parameters

    International Nuclear Information System (INIS)

    Suleman, M.; Maqbool, F.; Malik, A.H.; Bhatti, Z.A.

    2012-01-01

    Statistical analysis was conducted to evaluate the effect of solid waste leachate from the open solid waste dumping site of Salhad on the stream water quality. Five sites were selected along the stream. Two sites were selected prior to mixing of leachate with the surface water. One was of leachate and other two sites were affected with leachate. Samples were analyzed for pH, water temperature, electrical conductivity (EC), total dissolved solids (TDS), Biological oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO) and total bacterial load (TBL). In this study correlation coefficient r among different water quality parameters of various sites were calculated by using Pearson model and then average of each correlation between two parameters were also calculated, which shows TDS and EC and pH and BOD have significantly increasing r value, while temperature and TDS, temp and EC, DO and BL, DO and COD have decreasing r value. Single factor ANOVA at 5% level of significance was used which shows EC, TDS, TCL and COD were significantly differ among various sites. By the application of these two statistical approaches TDS and EC shows strongly positive correlation because the ions from the dissolved solids in water influence the ability of that water to conduct an electrical current. These two parameters significantly vary among 5 sites which are further confirmed by using linear regression. (author)

  12. Association between Positivity and Smoking Cessation

    Directory of Open Access Journals (Sweden)

    Maria Caterina Grassi

    2014-01-01

    Full Text Available The literature documents that personality characteristics are associated with healthy lifestyles, including smoking. Among positive traits, Positivity (POS, defined as a general disposition conducive to facing experience under a positive outlook has shown robust associations with psychological health. Thus, the present study investigated the extent to which POS is able to predict (i relapse after quitting smoking and (ii the desire to smoke again. All participants (481 had previously attended a Group Counselling Program (GCP for Smoking Cessation (from 2005 through 2010. They were contacted through telephone interview. Among participants, 244 were ex-smokers (age: years 56.3±10.08, 52% female and 237 were still-smokers (age: years 55.0±9.63; 63.5% female. The association of POS with “craving to smoke” levels was assessed with multivariate linear regression analysis while controlling also for important differences in personality such as conscientiousness and general self-efficacy, as well as for gender and age. Results showed that POS was significantly and negatively associated with smoking status and with craving to smoke. Among covariates (i.e., conscientiousness, generalized self-efficacy, gender was associated with smoking status and with craving to smoke. Altogether these findings corroborate the idea that POS plays a significant role in sustaining individuals' efforts to quit smoking.

  13. Determination of baroreflex sensitivity during the modified Oxford maneuver by trigonometric regressive spectral analysis.

    Directory of Open Access Journals (Sweden)

    Julia Gasch

    Full Text Available BACKGROUND: Differences in spontaneous and drug-induced baroreflex sensitivity (BRS have been attributed to its different operating ranges. The current study attempted to compare BRS estimates during cardiovascular steady-state and pharmacologically stimulation using an innovative algorithm for dynamic determination of baroreflex gain. METHODOLOGY/PRINCIPAL FINDINGS: Forty-five volunteers underwent the modified Oxford maneuver in supine and 60° tilted position with blood pressure and heart rate being continuously recorded. Drug-induced BRS-estimates were calculated from data obtained by bolus injections of nitroprusside and phenylephrine. Spontaneous indices were derived from data obtained during rest (stationary and under pharmacological stimulation (non-stationary using the algorithm of trigonometric regressive spectral analysis (TRS. Spontaneous and drug-induced BRS values were significantly correlated and display directionally similar changes under different situations. Using the Bland-Altman method, systematic differences between spontaneous and drug-induced estimates were found and revealed that the discrepancy can be as large as the gain itself. Fixed bias was not evident with ordinary least products regression. The correlation and agreement between the estimates increased significantly when BRS was calculated by TRS in non-stationary mode during the drug injection period. TRS-BRS significantly increased during phenylephrine and decreased under nitroprusside. CONCLUSIONS/SIGNIFICANCE: The TRS analysis provides a reliable, non-invasive assessment of human BRS not only under static steady state conditions, but also during pharmacological perturbation of the cardiovascular system.

  14. Activity in ventromedial prefrontal cortex during self-related processing: positive subjective value or personal significance?

    Science.gov (United States)

    Kim, Kyungmi; Johnson, Marcia K

    2015-04-01

    Well-being and subjective experience of a coherent world depend on our sense of 'self' and relations between the self and the environment (e.g. people, objects and ideas). The ventromedial prefrontal cortex (vMPFC) is involved in self-related processing, and disrupted vMPFC activity is associated with disruptions of emotional/social functioning (e.g. depression and autism). Clarifying precise function(s) of vMPFC in self-related processing is an area of active investigation. In this study, we sought to more specifically characterize the function of vMPFC in self-related processing, focusing on two alternative accounts: (i) assignment of positive subjective value to self-related information and (ii) assignment of personal significance to self-related information. During functional magnetic resonance imaging (fMRI), participants imagined owning objects associated with either their perceived ingroup or outgroup. We found that for ingroup-associated objects, vMPFC showed greater activity for objects with increased than decreased post-ownership preference. In contrast, for outgroup-associated objects, vMPFC showed greater activity for objects with decreased than increased post-ownership preference. Our findings support the idea that the function of vMPFC in self-related processing may not be to represent/evaluate the 'positivity' or absolute preference of self-related information but to assign personal significance to it based on its meaning/function for the self. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  15. Regression of Ophthalmopathic Exophthalmos in Graves' Disease After Total Thyroidectomy: a Prospective Study of a Surgical Series.

    Science.gov (United States)

    Bhargav, P R K; Sabaretnam, M; Kumar, S Chandra; Zwalitha, S; Devi, N Vimala

    2017-12-01

    Autoimmune ophthalmopathy is one of the salient clinical features associated with Graves' disease. Exophthalmos is one of the commonest manifestations of Graves' associated ophthalmopathy. It is reported to regress after thyroidectomy favourably compared to radioiodine or antithyroid drug therapy. In this context, we present our experience based on a surgical series of Graves' disease. This is a prospective study of 15 patients of Graves' disease associated with ophthalmopathic exophthalmos. Preoperative and monthly postoperative evaluation of exophthalmos was done with Hertel's exophthalmometer, apart from documenting lid, extra-ocular muscle and orbital involvement. The minimum follow-up of the cohort was 12 months. The female to male ratio was 12:3 and the mean age of the subjects was 33.4 years (18-55). Exophthalmos was bilateral in 13 and unilateral in 2 patients. All the 15 patients underwent total thyroidectomy without any major morbidity. Exophthalmos regressed in 12 patients at a mean follow-up of 15.6 ± 6.4 months (14-38) and was static in 3. None of the cases had worsened ophthalmopathy at the final follow-up. Mean regression of exophthalmos was 2.1 mm (1-5). The regression was statistically significant at P value = 0.035. Surgery has a positive impact on the regression of ophthalmopathic exophthalmos associated with Graves' disease.

  16. Schoolwide Positive Behavior Supports and Students with Significant Disabilities: Where Are We?

    Science.gov (United States)

    Kurth, Jennifer A.; Enyart, Matt

    2016-01-01

    Although the number of schools implementing schoolwide positive behavior supports (SWPBS) has increased dramatically, the inclusion of students with severe disabilities in these efforts remains negligible. This article describes the evolution of positive behavior intervention and supports into the SWPBS approach used in many schools today,…

  17. Regression analysis by example

    CERN Document Server

    Chatterjee, Samprit

    2012-01-01

    Praise for the Fourth Edition: ""This book is . . . an excellent source of examples for regression analysis. It has been and still is readily readable and understandable."" -Journal of the American Statistical Association Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. Regression Analysis by Example, Fifth Edition has been expanded

  18. Applied logistic regression

    CERN Document Server

    Hosmer, David W; Sturdivant, Rodney X

    2013-01-01

     A new edition of the definitive guide to logistic regression modeling for health science and other applications This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-

  19. Normalization Ridge Regression in Practice I: Comparisons Between Ordinary Least Squares, Ridge Regression and Normalization Ridge Regression.

    Science.gov (United States)

    Bulcock, J. W.

    The problem of model estimation when the data are collinear was examined. Though the ridge regression (RR) outperforms ordinary least squares (OLS) regression in the presence of acute multicollinearity, it is not a problem free technique for reducing the variance of the estimates. It is a stochastic procedure when it should be nonstochastic and it…

  20. Spontaneous regression of retinopathy of prematurity:incidence and predictive factors

    Directory of Open Access Journals (Sweden)

    Rui-Hong Ju

    2013-08-01

    Full Text Available AIM:To evaluate the incidence of spontaneous regression of changes in the retina and vitreous in active stage of retinopathy of prematurity(ROP and identify the possible relative factors during the regression.METHODS: This was a retrospective, hospital-based study. The study consisted of 39 premature infants with mild ROP showed spontaneous regression (Group A and 17 with severe ROP who had been treated before naturally involuting (Group B from August 2008 through May 2011. Data on gender, single or multiple pregnancy, gestational age, birth weight, weight gain from birth to the sixth week of life, use of oxygen in mechanical ventilation, total duration of oxygen inhalation, surfactant given or not, need for and times of blood transfusion, 1,5,10-min Apgar score, presence of bacterial or fungal or combined infection, hyaline membrane disease (HMD, patent ductus arteriosus (PDA, duration of stay in the neonatal intensive care unit (NICU and duration of ROP were recorded.RESULTS: The incidence of spontaneous regression of ROP with stage 1 was 86.7%, and with stage 2, stage 3 was 57.1%, 5.9%, respectively. With changes in zone Ⅲ regression was detected 100%, in zoneⅡ 46.2% and in zoneⅠ 0%. The mean duration of ROP in spontaneous regression group was 5.65±3.14 weeks, lower than that of the treated ROP group (7.34±4.33 weeks, but this difference was not statistically significant (P=0.201. GA, 1min Apgar score, 5min Apgar score, duration of NICU stay, postnatal age of initial screening and oxygen therapy longer than 10 days were significant predictive factors for the spontaneous regression of ROP (P<0.05. Retinal hemorrhage was the only independent predictive factor the spontaneous regression of ROP (OR 0.030, 95%CI 0.001-0.775, P=0.035.CONCLUSION:This study showed most stage 1 and 2 ROP and changes in zone Ⅲ can spontaneously regression in the end. Retinal hemorrhage is weakly inversely associated with the spontaneous regression.

  1. Multiple regression analysis of Jominy hardenability data for boron treated steels

    International Nuclear Information System (INIS)

    Komenda, J.; Sandstroem, R.; Tukiainen, M.

    1997-01-01

    The relations between chemical composition and their hardenability of boron treated steels have been investigated using a multiple regression analysis method. A linear model of regression was chosen. The free boron content that is effective for the hardenability was calculated using a model proposed by Jansson. The regression analysis for 1261 steel heats provided equations that were statistically significant at the 95% level. All heats met the specification according to the nordic countries producers classification. The variation in chemical composition explained typically 80 to 90% of the variation in the hardenability. In the regression analysis elements which did not significantly contribute to the calculated hardness according to the F test were eliminated. Carbon, silicon, manganese, phosphorus and chromium were of importance at all Jominy distances, nickel, vanadium, boron and nitrogen at distances above 6 mm. After the regression analysis it was demonstrated that very few outliers were present in the data set, i.e. data points outside four times the standard deviation. The model has successfully been used in industrial practice replacing some of the necessary Jominy tests. (orig.)

  2. Principal component regression for crop yield estimation

    CERN Document Server

    Suryanarayana, T M V

    2016-01-01

    This book highlights the estimation of crop yield in Central Gujarat, especially with regard to the development of Multiple Regression Models and Principal Component Regression (PCR) models using climatological parameters as independent variables and crop yield as a dependent variable. It subsequently compares the multiple linear regression (MLR) and PCR results, and discusses the significance of PCR for crop yield estimation. In this context, the book also covers Principal Component Analysis (PCA), a statistical procedure used to reduce a number of correlated variables into a smaller number of uncorrelated variables called principal components (PC). This book will be helpful to the students and researchers, starting their works on climate and agriculture, mainly focussing on estimation models. The flow of chapters takes the readers in a smooth path, in understanding climate and weather and impact of climate change, and gradually proceeds towards downscaling techniques and then finally towards development of ...

  3. Logistic regression model for diagnosis of transition zone prostate cancer on multi-parametric MRI.

    Science.gov (United States)

    Dikaios, Nikolaos; Alkalbani, Jokha; Sidhu, Harbir Singh; Fujiwara, Taiki; Abd-Alazeez, Mohamed; Kirkham, Alex; Allen, Clare; Ahmed, Hashim; Emberton, Mark; Freeman, Alex; Halligan, Steve; Taylor, Stuart; Atkinson, David; Punwani, Shonit

    2015-02-01

    We aimed to develop logistic regression (LR) models for classifying prostate cancer within the transition zone on multi-parametric magnetic resonance imaging (mp-MRI). One hundred and fifty-five patients (training cohort, 70 patients; temporal validation cohort, 85 patients) underwent mp-MRI and transperineal-template-prostate-mapping (TPM) biopsy. Positive cores were classified by cancer definitions: (1) any-cancer; (2) definition-1 [≥Gleason 4 + 3 or ≥ 6 mm cancer core length (CCL)] [high risk significant]; and (3) definition-2 (≥Gleason 3 + 4 or ≥ 4 mm CCL) cancer [intermediate-high risk significant]. For each, logistic-regression mp-MRI models were derived from the training cohort and validated internally and with the temporal cohort. Sensitivity/specificity and the area under the receiver operating characteristic (ROC-AUC) curve were calculated. LR model performance was compared to radiologists' performance. Twenty-eight of 70 patients from the training cohort, and 25/85 patients from the temporal validation cohort had significant cancer on TPM. The ROC-AUC of the LR model for classification of cancer was 0.73/0.67 at internal/temporal validation. The radiologist A/B ROC-AUC was 0.65/0.74 (temporal cohort). For patients scored by radiologists as Prostate Imaging Reporting and Data System (Pi-RADS) score 3, sensitivity/specificity of radiologist A 'best guess' and LR model was 0.14/0.54 and 0.71/0.61, respectively; and radiologist B 'best guess' and LR model was 0.40/0.34 and 0.50/0.76, respectively. LR models can improve classification of Pi-RADS score 3 lesions similar to experienced radiologists. • MRI helps find prostate cancer in the anterior of the gland • Logistic regression models based on mp-MRI can classify prostate cancer • Computers can help confirm cancer in areas doctors are uncertain about.

  4. Applied linear regression

    CERN Document Server

    Weisberg, Sanford

    2013-01-01

    Praise for the Third Edition ""...this is an excellent book which could easily be used as a course text...""-International Statistical Institute The Fourth Edition of Applied Linear Regression provides a thorough update of the basic theory and methodology of linear regression modeling. Demonstrating the practical applications of linear regression analysis techniques, the Fourth Edition uses interesting, real-world exercises and examples. Stressing central concepts such as model building, understanding parameters, assessing fit and reliability, and drawing conclusions, the new edition illus

  5. Assessing risk factors for periodontitis using regression

    Science.gov (United States)

    Lobo Pereira, J. A.; Ferreira, Maria Cristina; Oliveira, Teresa

    2013-10-01

    Multivariate statistical analysis is indispensable to assess the associations and interactions between different factors and the risk of periodontitis. Among others, regression analysis is a statistical technique widely used in healthcare to investigate and model the relationship between variables. In our work we study the impact of socio-demographic, medical and behavioral factors on periodontal health. Using regression, linear and logistic models, we can assess the relevance, as risk factors for periodontitis disease, of the following independent variables (IVs): Age, Gender, Diabetic Status, Education, Smoking status and Plaque Index. The multiple linear regression analysis model was built to evaluate the influence of IVs on mean Attachment Loss (AL). Thus, the regression coefficients along with respective p-values will be obtained as well as the respective p-values from the significance tests. The classification of a case (individual) adopted in the logistic model was the extent of the destruction of periodontal tissues defined by an Attachment Loss greater than or equal to 4 mm in 25% (AL≥4mm/≥25%) of sites surveyed. The association measures include the Odds Ratios together with the correspondent 95% confidence intervals.

  6. Regression: The Apple Does Not Fall Far From the Tree.

    Science.gov (United States)

    Vetter, Thomas R; Schober, Patrick

    2018-05-15

    Researchers and clinicians are frequently interested in either: (1) assessing whether there is a relationship or association between 2 or more variables and quantifying this association; or (2) determining whether 1 or more variables can predict another variable. The strength of such an association is mainly described by the correlation. However, regression analysis and regression models can be used not only to identify whether there is a significant relationship or association between variables but also to generate estimations of such a predictive relationship between variables. This basic statistical tutorial discusses the fundamental concepts and techniques related to the most common types of regression analysis and modeling, including simple linear regression, multiple regression, logistic regression, ordinal regression, and Poisson regression, as well as the common yet often underrecognized phenomenon of regression toward the mean. The various types of regression analysis are powerful statistical techniques, which when appropriately applied, can allow for the valid interpretation of complex, multifactorial data. Regression analysis and models can assess whether there is a relationship or association between 2 or more observed variables and estimate the strength of this association, as well as determine whether 1 or more variables can predict another variable. Regression is thus being applied more commonly in anesthesia, perioperative, critical care, and pain research. However, it is crucial to note that regression can identify plausible risk factors; it does not prove causation (a definitive cause and effect relationship). The results of a regression analysis instead identify independent (predictor) variable(s) associated with the dependent (outcome) variable. As with other statistical methods, applying regression requires that certain assumptions be met, which can be tested with specific diagnostics.

  7. Arcuate Fasciculus in Autism Spectrum Disorder Toddlers with Language Regression

    Directory of Open Access Journals (Sweden)

    Zhang Lin

    2018-03-01

    Full Text Available Language regression is observed in a subset of toddlers with autism spectrum disorder (ASD as initial symptom. However, such a phenomenon has not been fully explored, partly due to the lack of definite diagnostic evaluation methods and criteria. Materials and Methods: Fifteen toddlers with ASD exhibiting language regression and fourteen age-matched typically developing (TD controls underwent diffusion tensor imaging (DTI. DTI parameters including fractional anisotropy (FA, average fiber length (AFL, tract volume (TV and number of voxels (NV were analyzed by Neuro 3D in Siemens syngo workstation. Subsequently, the data were analyzed by using IBM SPSS Statistics 22. Results: Compared with TD children, a significant reduction of FA along with an increase in TV and NV was observed in ASD children with language regression. Note that there were no significant differences between ASD and TD children in AFL of the arcuate fasciculus (AF. Conclusions: These DTI changes in the AF suggest that microstructural anomalies of the AF white matter may be associated with language deficits in ASD children exhibiting language regression starting from an early age.

  8. Understanding poisson regression.

    Science.gov (United States)

    Hayat, Matthew J; Higgins, Melinda

    2014-04-01

    Nurse investigators often collect study data in the form of counts. Traditional methods of data analysis have historically approached analysis of count data either as if the count data were continuous and normally distributed or with dichotomization of the counts into the categories of occurred or did not occur. These outdated methods for analyzing count data have been replaced with more appropriate statistical methods that make use of the Poisson probability distribution, which is useful for analyzing count data. The purpose of this article is to provide an overview of the Poisson distribution and its use in Poisson regression. Assumption violations for the standard Poisson regression model are addressed with alternative approaches, including addition of an overdispersion parameter or negative binomial regression. An illustrative example is presented with an application from the ENSPIRE study, and regression modeling of comorbidity data is included for illustrative purposes. Copyright 2014, SLACK Incorporated.

  9. Alternative Methods of Regression

    CERN Document Server

    Birkes, David

    2011-01-01

    Of related interest. Nonlinear Regression Analysis and its Applications Douglas M. Bates and Donald G. Watts ".an extraordinary presentation of concepts and methods concerning the use and analysis of nonlinear regression models.highly recommend[ed].for anyone needing to use and/or understand issues concerning the analysis of nonlinear regression models." --Technometrics This book provides a balance between theory and practice supported by extensive displays of instructive geometrical constructs. Numerous in-depth case studies illustrate the use of nonlinear regression analysis--with all data s

  10. The influences of working memory representations on long-range regression in text reading: An eye-tracking study

    Directory of Open Access Journals (Sweden)

    Teppei eTanaka

    2014-09-01

    Full Text Available The present study investigated the relationship between verbal and visuospatial working memory capacity and long-range regression (i.e., word relocation processes in reading. We analyzed eye movements during a whodunit task, in which readers were asked to answer a content question while original text was being presented. The eye movements were more efficient in relocating a target word when the target was at recency positions within the text than when it was at primacy positions. Furthermore, both verbal and visuospatial working memory capacity partly predicted the efficiency of the initial long-range regression. The results indicate that working memory representations have a strong influence at the first stage of long-range regression by driving the first saccade movement toward the correct target position, suggesting that there is a dynamic interaction between internal working memory representations and external actions during text reading.

  11. Introduction to regression graphics

    CERN Document Server

    Cook, R Dennis

    2009-01-01

    Covers the use of dynamic and interactive computer graphics in linear regression analysis, focusing on analytical graphics. Features new techniques like plot rotation. The authors have composed their own regression code, using Xlisp-Stat language called R-code, which is a nearly complete system for linear regression analysis and can be utilized as the main computer program in a linear regression course. The accompanying disks, for both Macintosh and Windows computers, contain the R-code and Xlisp-Stat. An Instructor's Manual presenting detailed solutions to all the problems in the book is ava

  12. Identifying risk factors associated with smear positivity of pulmonary tuberculosis in Kazakhstan.

    Directory of Open Access Journals (Sweden)

    Sabrina Hermosilla

    Full Text Available Sputum smear-positive tuberculosis (TB patients have a high risk of transmission and are of great epidemiological and infection control significance. Little is known about the smear-positive populations in high TB burden regions, such as Kazakhstan. The objective of this study is to characterize the smear-positive population in Kazakhstan and identify associated modifiable risk factors.Data on incident TB cases' (identified between April 2012 and March 2014 socio-demographic, risk behavior, and comorbidity characteristics were collected in four regions of Kazakhstan through structured survey and medical record review. We used multivariable logistic regression to determine factors associated with smear positivity.Of the total sample, 193 (34.3% of the 562 study participants tested smear-positive. In the final adjusted multivariable logistic regression model, sex (adjusted odds ratio (aOR = 2.0, 95% CI:1.3-3.1, p < 0.01, incarceration (aOR = 3.6, 95% CI:1.2-11.1, p = 0.03, alcohol dependence (aOR = 2.6, 95% CI:1.2-5.7, p = 0.02, diabetes (aOR = 5.0, 95% CI:2.4-10.7, p < 0.01, and physician access (aOR = 2.7, 95% CI:1.3-5.5p < 0.01 were associated with smear-positivity.Incarceration, alcohol dependence, diabetes, and physician access are associated with smear positivity among incident TB cases in Kazakhstan. To stem the TB epidemic, screening, treatment and prevention policies should address these factors.

  13. Tuberculosis risk factors in children with smear-positive tuberculosis adult as household contact

    Directory of Open Access Journals (Sweden)

    Nora Hajarsjah

    2018-04-01

    Full Text Available Background Children in household contact of adults with smear-positive tuberculosis (TB are at higher risk of TB infection. Screening of these children is a main strategy for eliminating childhood TB. Objective To determine risk factors of TB among children in household contact with smear-positive adult TB patients. Methods This case-control study was conducted in 5 public health centers at Batu Bara District, North Sumatera. We studied children from birth to 18 year-old living in the same house as adults with smear-positive TB. A tuberculosis scoring system was used to diagnosis TB in the children. Associations between risk factors and the incidence of TB were analyzed using Chi-square, Mann-Whitney U, and logistic regression tests. Results We enrolled 145 children who had household contact with smear-positive adult TB patients. Subjects were allocated to either the case group [TB score >6; 61 subjects (42.0%] or the control group [TB score <6; 84 subjects (58.0%]. Bivariate analysis revealed that nutritional status, immunization status, number of people in the house, sleeping in the same bed, and duration of household contact had significant associations with the incidence of TB. By multivariate logistic regression analysis, nutritional status and duration of household contact were significant risk factors for TB, with OR 5.89 and 8.91, respectively. Conclusion Malnutrition and duration of household contact with smear-positive adult TB patients of more than 6 hours per day were risk factors for TB among children.

  14. Mitigating Errors in External Respiratory Surrogate-Based Models of Tumor Position

    International Nuclear Information System (INIS)

    Malinowski, Kathleen T.; McAvoy, Thomas J.; George, Rohini; Dieterich, Sonja; D'Souza, Warren D.

    2012-01-01

    Purpose: To investigate the effect of tumor site, measurement precision, tumor–surrogate correlation, training data selection, model design, and interpatient and interfraction variations on the accuracy of external marker-based models of tumor position. Methods and Materials: Cyberknife Synchrony system log files comprising synchronously acquired positions of external markers and the tumor from 167 treatment fractions were analyzed. The accuracy of Synchrony, ordinary-least-squares regression, and partial-least-squares regression models for predicting the tumor position from the external markers was evaluated. The quantity and timing of the data used to build the predictive model were varied. The effects of tumor–surrogate correlation and the precision in both the tumor and the external surrogate position measurements were explored by adding noise to the data. Results: The tumor position prediction errors increased during the duration of a fraction. Increasing the training data quantities did not always lead to more accurate models. Adding uncorrelated noise to the external marker-based inputs degraded the tumor–surrogate correlation models by 16% for partial-least-squares and 57% for ordinary-least-squares. External marker and tumor position measurement errors led to tumor position prediction changes 0.3–3.6 times the magnitude of the measurement errors, varying widely with model algorithm. The tumor position prediction errors were significantly associated with the patient index but not with the fraction index or tumor site. Partial-least-squares was as accurate as Synchrony and more accurate than ordinary-least-squares. Conclusions: The accuracy of surrogate-based inferential models of tumor position was affected by all the investigated factors, except for the tumor site and fraction index.

  15. Mitigating Errors in External Respiratory Surrogate-Based Models of Tumor Position

    Energy Technology Data Exchange (ETDEWEB)

    Malinowski, Kathleen T. [Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD (United States); Fischell Department of Bioengineering, University of Maryland, College Park, MD (United States); McAvoy, Thomas J. [Fischell Department of Bioengineering, University of Maryland, College Park, MD (United States); Department of Chemical and Biomolecular Engineering and Institute of Systems Research, University of Maryland, College Park, MD (United States); George, Rohini [Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD (United States); Dieterich, Sonja [Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA (United States); D' Souza, Warren D., E-mail: wdsou001@umaryland.edu [Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD (United States); Fischell Department of Bioengineering, University of Maryland, College Park, MD (United States)

    2012-04-01

    Purpose: To investigate the effect of tumor site, measurement precision, tumor-surrogate correlation, training data selection, model design, and interpatient and interfraction variations on the accuracy of external marker-based models of tumor position. Methods and Materials: Cyberknife Synchrony system log files comprising synchronously acquired positions of external markers and the tumor from 167 treatment fractions were analyzed. The accuracy of Synchrony, ordinary-least-squares regression, and partial-least-squares regression models for predicting the tumor position from the external markers was evaluated. The quantity and timing of the data used to build the predictive model were varied. The effects of tumor-surrogate correlation and the precision in both the tumor and the external surrogate position measurements were explored by adding noise to the data. Results: The tumor position prediction errors increased during the duration of a fraction. Increasing the training data quantities did not always lead to more accurate models. Adding uncorrelated noise to the external marker-based inputs degraded the tumor-surrogate correlation models by 16% for partial-least-squares and 57% for ordinary-least-squares. External marker and tumor position measurement errors led to tumor position prediction changes 0.3-3.6 times the magnitude of the measurement errors, varying widely with model algorithm. The tumor position prediction errors were significantly associated with the patient index but not with the fraction index or tumor site. Partial-least-squares was as accurate as Synchrony and more accurate than ordinary-least-squares. Conclusions: The accuracy of surrogate-based inferential models of tumor position was affected by all the investigated factors, except for the tumor site and fraction index.

  16. Dimensions of socioeconomic position related to body mass index and obesity among Danish women and men

    DEFF Research Database (Denmark)

    Groth, Margit Velsing; Fagt, Sisse; Stockmarr, Anders

    2009-01-01

    Aims: The aim of this study was to examine the association between different dimensions of socioeconomic position, body mass index (BMI) and obesity in the Danish population. Possible interactions between the different dimensions and gender differences were also investigated. Methods....... Associations between dimensions of socioeconomic position and weight status were examined by use of linear multiple regression analysis and logistic regression analysis. Results: BMI and prevalence of obesity were significantly associated with education for both men and women. Odds ratios (ORs) for obesity...... adjustment for educational level. Conclusions: Education was the dimension most consistently associated with BMI and obesity, indicating the importance of cultural capital for weight status. The gender-specific pattern showed a stronger social gradient for women, and indicated that a high relative body...

  17. Innovative Behavior and Psychological Capital: Does Positivity Make any Difference

    Directory of Open Access Journals (Sweden)

    Yomna M. Sameer

    2018-05-01

    Full Text Available Aim/purpose - Despite increasing importance of fostering innovation among employees, and the growing interest in Positive Organizational Behaviour (POB constructs, little empirical research has been conducted on the topic of innovation with POB. Moreover, though research proved significant relationship between positive psychological capital (PsyCap and creative performance, no studies examined PsyCap with innovative behavior. Thus, the purpose of this paper is to examine the link between positive psychological capital and innovative behavior as well as the link between innovative behavior and job satisfaction as well as engagement. Design/methodology/approach - Using regression analyses, data were obtained from Egyptian professionals (N = 250. The survey included measures of psychological capital and innovative behavior as well as job satisfaction and engagement. Findings - Regression analyses reveal that PsyCap, with its four components of hope, optimism, resilience and efficacy, predict innovative behavior, which in turn affects satisfaction and engagement. Research implications/limitations - Limitations, contributions and recommendations for future research are noted. Results contribute to a better understanding of how psychological capital enhances Innovative behavior in the workplace, which in turns, enhances job satisfaction and engagement. Originality/value/contribution - The study is the first to examine the relationship between psychological capital and innovative behavior.(original abstract

  18. Age Regression in the Treatment of Anger in a Prison Setting.

    Science.gov (United States)

    Eisel, Harry E.

    1988-01-01

    Incorporated hypnotherapy with age regression into cognitive therapeutic approach with prisoners having history of anger. Technique involved age regression to establish first significant event causing current anger, catharsis of feelings for original event, and reorientation of event while under hypnosis. Results indicated decrease in acting-out…

  19. Positive pacing in elite IRONMAN triathletes.

    Science.gov (United States)

    Angehrn, Nicole; Rüst, Christoph A.; Nikolaidis, Pantelis T.; Rosemann, Thomas; Knechtle, Beat

    2016-12-31

    Pacing is known to influence athletic performance. For the Ironman triathlon program, a positive pacing strategy, i.e., the continuous decrease of speed over time was recommended. By analyzing split times, we assessed the pacing strategies of the top 100 finishers of the cycling part of 13 Ironman races and of the running part of 11 Ironman races taking place in 2014. Furthermore, sex-associated differences in performance and pacing strategies were calculated. We analyzed 7,687 cycling and 11,894 running split times of 1,392 triathletes (1,263 men, 129 women). Changes in speed were assessed using mixed-effects regression analyses. A continuous decrease in speed was observed during cycling in 10/13 races, and during running in 11/11 races. In 6/13 races, women decreased their speed during cycling significantly more than men. The running part showed no significant difference of changes in speed between the sexes. In summary, in the Ironman races evaluated, a positive pacing strategy was adopted in most races. Women were slower than men in 6/13 cycling races, but there was no difference between men and women in the run splits. Women used the same pacing strategy as men.

  20. Leadership and regressive group processes: a pilot study.

    Science.gov (United States)

    Rudden, Marie G; Twemlow, Stuart; Ackerman, Steven

    2008-10-01

    Various perspectives on leadership within the psychoanalytic, organizational and sociobiological literature are reviewed, with particular attention to research studies in these areas. Hypotheses are offered about what makes an effective leader: her ability to structure tasks well in order to avoid destructive regressions, to make constructive use of the omnipresent regressive energies in group life, and to redirect regressions when they occur. Systematic qualitative observations of three videotaped sessions each from N = 18 medical staff work groups at an urban medical center are discussed, as is the utility of a scale, the Leadership and Group Regressions Scale (LGRS), that attempts to operationalize the hypotheses. Analyzing the tapes qualitatively, it was noteworthy that at times (in N = 6 groups), the nominal leader of the group did not prove to be the actual, working leader. Quantitatively, a significant correlation was seen between leaders' LGRS scores and the group's satisfactory completion of their quantitative goals (p = 0.007) and ability to sustain the goals (p = 0.04), when the score of the person who met criteria for group leadership was used.

  1. Testing for marginal linear effects in quantile regression

    KAUST Repository

    Wang, Huixia Judy

    2017-10-23

    The paper develops a new marginal testing procedure to detect significant predictors that are associated with the conditional quantiles of a scalar response. The idea is to fit the marginal quantile regression on each predictor one at a time, and then to base the test on the t-statistics that are associated with the most predictive predictors. A resampling method is devised to calibrate this test statistic, which has non-regular limiting behaviour due to the selection of the most predictive variables. Asymptotic validity of the procedure is established in a general quantile regression setting in which the marginal quantile regression models can be misspecified. Even though a fixed dimension is assumed to derive the asymptotic results, the test proposed is applicable and computationally feasible for large dimensional predictors. The method is more flexible than existing marginal screening test methods based on mean regression and has the added advantage of being robust against outliers in the response. The approach is illustrated by using an application to a human immunodeficiency virus drug resistance data set.

  2. Testing for marginal linear effects in quantile regression

    KAUST Repository

    Wang, Huixia Judy; McKeague, Ian W.; Qian, Min

    2017-01-01

    The paper develops a new marginal testing procedure to detect significant predictors that are associated with the conditional quantiles of a scalar response. The idea is to fit the marginal quantile regression on each predictor one at a time, and then to base the test on the t-statistics that are associated with the most predictive predictors. A resampling method is devised to calibrate this test statistic, which has non-regular limiting behaviour due to the selection of the most predictive variables. Asymptotic validity of the procedure is established in a general quantile regression setting in which the marginal quantile regression models can be misspecified. Even though a fixed dimension is assumed to derive the asymptotic results, the test proposed is applicable and computationally feasible for large dimensional predictors. The method is more flexible than existing marginal screening test methods based on mean regression and has the added advantage of being robust against outliers in the response. The approach is illustrated by using an application to a human immunodeficiency virus drug resistance data set.

  3. Ethanolic extract of Artemisia aucheri induces regression of aorta wall fatty streaks in hypercholesterolemic rabbits.

    Science.gov (United States)

    Asgary, S; Dinani, N Jafari; Madani, H; Mahzouni, P

    2008-05-01

    Artemisia aucheri is a native-growing plant which is widely used in Iranian traditional medicine. This study was designed to evaluate the effects of A. aucheri on regression of atherosclerosis in hypercholesterolemic rabbits. Twenty five rabbits were randomly divided into five groups of five each and treated 3-months as follows: 1: normal diet, 2: hypercholesterolemic diet (HCD), 3 and 4: HCD for 60 days and then normal diet and normal diet + A. aucheri (100 mg x kg(-1) x day(-1)) respectively for an additional 30 days (regression period). In the regression period dietary use of A. aucheri in group 4 significantly decreased total cholesterol, triglyceride and LDL-cholesterol, while HDL-cholesterol was significantly increased. The atherosclerotic area was significantly decreased in this group. Animals, which received only normal diet in the regression period showed no regression but rather progression of atherosclerosis. These findings suggest that A. aucheri may cause regression of atherosclerotic lesions.

  4. Predicting respiratory tumor motion with multi-dimensional adaptive filters and support vector regression

    International Nuclear Information System (INIS)

    Riaz, Nadeem; Wiersma, Rodney; Mao Weihua; Xing Lei; Shanker, Piyush; Gudmundsson, Olafur; Widrow, Bernard

    2009-01-01

    Intra-fraction tumor tracking methods can improve radiation delivery during radiotherapy sessions. Image acquisition for tumor tracking and subsequent adjustment of the treatment beam with gating or beam tracking introduces time latency and necessitates predicting the future position of the tumor. This study evaluates the use of multi-dimensional linear adaptive filters and support vector regression to predict the motion of lung tumors tracked at 30 Hz. We expand on the prior work of other groups who have looked at adaptive filters by using a general framework of a multiple-input single-output (MISO) adaptive system that uses multiple correlated signals to predict the motion of a tumor. We compare the performance of these two novel methods to conventional methods like linear regression and single-input, single-output adaptive filters. At 400 ms latency the average root-mean-square-errors (RMSEs) for the 14 treatment sessions studied using no prediction, linear regression, single-output adaptive filter, MISO and support vector regression are 2.58, 1.60, 1.58, 1.71 and 1.26 mm, respectively. At 1 s, the RMSEs are 4.40, 2.61, 3.34, 2.66 and 1.93 mm, respectively. We find that support vector regression most accurately predicts the future tumor position of the methods studied and can provide a RMSE of less than 2 mm at 1 s latency. Also, a multi-dimensional adaptive filter framework provides improved performance over single-dimension adaptive filters. Work is underway to combine these two frameworks to improve performance.

  5. Prediction of unwanted pregnancies using logistic regression, probit regression and discriminant analysis.

    Science.gov (United States)

    Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon

    2015-01-01

    Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended.

  6. Logistic regression function for detection of suspicious performance during baseline evaluations using concussion vital signs.

    Science.gov (United States)

    Hill, Benjamin David; Womble, Melissa N; Rohling, Martin L

    2015-01-01

    This study utilized logistic regression to determine whether performance patterns on Concussion Vital Signs (CVS) could differentiate known groups with either genuine or feigned performance. For the embedded measure development group (n = 174), clinical patients and undergraduate students categorized as feigning obtained significantly lower scores on the overall test battery mean for the CVS, Shipley-2 composite score, and California Verbal Learning Test-Second Edition subtests than did genuinely performing individuals. The final full model of 3 predictor variables (Verbal Memory immediate hits, Verbal Memory immediate correct passes, and Stroop Test complex reaction time correct) was significant and correctly classified individuals in their known group 83% of the time (sensitivity = .65; specificity = .97) in a mixed sample of young-adult clinical cases and simulators. The CVS logistic regression function was applied to a separate undergraduate college group (n = 378) that was asked to perform genuinely and identified 5% as having possibly feigned performance indicating a low false-positive rate. The failure rate was 11% and 16% at baseline cognitive testing in samples of high school and college athletes, respectively. These findings have particular relevance given the increasing use of computerized test batteries for baseline cognitive testing and return-to-play decisions after concussion.

  7. Trait emotional intelligence and mental distress: the mediating role of positive and negative affect.

    Science.gov (United States)

    Kong, Feng; Zhao, Jingjing; You, Xuqun

    2012-01-01

    Over the past decade, emotional intelligence (EI) has received much attention in the literature. Previous studies indicated that higher trait or ability EI was associated with greater mental distress. The present study focused on mediating effects of positive and negative affect on the association between trait EI and mental distress in a sample of Chinese adults. The participants were 726 Chinese adults (384 females) with an age range of 18-60 years. Data were collected by using the Wong Law Emotional Intelligence Scale, the Positive Affect and Negative Affect Scale, and the General Health Questionnaire. Hierarchical regression analysis showed that EI was a significant predictor of positive affect, negative affect and mental distress. Further mediation analysis showed that positive and negative affect acted as partial mediators of the relationship between EI and mental distress. Furthermore, effect contrasts showed that there was no significant difference between the specific indirect effects through positive affect and through negative affect. This result indicated that positive affect and negative affect played an equally important function in the association between EI and distress. The significance and limitations of the results are discussed.

  8. Prediction, Regression and Critical Realism

    DEFF Research Database (Denmark)

    Næss, Petter

    2004-01-01

    This paper considers the possibility of prediction in land use planning, and the use of statistical research methods in analyses of relationships between urban form and travel behaviour. Influential writers within the tradition of critical realism reject the possibility of predicting social...... phenomena. This position is fundamentally problematic to public planning. Without at least some ability to predict the likely consequences of different proposals, the justification for public sector intervention into market mechanisms will be frail. Statistical methods like regression analyses are commonly...... seen as necessary in order to identify aggregate level effects of policy measures, but are questioned by many advocates of critical realist ontology. Using research into the relationship between urban structure and travel as an example, the paper discusses relevant research methods and the kinds...

  9. Learning Inverse Rig Mappings by Nonlinear Regression.

    Science.gov (United States)

    Holden, Daniel; Saito, Jun; Komura, Taku

    2017-03-01

    We present a framework to design inverse rig-functions-functions that map low level representations of a character's pose such as joint positions or surface geometry to the representation used by animators called the animation rig. Animators design scenes using an animation rig, a framework widely adopted in animation production which allows animators to design character poses and geometry via intuitive parameters and interfaces. Yet most state-of-the-art computer animation techniques control characters through raw, low level representations such as joint angles, joint positions, or vertex coordinates. This difference often stops the adoption of state-of-the-art techniques in animation production. Our framework solves this issue by learning a mapping between the low level representations of the pose and the animation rig. We use nonlinear regression techniques, learning from example animation sequences designed by the animators. When new motions are provided in the skeleton space, the learned mapping is used to estimate the rig controls that reproduce such a motion. We introduce two nonlinear functions for producing such a mapping: Gaussian process regression and feedforward neural networks. The appropriate solution depends on the nature of the rig and the amount of data available for training. We show our framework applied to various examples including articulated biped characters, quadruped characters, facial animation rigs, and deformable characters. With our system, animators have the freedom to apply any motion synthesis algorithm to arbitrary rigging and animation pipelines for immediate editing. This greatly improves the productivity of 3D animation, while retaining the flexibility and creativity of artistic input.

  10. Predicting Positive Education Outcomes for Emerging Adults in Mental Health Systems of Care.

    Science.gov (United States)

    Brennan, Eileen M; Nygren, Peggy; Stephens, Robert L; Croskey, Adrienne

    2016-10-01

    Emerging adults who receive services based on positive youth development models have shown an ability to shape their own life course to achieve positive goals. This paper reports secondary data analysis from the Longitudinal Child and Family Outcome Study including 248 culturally diverse youth ages 17 through 22 receiving mental health services in systems of care. After 12 months of services, school performance was positively related to youth ratings of school functioning and service participation and satisfaction. Regression analysis revealed ratings of young peoples' perceptions of school functioning, and their experience in services added to the significant prediction of satisfactory school performance, even controlling for sex and attendance. Finally, in addition to expected predictors, participation in planning their own services significantly predicted enrollment in higher education for those who finished high school. Findings suggest that programs and practices based on positive youth development approaches can improve educational outcomes for emerging adults.

  11. Estimation of Stature from Footprint Anthropometry Using Regression Analysis: A Study on the Bidayuh Population of East Malaysia

    Directory of Open Access Journals (Sweden)

    T. Nataraja Moorthy

    2015-05-01

    Full Text Available The human foot has been studied for a variety of reasons, i.e., for forensic as well as non-forensic purposes by anatomists, forensic scientists, anthropologists, physicians, podiatrists, and numerous other groups. An aspect of human identification that has received scant attention from forensic anthropologists is the study of human feet and the footprints made by the feet. The present study, conducted during 2013-2014, aimed to derive population specific regression equations to estimate stature from the footprint anthropometry of indigenous adult Bidayuhs in the east of Malaysia. The study sample consisted of 480 bilateral footprints collected using a footprint kit from 240 Bidayuhs (120 males and 120 females, who consented to taking part in the study. Their ages ranged from 18 to 70 years. Stature was measured using a portable body meter device (SECA model 206. The data were analyzed using PASW Statistics version 20. In this investigation, better results were obtained in terms of correlation coefficient (R between stature and various footprint measurements and regression analysis in estimating the stature. The (R values showed a positive and statistically significant (p < 0.001 relationship between the two parameters. The correlation coefficients in the pooled sample (0.861–0.882 were comparatively higher than those of an individual male (0.762-0.795 and female (0.722-0.765. This study provided regression equations to estimate stature from footprints in the Bidayuh population. The result showed that the regression equations without sex indicators performed significantly better than models with gender indications. The regression equations derived for a pooled sample can be used to estimate stature, even when the sex of the footprint is unknown, as in real crime scenes.

  12. Geodesic least squares regression for scaling studies in magnetic confinement fusion

    International Nuclear Information System (INIS)

    Verdoolaege, Geert

    2015-01-01

    In regression analyses for deriving scaling laws that occur in various scientific disciplines, usually standard regression methods have been applied, of which ordinary least squares (OLS) is the most popular. However, concerns have been raised with respect to several assumptions underlying OLS in its application to scaling laws. We here discuss a new regression method that is robust in the presence of significant uncertainty on both the data and the regression model. The method, which we call geodesic least squares regression (GLS), is based on minimization of the Rao geodesic distance on a probabilistic manifold. We demonstrate the superiority of the method using synthetic data and we present an application to the scaling law for the power threshold for the transition to the high confinement regime in magnetic confinement fusion devices

  13. Modeling Personalized Email Prioritization: Classification-based and Regression-based Approaches

    Energy Technology Data Exchange (ETDEWEB)

    Yoo S.; Yang, Y.; Carbonell, J.

    2011-10-24

    Email overload, even after spam filtering, presents a serious productivity challenge for busy professionals and executives. One solution is automated prioritization of incoming emails to ensure the most important are read and processed quickly, while others are processed later as/if time permits in declining priority levels. This paper presents a study of machine learning approaches to email prioritization into discrete levels, comparing ordinal regression versus classier cascades. Given the ordinal nature of discrete email priority levels, SVM ordinal regression would be expected to perform well, but surprisingly a cascade of SVM classifiers significantly outperforms ordinal regression for email prioritization. In contrast, SVM regression performs well -- better than classifiers -- on selected UCI data sets. This unexpected performance inversion is analyzed and results are presented, providing core functionality for email prioritization systems.

  14. Regression and regression analysis time series prediction modeling on climate data of quetta, pakistan

    International Nuclear Information System (INIS)

    Jafri, Y.Z.; Kamal, L.

    2007-01-01

    Various statistical techniques was used on five-year data from 1998-2002 of average humidity, rainfall, maximum and minimum temperatures, respectively. The relationships to regression analysis time series (RATS) were developed for determining the overall trend of these climate parameters on the basis of which forecast models can be corrected and modified. We computed the coefficient of determination as a measure of goodness of fit, to our polynomial regression analysis time series (PRATS). The correlation to multiple linear regression (MLR) and multiple linear regression analysis time series (MLRATS) were also developed for deciphering the interdependence of weather parameters. Spearman's rand correlation and Goldfeld-Quandt test were used to check the uniformity or non-uniformity of variances in our fit to polynomial regression (PR). The Breusch-Pagan test was applied to MLR and MLRATS, respectively which yielded homoscedasticity. We also employed Bartlett's test for homogeneity of variances on a five-year data of rainfall and humidity, respectively which showed that the variances in rainfall data were not homogenous while in case of humidity, were homogenous. Our results on regression and regression analysis time series show the best fit to prediction modeling on climatic data of Quetta, Pakistan. (author)

  15. Logic regression and its extensions.

    Science.gov (United States)

    Schwender, Holger; Ruczinski, Ingo

    2010-01-01

    Logic regression is an adaptive classification and regression procedure, initially developed to reveal interacting single nucleotide polymorphisms (SNPs) in genetic association studies. In general, this approach can be used in any setting with binary predictors, when the interaction of these covariates is of primary interest. Logic regression searches for Boolean (logic) combinations of binary variables that best explain the variability in the outcome variable, and thus, reveals variables and interactions that are associated with the response and/or have predictive capabilities. The logic expressions are embedded in a generalized linear regression framework, and thus, logic regression can handle a variety of outcome types, such as binary responses in case-control studies, numeric responses, and time-to-event data. In this chapter, we provide an introduction to the logic regression methodology, list some applications in public health and medicine, and summarize some of the direct extensions and modifications of logic regression that have been proposed in the literature. Copyright © 2010 Elsevier Inc. All rights reserved.

  16. Tumor regression patterns in retinoblastoma

    International Nuclear Information System (INIS)

    Zafar, S.N.; Siddique, S.N.; Zaheer, N.

    2016-01-01

    To observe the types of tumor regression after treatment, and identify the common pattern of regression in our patients. Study Design: Descriptive study. Place and Duration of Study: Department of Pediatric Ophthalmology and Strabismus, Al-Shifa Trust Eye Hospital, Rawalpindi, Pakistan, from October 2011 to October 2014. Methodology: Children with unilateral and bilateral retinoblastoma were included in the study. Patients were referred to Pakistan Institute of Medical Sciences, Islamabad, for chemotherapy. After every cycle of chemotherapy, dilated funds examination under anesthesia was performed to record response of the treatment. Regression patterns were recorded on RetCam II. Results: Seventy-four tumors were included in the study. Out of 74 tumors, 3 were ICRB group A tumors, 43 were ICRB group B tumors, 14 tumors belonged to ICRB group C, and remaining 14 were ICRB group D tumors. Type IV regression was seen in 39.1% (n=29) tumors, type II in 29.7% (n=22), type III in 25.6% (n=19), and type I in 5.4% (n=4). All group A tumors (100%) showed type IV regression. Seventeen (39.5%) group B tumors showed type IV regression. In group C, 5 tumors (35.7%) showed type II regression and 5 tumors (35.7%) showed type IV regression. In group D, 6 tumors (42.9%) regressed to type II non-calcified remnants. Conclusion: The response and success of the focal and systemic treatment, as judged by the appearance of different patterns of tumor regression, varies with the ICRB grouping of the tumor. (author)

  17. Combining Alphas via Bounded Regression

    Directory of Open Access Journals (Sweden)

    Zura Kakushadze

    2015-11-01

    Full Text Available We give an explicit algorithm and source code for combining alpha streams via bounded regression. In practical applications, typically, there is insufficient history to compute a sample covariance matrix (SCM for a large number of alphas. To compute alpha allocation weights, one then resorts to (weighted regression over SCM principal components. Regression often produces alpha weights with insufficient diversification and/or skewed distribution against, e.g., turnover. This can be rectified by imposing bounds on alpha weights within the regression procedure. Bounded regression can also be applied to stock and other asset portfolio construction. We discuss illustrative examples.

  18. Dirichlet Component Regression and its Applications to Psychiatric Data

    OpenAIRE

    Gueorguieva, Ralitza; Rosenheck, Robert; Zelterman, Daniel

    2008-01-01

    We describe a Dirichlet multivariable regression method useful for modeling data representing components as a percentage of a total. This model is motivated by the unmet need in psychiatry and other areas to simultaneously assess the effects of covariates on the relative contributions of different components of a measure. The model is illustrated using the Positive and Negative Syndrome Scale (PANSS) for assessment of schizophrenia symptoms which, like many other metrics in psychiatry, is com...

  19. Technological progress and regress in pre-industrial times

    DEFF Research Database (Denmark)

    Aiyar, Shekhar; Dalgaard, Carl-Johan Lars; Moav, Omer

    2008-01-01

    This paper offers micro-foundations for the dynamic relationship between technology and population in the pre-industrial world, accounting for both technological progress and the hitherto neglected but common phenomenon of technological regress. A positive feedback between population and the adop....... Inventions don't just get adopted once and forever; they have to be constantly practised and transmitted, or useful techniques may be forgotten. Jared Diamond, Ten Thousand Years of Solitude, 1993...

  20. Optimizing Prophylactic CPAP in Patients Without Obstructive Sleep Apnoea for High-Risk Abdominal Surgeries: A Meta-regression Analysis.

    Science.gov (United States)

    Singh, Preet Mohinder; Borle, Anuradha; Shah, Dipal; Sinha, Ashish; Makkar, Jeetinder Kaur; Trikha, Anjan; Goudra, Basavana Gouda

    2016-04-01

    Prophylactic continuous positive airway pressure (CPAP) can prevent pulmonary adverse events following upper abdominal surgeries. The present meta-regression evaluates and quantifies the effect of degree/duration of (CPAP) on the incidence of postoperative pulmonary events. Medical databases were searched for randomized controlled trials involving adult patients, comparing the outcome in those receiving prophylactic postoperative CPAP versus no CPAP, undergoing high-risk abdominal surgeries. Our meta-analysis evaluated the relationship between the postoperative pulmonary complications and the use of CPAP. Furthermore, meta-regression was used to quantify the effect of cumulative duration and degree of CPAP on the measured outcomes. Seventy-three potentially relevant studies were identified, of which 11 had appropriate data, allowing us to compare a total of 362 and 363 patients in CPAP and control groups, respectively. Qualitatively, Odds ratio for CPAP showed protective effect for pneumonia [0.39 (0.19-0.78)], atelectasis [0.51 (0.32-0.80)] and pulmonary complications [0.37 (0.24-0.56)] with zero heterogeneity. For prevention of pulmonary complications, odds ratio was better for continuous than intermittent CPAP. Meta-regression demonstrated a positive correlation between the degree of CPAP and the incidence of pneumonia with a regression coefficient of +0.61 (95 % CI 0.02-1.21, P = 0.048, τ (2) = 0.078, r (2) = 7.87 %). Overall, adverse effects were similar with or without the use of CPAP. Prophylactic postoperative use of continuous CPAP significantly reduces the incidence of postoperative pneumonia, atelectasis and pulmonary complications in patients undergoing high-risk abdominal surgeries. Quantitatively, increasing the CPAP levels does not necessarily enhance the protective effect against pneumonia. Instead, protective effect diminishes with increasing degree of CPAP.

  1. riskRegression

    DEFF Research Database (Denmark)

    Ozenne, Brice; Sørensen, Anne Lyngholm; Scheike, Thomas

    2017-01-01

    In the presence of competing risks a prediction of the time-dynamic absolute risk of an event can be based on cause-specific Cox regression models for the event and the competing risks (Benichou and Gail, 1990). We present computationally fast and memory optimized C++ functions with an R interface...... for predicting the covariate specific absolute risks, their confidence intervals, and their confidence bands based on right censored time to event data. We provide explicit formulas for our implementation of the estimator of the (stratified) baseline hazard function in the presence of tied event times. As a by...... functionals. The software presented here is implemented in the riskRegression package....

  2. Binary logistic regression-Instrument for assessing museum indoor air impact on exhibits.

    Science.gov (United States)

    Bucur, Elena; Danet, Andrei Florin; Lehr, Carol Blaziu; Lehr, Elena; Nita-Lazar, Mihai

    2017-04-01

    This paper presents a new way to assess the environmental impact on historical artifacts using binary logistic regression. The prediction of the impact on the exhibits during certain pollution scenarios (environmental impact) was calculated by a mathematical model based on the binary logistic regression; it allows the identification of those environmental parameters from a multitude of possible parameters with a significant impact on exhibitions and ranks them according to their severity effect. Air quality (NO 2 , SO 2 , O 3 and PM 2.5 ) and microclimate parameters (temperature, humidity) monitoring data from a case study conducted within exhibition and storage spaces of the Romanian National Aviation Museum Bucharest have been used for developing and validating the binary logistic regression method and the mathematical model. The logistic regression analysis was used on 794 data combinations (715 to develop of the model and 79 to validate it) by a Statistical Package for Social Sciences (SPSS 20.0). The results from the binary logistic regression analysis demonstrated that from six parameters taken into consideration, four of them present a significant effect upon exhibits in the following order: O 3 >PM 2.5 >NO 2 >humidity followed at a significant distance by the effects of SO 2 and temperature. The mathematical model, developed in this study, correctly predicted 95.1 % of the cumulated effect of the environmental parameters upon the exhibits. Moreover, this model could also be used in the decisional process regarding the preventive preservation measures that should be implemented within the exhibition space. The paper presents a new way to assess the environmental impact on historical artifacts using binary logistic regression. The mathematical model developed on the environmental parameters analyzed by the binary logistic regression method could be useful in a decision-making process establishing the best measures for pollution reduction and preventive

  3. Spatial stochastic regression modelling of urban land use

    International Nuclear Information System (INIS)

    Arshad, S H M; Jaafar, J; Abiden, M Z Z; Latif, Z A; Rasam, A R A

    2014-01-01

    Urbanization is very closely linked to industrialization, commercialization or overall economic growth and development. This results in innumerable benefits of the quantity and quality of the urban environment and lifestyle but on the other hand contributes to unbounded development, urban sprawl, overcrowding and decreasing standard of living. Regulation and observation of urban development activities is crucial. The understanding of urban systems that promotes urban growth are also essential for the purpose of policy making, formulating development strategies as well as development plan preparation. This study aims to compare two different stochastic regression modeling techniques for spatial structure models of urban growth in the same specific study area. Both techniques will utilize the same datasets and their results will be analyzed. The work starts by producing an urban growth model by using stochastic regression modeling techniques namely the Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR). The two techniques are compared to and it is found that, GWR seems to be a more significant stochastic regression model compared to OLS, it gives a smaller AICc (Akaike's Information Corrected Criterion) value and its output is more spatially explainable

  4. REGSTEP - stepwise multivariate polynomial regression with singular extensions

    International Nuclear Information System (INIS)

    Davierwalla, D.M.

    1977-09-01

    The program REGSTEP determines a polynomial approximation, in the least squares sense, to tabulated data. The polynomial may be univariate or multivariate. The computational method is that of stepwise regression. A variable is inserted into the regression basis if it is significant with respect to an appropriate F-test at a preselected risk level. In addition, should a variable already in the basis, become nonsignificant (again with respect to an appropriate F-test) after the entry of a new variable, it is expelled from the model. Thus only significant variables are retained in the model. Although written expressly to be incorporated into CORCOD, a code for predicting nuclear cross sections for given values of power, temperature, void fractions, Boron content etc. there is nothing to limit the use of REGSTEP to nuclear applications, as the examples demonstrate. A separate version has been incorporated into RSYST for the general user. (Auth.)

  5. Prevention measures for avoiding unexpected drifting of marine component in recovery equipment of significant metals from sea water. Positioning and monitoring system for marine component and improvement of its positioning accuracy

    Energy Technology Data Exchange (ETDEWEB)

    Tamada, Masao; Kasai, Noboru; Seko, Noriaki; Hasegawa, Shin; Takeda, Hayato; Katakai, Akio; Sugo, Takanobu [Japan Atomic Energy Research Inst., Takasaki, Gunma (Japan). Takasaki Radiation Chemistry Research Establishment; Kawabata, Yukiya [Ebara Reseach Co., Ltd., Fujisawa, Kanagawa (Japan); Onuma, Kenji [Mitsubishi Materials Corp., Tokyo (Japan)

    2001-11-01

    Positioning and monitoring system for marine component in recovery equipment of significant metals from seawater with adsorbent was designed and assembled to avoid unexpected drifting accident. This system which was set on float part of the marine component obtains the positioning data from GPS satellites and sends them to Takasaki and Mutsu establishments through satellite communication. In both establishments, the position data were shown in computer displays. As characteristic test for 20 days in the real sea, 262 data were obtained every 2 hours. The twice of the distance root mean square (2DRMS) was 223.7 m. To improve this performance, three new functions were added to the present firmware. There are to raise positioning resolutions in longitude and latitude from 0.001 to 0.00001 degree, to remove the reflection of GPS signal from sea surface, and to average remaining three positioning data after maximum and minimum data were omitted from continuous five positioning data. The improved system shows the 2DRMS positioning of 15.5 m. This performance is enough to prevent marine component from its drifting accident. (author)

  6. Understanding logistic regression analysis

    OpenAIRE

    Sperandei, Sandro

    2014-01-01

    Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using ex...

  7. Positioning patient-perceived medical services to develop a marketing strategy.

    Science.gov (United States)

    Jung, Minsoo; Hong, Myung-Sun

    2012-01-01

    In today's medical market, marketing philosophy is being rapidly transformed from customer searching to patient satisfaction and service improvement. The principal objective of this study was to contribute to the establishment of a desirable medical marketing strategy, through the factors of customer satisfaction and the positioning of patients' perceptions by marketing institutions. The data were collected from 282 students of the College of Public Health and Medicine in Seoul. The survey tools were developed using the SERVQUAL scale. Analysis in this study involved both statistical and network analysis. The former was used to verify the determinants of service satisfaction as perceived by respondents, via factor analysis and multiple regression analysis. The latter was obtained using a positioning map and 2-mode network analysis with the matrix data converted from raw data. The determining factors for patient satisfaction were identified as facilities, accessibility, process, physicians, and medical staff. The regression equation was significant (R = 0.606), and the most influential variable was the service quality of physicians (β = .569). According to multidimensional scaling, the positioning of medical institutions indicated that patients' perceptions were affected by hospital size and specialization. By recognizing and managing patient satisfaction, medical institutions are able to foster customer loyalty and, in turn, to enhance service quality. It is necessary to develop an adequate marketing mix to provide better medical services and to overcome medical competition among institutions.

  8. An Excel Solver Exercise to Introduce Nonlinear Regression

    Science.gov (United States)

    Pinder, Jonathan P.

    2013-01-01

    Business students taking business analytics courses that have significant predictive modeling components, such as marketing research, data mining, forecasting, and advanced financial modeling, are introduced to nonlinear regression using application software that is a "black box" to the students. Thus, although correct models are…

  9. Linear regression in astronomy. II

    Science.gov (United States)

    Feigelson, Eric D.; Babu, Gutti J.

    1992-01-01

    A wide variety of least-squares linear regression procedures used in observational astronomy, particularly investigations of the cosmic distance scale, are presented and discussed. The classes of linear models considered are (1) unweighted regression lines, with bootstrap and jackknife resampling; (2) regression solutions when measurement error, in one or both variables, dominates the scatter; (3) methods to apply a calibration line to new data; (4) truncated regression models, which apply to flux-limited data sets; and (5) censored regression models, which apply when nondetections are present. For the calibration problem we develop two new procedures: a formula for the intercept offset between two parallel data sets, which propagates slope errors from one regression to the other; and a generalization of the Working-Hotelling confidence bands to nonstandard least-squares lines. They can provide improved error analysis for Faber-Jackson, Tully-Fisher, and similar cosmic distance scale relations.

  10. Left ventricular orientation and position in an advanced heart failure population

    Directory of Open Access Journals (Sweden)

    Antonia E. Curtin

    2017-06-01

    Full Text Available Introduction: Previous canine and in silico studies indicate that left ventricular (LV orientation and position have clinically significant effects on standard ECG elements, which are particularly relevant in an advanced heart failure (HF population. Our objectives were to investigate the real-world implications of these previous results by describing for the first time the range of LV orientations and positions in HF patients, identifying clinical predictors of orientation and position, and investigating how thoracic geometry may affect orientation and position. Materials and methods: Cardiac MRIs were used to measure LV orientation angles, LV position, chest dimensions, and the ratio of LV volume to thoracic area (LVTR. Multivariate regression analyses were used to identify significant predictors of orientation and position. Results: The mean frontal plane LV orientation angle was 31 ± 11° (range, 0°–47° and fell within the ranges used in previous studies of orientation effects. Orientation in the transverse plane, the effects of which have not been simulated, averaged 48 ± 10° (range, 21°–71°. The ranges of LV positions in the frontal and transverse planes (7.9 and 5.6 cm, respectively are similar to or greater than those used in silico. Orientation and position were weakly correlated with multiple significant predictors, and the relationship between HF progression and LV orientation and position could not be determined. Conclusion: Variation in LV orientation and position in advanced HF patients is large and cannot be readily predicted using the standard clinical variables or additional thoracic geometry measures used in this study. These findings may have significant clinical implications because of the possible effects of orientation and position on key ECG features. New tools and additional studies are needed before LV orientation or position data can be incorporated into clinical ECG interpretation. Keywords: Heart

  11. A flexible fuzzy regression algorithm for forecasting oil consumption estimation

    International Nuclear Information System (INIS)

    Azadeh, A.; Khakestani, M.; Saberi, M.

    2009-01-01

    Oil consumption plays a vital role in socio-economic development of most countries. This study presents a flexible fuzzy regression algorithm for forecasting oil consumption based on standard economic indicators. The standard indicators are annual population, cost of crude oil import, gross domestic production (GDP) and annual oil production in the last period. The proposed algorithm uses analysis of variance (ANOVA) to select either fuzzy regression or conventional regression for future demand estimation. The significance of the proposed algorithm is three fold. First, it is flexible and identifies the best model based on the results of ANOVA and minimum absolute percentage error (MAPE), whereas previous studies consider the best fitted fuzzy regression model based on MAPE or other relative error results. Second, the proposed model may identify conventional regression as the best model for future oil consumption forecasting because of its dynamic structure, whereas previous studies assume that fuzzy regression always provide the best solutions and estimation. Third, it utilizes the most standard independent variables for the regression models. To show the applicability and superiority of the proposed flexible fuzzy regression algorithm the data for oil consumption in Canada, United States, Japan and Australia from 1990 to 2005 are used. The results show that the flexible algorithm provides accurate solution for oil consumption estimation problem. The algorithm may be used by policy makers to accurately foresee the behavior of oil consumption in various regions.

  12. Anti-correlated networks, global signal regression, and the effects of caffeine in resting-state functional MRI.

    Science.gov (United States)

    Wong, Chi Wah; Olafsson, Valur; Tal, Omer; Liu, Thomas T

    2012-10-15

    Resting-state functional connectivity magnetic resonance imaging is proving to be an essential tool for the characterization of functional networks in the brain. Two of the major networks that have been identified are the default mode network (DMN) and the task positive network (TPN). Although prior work indicates that these two networks are anti-correlated, the findings are controversial because the anti-correlations are often found only after the application of a pre-processing step, known as global signal regression, that can produce artifactual anti-correlations. In this paper, we show that, for subjects studied in an eyes-closed rest state, caffeine can significantly enhance the detection of anti-correlations between the DMN and TPN without the need for global signal regression. In line with these findings, we find that caffeine also leads to widespread decreases in connectivity and global signal amplitude. Using a recently introduced geometric model of global signal effects, we demonstrate that these decreases are consistent with the removal of an additive global signal confound. In contrast to the effects observed in the eyes-closed rest state, caffeine did not lead to significant changes in global functional connectivity in the eyes-open rest state. Copyright © 2012 Elsevier Inc. All rights reserved.

  13. Can slide positivity rates predict malaria transmission?

    Directory of Open Access Journals (Sweden)

    Bi Yan

    2012-04-01

    Full Text Available Abstract Background Malaria is a significant threat to population health in the border areas of Yunnan Province, China. How to accurately measure malaria transmission is an important issue. This study aimed to examine the role of slide positivity rates (SPR in malaria transmission in Mengla County, Yunnan Province, China. Methods Data on annual malaria cases, SPR and socio-economic factors for the period of 1993 to 2008 were obtained from the Center for Disease Control and Prevention (CDC and the Bureau of Statistics, Mengla, China. Multiple linear regression models were conducted to evaluate the relationship between socio-ecologic factors and malaria incidence. Results The results show that SPR was significantly positively associated with the malaria incidence rates. The SPR (β = 1.244, p = 0.000 alone and combination (SPR, β = 1.326, p  Conclusion SPR is a strong predictor of malaria transmission, and can be used to improve the planning and implementation of malaria elimination programmes in Mengla and other similar locations. SPR might also be a useful indicator of malaria early warning systems in China.

  14. Factors affecting CO_2 emissions in China’s agriculture sector: Evidence from geographically weighted regression model

    International Nuclear Information System (INIS)

    Xu, Bin; Lin, Boqiang

    2017-01-01

    China is currently the world's largest emitter of carbon dioxide. Considered as a large agricultural country, carbon emission in China’s agriculture sector keeps on growing rapidly. It is, therefore, of great importance to investigate the driving forces of carbon dioxide emissions in this sector. The traditional regression estimation can only get “average” and “global” parameter estimates; it excludes the “local” parameter estimates which vary across space in some spatial systems. Geographically weighted regression embeds the latitude and longitude of the sample data into the regression parameters, and uses the local weighted least squares method to estimate the parameters point–by–point. To reveal the nonstationary spatial effects of driving forces, geographically weighted regression model is employed in this paper. The results show that economic growth is positively correlated with emissions, with the impact in the western region being less than that in the central and eastern regions. Urbanization is positively related to emissions but produces opposite effects pattern. Energy intensity is also correlated with emissions, with a decreasing trend from the eastern region to the central and western regions. Therefore, policymakers should take full account of the spatial nonstationarity of driving forces in designing emission reduction policies. - Highlights: • We explore the driving forces of CO_2 emissions in the agriculture sector. • Urbanization is positively related to emissions but produces opposite effect pattern. • The effect of energy intensity declines from the eastern region to western region.

  15. Predicting risk for portal vein thrombosis in acute pancreatitis patients: A comparison of radical basis function artificial neural network and logistic regression models.

    Science.gov (United States)

    Fei, Yang; Hu, Jian; Gao, Kun; Tu, Jianfeng; Li, Wei-Qin; Wang, Wei

    2017-06-01

    To construct a radical basis function (RBF) artificial neural networks (ANNs) model to predict the incidence of acute pancreatitis (AP)-induced portal vein thrombosis. The analysis included 353 patients with AP who had admitted between January 2011 and December 2015. RBF ANNs model and logistic regression model were constructed based on eleven factors relevant to AP respectively. Statistical indexes were used to evaluate the value of the prediction in two models. The predict sensitivity, specificity, positive predictive value, negative predictive value and accuracy by RBF ANNs model for PVT were 73.3%, 91.4%, 68.8%, 93.0% and 87.7%, respectively. There were significant differences between the RBF ANNs and logistic regression models in these parameters (Plogistic regression model. D-dimer, AMY, Hct and PT were important prediction factors of approval for AP-induced PVT. Copyright © 2017 Elsevier Inc. All rights reserved.

  16. Bayesian median regression for temporal gene expression data

    Science.gov (United States)

    Yu, Keming; Vinciotti, Veronica; Liu, Xiaohui; 't Hoen, Peter A. C.

    2007-09-01

    Most of the existing methods for the identification of biologically interesting genes in a temporal expression profiling dataset do not fully exploit the temporal ordering in the dataset and are based on normality assumptions for the gene expression. In this paper, we introduce a Bayesian median regression model to detect genes whose temporal profile is significantly different across a number of biological conditions. The regression model is defined by a polynomial function where both time and condition effects as well as interactions between the two are included. MCMC-based inference returns the posterior distribution of the polynomial coefficients. From this a simple Bayes factor test is proposed to test for significance. The estimation of the median rather than the mean, and within a Bayesian framework, increases the robustness of the method compared to a Hotelling T2-test previously suggested. This is shown on simulated data and on muscular dystrophy gene expression data.

  17. Quantile regression theory and applications

    CERN Document Server

    Davino, Cristina; Vistocco, Domenico

    2013-01-01

    A guide to the implementation and interpretation of Quantile Regression models This book explores the theory and numerous applications of quantile regression, offering empirical data analysis as well as the software tools to implement the methods. The main focus of this book is to provide the reader with a comprehensivedescription of the main issues concerning quantile regression; these include basic modeling, geometrical interpretation, estimation and inference for quantile regression, as well as issues on validity of the model, diagnostic tools. Each methodological aspect is explored and

  18. Fungible weights in logistic regression.

    Science.gov (United States)

    Jones, Jeff A; Waller, Niels G

    2016-06-01

    In this article we develop methods for assessing parameter sensitivity in logistic regression models. To set the stage for this work, we first review Waller's (2008) equations for computing fungible weights in linear regression. Next, we describe 2 methods for computing fungible weights in logistic regression. To demonstrate the utility of these methods, we compute fungible logistic regression weights using data from the Centers for Disease Control and Prevention's (2010) Youth Risk Behavior Surveillance Survey, and we illustrate how these alternate weights can be used to evaluate parameter sensitivity. To make our work accessible to the research community, we provide R code (R Core Team, 2015) that will generate both kinds of fungible logistic regression weights. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  19. Significance of Waterway Navigation Positioning Systems On Ship's Manoeuvring Safety

    Science.gov (United States)

    Galor, W.

    The main goal of navigation is to lead the ship to the point of destination safety and efficiently. Various factors may affect ship realisating this process. The ship movement on waterway are mainly limited by water area dimensions (surface and depth). These limitations cause the requirement to realise the proper of ship movement trajectory. In case when this re requirement cant't fulfil then marine accident may happend. This fact is unwanted event caused losses of human health and life, damage or loss of cargo and ship, pollution of natural environment, damage of port structures or blocking the port of its ports and lost of salvage operation. These losses in same cases can be catas- trophical especially while e.i. crude oil spilling could be place. To realise of safety navigation process is needed to embrace the ship's movement trajectory by waterways area. The ship's trajectory is described by manoeuvring lane as a surface of water area which is require to realise of safety ship movement. Many conditions affect to ship manoeuvring line. The main are following: positioning accuracy, ship's manoeuvring features and phenomena's of shore and ship's bulk common affecting. The accuracy of positioning system is most important. This system depends on coast navigation mark- ing which can range many kinds of technical realisation. Mainly used systems based on lights (line), radionavigation (local system or GPS, DGPS), or radars. If accuracy of positiong is higer, then safety of navigation is growing. This article presents these problems exemplifying with approaching channel to ports situated on West Pomera- nian water region.

  20. Geographically weighted regression model on poverty indicator

    Science.gov (United States)

    Slamet, I.; Nugroho, N. F. T. A.; Muslich

    2017-12-01

    In this research, we applied geographically weighted regression (GWR) for analyzing the poverty in Central Java. We consider Gaussian Kernel as weighted function. The GWR uses the diagonal matrix resulted from calculating kernel Gaussian function as a weighted function in the regression model. The kernel weights is used to handle spatial effects on the data so that a model can be obtained for each location. The purpose of this paper is to model of poverty percentage data in Central Java province using GWR with Gaussian kernel weighted function and to determine the influencing factors in each regency/city in Central Java province. Based on the research, we obtained geographically weighted regression model with Gaussian kernel weighted function on poverty percentage data in Central Java province. We found that percentage of population working as farmers, population growth rate, percentage of households with regular sanitation, and BPJS beneficiaries are the variables that affect the percentage of poverty in Central Java province. In this research, we found the determination coefficient R2 are 68.64%. There are two categories of district which are influenced by different of significance factors.

  1. Satellite rainfall retrieval by logistic regression

    Science.gov (United States)

    Chiu, Long S.

    1986-01-01

    The potential use of logistic regression in rainfall estimation from satellite measurements is investigated. Satellite measurements provide covariate information in terms of radiances from different remote sensors.The logistic regression technique can effectively accommodate many covariates and test their significance in the estimation. The outcome from the logistical model is the probability that the rainrate of a satellite pixel is above a certain threshold. By varying the thresholds, a rainrate histogram can be obtained, from which the mean and the variant can be estimated. A logistical model is developed and applied to rainfall data collected during GATE, using as covariates the fractional rain area and a radiance measurement which is deduced from a microwave temperature-rainrate relation. It is demonstrated that the fractional rain area is an important covariate in the model, consistent with the use of the so-called Area Time Integral in estimating total rain volume in other studies. To calibrate the logistical model, simulated rain fields generated by rainfield models with prescribed parameters are needed. A stringent test of the logistical model is its ability to recover the prescribed parameters of simulated rain fields. A rain field simulation model which preserves the fractional rain area and lognormality of rainrates as found in GATE is developed. A stochastic regression model of branching and immigration whose solutions are lognormally distributed in some asymptotic limits has also been developed.

  2. Prognostic Significance of the Number of Positive Lymph Nodes in Women With T1-2N1 Breast Cancer Treated With Mastectomy: Should Patients With 1, 2, and 3 Positive Lymph Nodes Be Grouped Together?

    Energy Technology Data Exchange (ETDEWEB)

    Dai Kubicky, Charlotte, E-mail: charlottedai@gmail.com [Department of Radiation Medicine and Knight Cancer Institute, Oregon Health and Science University, Portland, Oregon (United States); Mongoue-Tchokote, Solange [Biostatistics Shared Resource, Knight Cancer Institute, Oregon Health and Science University, Portland, Oregon (United States)

    2013-04-01

    Purpose: To determine whether patients with 1, 2, or 3 positive lymph nodes (LNs) have similar survival outcomes. Methods and Materials: We analyzed the Surveillance, Epidemiology, and End Results registry of breast cancer patients diagnosed between 1990 and 2003. We identified 10,415 women with T1-2N1M0 breast cancer who were treated with mastectomy with no adjuvant radiation, with at least 10 LNs examined and 6 months of follow-up. The Kaplan-Meier method and log–rank test were used for survival analysis. Multivariate analysis was performed using the Cox proportional hazard model. Results: Median follow-up was 92 months. Ten-year overall survival (OS) and cause-specific survival (CSS) were progressively worse with increasing number of positive LNs. Survival rates were 70%, 64%, and 60% (OS), and 82%, 76%, and 72% (CSS) for 1, 2, and 3 positive LNs, respectively. Pairwise log–rank test P values were <.001 (1 vs 2 positive LNs), <.001 (1 vs 3 positive LNs), and .002 (2 vs 3 positive LNs). Multivariate analysis showed that number of positive LNs was a significant predictor of OS and CSS. Hazard ratios increased with the number of positive LNs. In addition, age, primary tumor size, grade, estrogen receptor and progesterone receptor status, race, and year of diagnosis were significant prognostic factors. Conclusions: Our study suggests that patients with 1, 2, and 3 positive LNs have distinct survival outcomes, with increasing number of positive LNs associated with worse OS and CSS. The conventional grouping of 1-3 positive LNs needs to be reconsidered.

  3. Using multiple linear regression techniques to quantify carbon ...

    African Journals Online (AJOL)

    Fallow ecosystems provide a significant carbon stock that can be quantified for inclusion in the accounts of global carbon budgets. Process and statistical models of productivity, though useful, are often technically rigid as the conditions for their application are not easy to satisfy. Multiple regression techniques have been ...

  4. A land use regression model for ambient ultrafine particles in Montreal, Canada: A comparison of linear regression and a machine learning approach.

    Science.gov (United States)

    Weichenthal, Scott; Ryswyk, Keith Van; Goldstein, Alon; Bagg, Scott; Shekkarizfard, Maryam; Hatzopoulou, Marianne

    2016-04-01

    Existing evidence suggests that ambient ultrafine particles (UFPs) (regression model for UFPs in Montreal, Canada using mobile monitoring data collected from 414 road segments during the summer and winter months between 2011 and 2012. Two different approaches were examined for model development including standard multivariable linear regression and a machine learning approach (kernel-based regularized least squares (KRLS)) that learns the functional form of covariate impacts on ambient UFP concentrations from the data. The final models included parameters for population density, ambient temperature and wind speed, land use parameters (park space and open space), length of local roads and rail, and estimated annual average NOx emissions from traffic. The final multivariable linear regression model explained 62% of the spatial variation in ambient UFP concentrations whereas the KRLS model explained 79% of the variance. The KRLS model performed slightly better than the linear regression model when evaluated using an external dataset (R(2)=0.58 vs. 0.55) or a cross-validation procedure (R(2)=0.67 vs. 0.60). In general, our findings suggest that the KRLS approach may offer modest improvements in predictive performance compared to standard multivariable linear regression models used to estimate spatial variations in ambient UFPs. However, differences in predictive performance were not statistically significant when evaluated using the cross-validation procedure. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.

  5. Principal component regression analysis with SPSS.

    Science.gov (United States)

    Liu, R X; Kuang, J; Gong, Q; Hou, X L

    2003-06-01

    The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.

  6. Significance and management of positive surgical margins at the time of radical prostatectomy.

    Science.gov (United States)

    Silberstein, Jonathan L; Eastham, James A

    2014-10-01

    Positive surgical margins (PSM) at the time of radical prostatectomy (RP) result in an increased risk of biochemical recurrence (BCR) and secondary treatment. We review current literature with a focus on stratifying the characteristics of the PSM that may define its significance, the impact of modern imaging and surgical approaches in avoidance of PSM, and management strategies when PSM do occur. We performed a review of the available literature to identify factors associated with PSM and their management. PSM have been repeatedly demonstrated to be associated with an increased risk of BCR following RP. The specific characteristics (size, number, location, Gleason score at the margin) of the PSM may influence the risk of recurrence. Novel imaging and surgical approaches are being investigated and may allow for reductions of PSM in the future. The use of adjuvant treatment for a PSM remains controversial and should be decided on an individual basis after a discussion about the risks and benefits. The goal of RP is complete resection of the tumor. PSM are associated with increased risk of BCR and secondary treatments. Of the risk factors associated with BCR after RP, a PSM is directly influenced by surgical technique.

  7. Significance and management of positive surgical margins at the time of radical prostatectomy

    Directory of Open Access Journals (Sweden)

    Jonathan L. Silberstein

    2014-01-01

    Full Text Available Positive surgical margins (PSM at the time of radical prostatectomy (RP result in an increased risk of biochemical recurrence (BCR and secondary treatment. We review current literature with a focus on stratifying the characteristics of the PSM that may define its significance, the impact of modern imaging and surgical approaches in avoidance of PSM, and management strategies when PSM do occur. We performed a review of the available literature to identify factors associated with PSM and their management. PSM have been repeatedly demonstrated to be associated with an increased risk of BCR following RP. The specific characteristics (size, number, location, Gleason score at the margin of the PSM may influence the risk of recurrence. Novel imaging and surgical approaches are being investigated and may allow for reductions of PSM in the future. The use of adjuvant treatment for a PSM remains controversial and should be decided on an individual basis after a discussion about the risks and benefits. The goal of RP is complete resection of the tumor. PSM are associated with increased risk of BCR and secondary treatments. Of the risk factors associated with BCR after RP, a PSM is directly influenced by surgical technique.

  8. Accounting for measurement error in log regression models with applications to accelerated testing.

    Science.gov (United States)

    Richardson, Robert; Tolley, H Dennis; Evenson, William E; Lunt, Barry M

    2018-01-01

    In regression settings, parameter estimates will be biased when the explanatory variables are measured with error. This bias can significantly affect modeling goals. In particular, accelerated lifetime testing involves an extrapolation of the fitted model, and a small amount of bias in parameter estimates may result in a significant increase in the bias of the extrapolated predictions. Additionally, bias may arise when the stochastic component of a log regression model is assumed to be multiplicative when the actual underlying stochastic component is additive. To account for these possible sources of bias, a log regression model with measurement error and additive error is approximated by a weighted regression model which can be estimated using Iteratively Re-weighted Least Squares. Using the reduced Eyring equation in an accelerated testing setting, the model is compared to previously accepted approaches to modeling accelerated testing data with both simulations and real data.

  9. Accounting for measurement error in log regression models with applications to accelerated testing.

    Directory of Open Access Journals (Sweden)

    Robert Richardson

    Full Text Available In regression settings, parameter estimates will be biased when the explanatory variables are measured with error. This bias can significantly affect modeling goals. In particular, accelerated lifetime testing involves an extrapolation of the fitted model, and a small amount of bias in parameter estimates may result in a significant increase in the bias of the extrapolated predictions. Additionally, bias may arise when the stochastic component of a log regression model is assumed to be multiplicative when the actual underlying stochastic component is additive. To account for these possible sources of bias, a log regression model with measurement error and additive error is approximated by a weighted regression model which can be estimated using Iteratively Re-weighted Least Squares. Using the reduced Eyring equation in an accelerated testing setting, the model is compared to previously accepted approaches to modeling accelerated testing data with both simulations and real data.

  10. Efficacy of an opposite position aspiration on resolution of pneumothorax following CT-guided lung biopsy

    Science.gov (United States)

    Zeng, L-C; Du, Y; Yang, H-F; Xie, M-G; Liao, H-Q; Zhang, Y-D; Li, L; Wang, Q; Hu, L

    2015-01-01

    Objective: To evaluate the efficacy of aspiration in an opposite position to deal with pneumothorax after CT-guided lung biopsy. Methods: A retrospective study was developed involving 210 patients with pneumothorax who had undergone CT-guided percutaneous core biopsies from January 2012 to March 2014 for various pulmonary lesions. Asymptomatic patients with minimal pneumothorax were treated conservatively. Simple manual aspiration was performed for symptomatic patients with minimal pneumothorax and for all patients with moderate to large pneumothorax. An opposite position aspiration was performed when simple manual aspiration failed. The efficacy of simple manual aspiration and the opposite position aspiration was observed. Results: Among 210 patients with pneumothorax, 128 (61.0%) asymptomatic patients with minimal pneumothorax were treated conservatively. The remaining 82 were treated with attempted simple manual aspiration. Out of these 82 patients, simple manual aspiration was successful in 58 (70.7%, 58/82) cases. The complete and partial regression rates were 17.2% (10/58) and 82.8% (48/58), respectively. In the other 24 patients (29.3%, 24/82), simple aspiration technique was ineffective. An opposite position (from prone to supine or vice versa) was applied, and a new biopsy puncture site was chosen for reaspiration. This procedure was successful in 22 patients but not in 2 patients who had to have a chest tube insertion. The complete and partial regression rates were 25.0% (6/24) and 66.7% (16/24), respectively. Applying the new method, the total effective rate of aspiration improved significantly from 70.7% (58/82) to 97.6% (80/82). Conclusion: The opposite position aspiration can be safe, effective and minimally invasive treatment for CT-guided lung biopsy-induced pneumothorax thus reducing the use of chest tube significantly. Advances in knowledge: (1) Opposite position aspiration can elevate the success rate of aspiration significantly (from 70.7% to 97

  11. Determinants of LSIL Regression in Women from a Colombian Cohort; Determinantes de la regresion de lesiones cervicales de bajo grado en una cohorte de mujeres colombianas.

    Energy Technology Data Exchange (ETDEWEB)

    Molano, Monica; Gonzalez, Mauricio; Gamboa, Oscar; Ortiz, Natasha; Luna, Joaquin; Hernandez, Gustavo; Posso, Hector; Murillo, Raul; Munoz, Nubia

    2010-07-01

    Objective: To analyze the role of Human Papillomavirus (HPV) and other risk factors in the regression of cervical lesions in women from the Bogota Cohort. Methods: 200 HPV positive women with abnormal cytology were included for regression analysis. The time of lesion regression was modeled using methods for interval censored survival time data. Median duration of total follow-up was 9 years. Results: 80 (40%) women were diagnosed with Atypical Squamous Cells of Undetermined Significance (ASCUS) or Atypical Glandular Cells of Undetermined Significance (AGUS) while 120 (60%) were diagnosed with Low Grade Squamous Intra-epithelial Lesions (LSIL). Globally, 40% of the lesions were still present at first year of follow up, while 1.5% was still present at 5 year check-up. The multivariate model showed similar regression rates for lesions in women with ASCUS/AGUS and women with LSIL (HR= 0.82, 95% CI 0.59-1.12). Women infected with HR HPV types and those with mixed infections had lower regression rates for lesions than did women infected with LR types (HR=0.526, 95% CI 0.33-0.84, for HR types and HR=0.378, 95% CI 0.20-0.69, for mixed infections). Furthermore, women over 30 years had a higher lesion regression rate than did women under 30 years (HR1.53, 95% CI 1.03-2.27). The study showed that the median time for lesion regression was 9 months while the median time for HPV clearance was 12 months. Conclusions: In the studied population, the type of infection and the age of the women are critical factors for the regression of cervical lesions.

  12. Religiosity, health and happiness: significant relations in adolescents from Qatar.

    Science.gov (United States)

    Abdel-Khalek, Ahmed M

    2014-11-01

    Several studies have revealed positive associations between religiosity, health and happiness. However, the vast majority of these studies were carried out on native English-speaking participants. The objective of this study was to estimate the relations between religiosity, health and happiness among a sample (N = 372) of Qatari adolescents (M age = 15.2). The students responded to five self-rating scales to assess religiosity, mental health, physical health, happiness and satisfaction with life. Boys obtained a higher mean score on mental health than did their female counterparts. All the correlations between the rating scales were significant and positive. Principal component analysis disclosed one component and labelled 'Religiosity, health and happiness' in both sexes. The multiple stepwise regression indicated that the predictors of religiosity were the self-ratings of satisfaction with life and happiness in boys, whereas the predictors among girls were satisfaction with life and physical health. On the basis of the responses of the present sample, it was concluded that those who consider themselves as religious were more happy, satisfied with their life and healthy. © The Author(s) 2013.

  13. Extreme Learning Machine and Moving Least Square Regression Based Solar Panel Vision Inspection

    Directory of Open Access Journals (Sweden)

    Heng Liu

    2017-01-01

    Full Text Available In recent years, learning based machine intelligence has aroused a lot of attention across science and engineering. Particularly in the field of automatic industry inspection, the machine learning based vision inspection plays a more and more important role in defect identification and feature extraction. Through learning from image samples, many features of industry objects, such as shapes, positions, and orientations angles, can be obtained and then can be well utilized to determine whether there is defect or not. However, the robustness and the quickness are not easily achieved in such inspection way. In this work, for solar panel vision inspection, we present an extreme learning machine (ELM and moving least square regression based approach to identify solder joint defect and detect the panel position. Firstly, histogram peaks distribution (HPD and fractional calculus are applied for image preprocessing. Then an ELM-based defective solder joints identification is discussed in detail. Finally, moving least square regression (MLSR algorithm is introduced for solar panel position determination. Experimental results and comparisons show that the proposed ELM and MLSR based inspection method is efficient not only in detection accuracy but also in processing speed.

  14. Logistic regression models

    CERN Document Server

    Hilbe, Joseph M

    2009-01-01

    This book really does cover everything you ever wanted to know about logistic regression … with updates available on the author's website. Hilbe, a former national athletics champion, philosopher, and expert in astronomy, is a master at explaining statistical concepts and methods. Readers familiar with his other expository work will know what to expect-great clarity.The book provides considerable detail about all facets of logistic regression. No step of an argument is omitted so that the book will meet the needs of the reader who likes to see everything spelt out, while a person familiar with some of the topics has the option to skip "obvious" sections. The material has been thoroughly road-tested through classroom and web-based teaching. … The focus is on helping the reader to learn and understand logistic regression. The audience is not just students meeting the topic for the first time, but also experienced users. I believe the book really does meet the author's goal … .-Annette J. Dobson, Biometric...

  15. [The effect of prison crowding on prisoners' violence in Japan: testing with cointegration regressions and error correction models].

    Science.gov (United States)

    Yuma, Yoshikazu

    2010-08-01

    This research examined the effect of prison population densities (PPD) on inmate-inmate prison violence rates (PVR) in Japan using one-year-interval time-series data (1972-2006). Cointegration regressions revealed a long-run equilibrium relationship between PPD and PVR. PPD had a significant and increasing effect on PVR in the long-term. Error correction models showed that in the short-term, the effect of PPD was significant and positive on PVR, even after controlling for the effects of the proportions of males, age younger than 30 years, less than one-year incarceration, and prisoner/staff ratio. The results were discussed in regard to (a) differences between Japanese prisons and prisons in the United States, and (b) methodological problems found in previous research.

  16. Logistic regression applied to natural hazards: rare event logistic regression with replications

    Science.gov (United States)

    Guns, M.; Vanacker, V.

    2012-06-01

    Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logistic regression with replications, combines the strength of probabilistic and statistical methods, and allows overcoming some of the limitations of previous developments through robust variable selection. This technique was here developed for the analyses of landslide controlling factors, but the concept is widely applicable for statistical analyses of natural hazards.

  17. Antecedents of positive self-disclosure online: an empirical study of US college students' Facebook usage.

    Science.gov (United States)

    Chen, Hongliang

    2017-01-01

    This study investigates the factors predicting positive self-disclosure on social networking sites (SNSs). There is a formidable body of empirical research relating to online self-disclosure, but very few studies have assessed the antecedents of positive self-disclosure. To address this literature gap, the current study tests the effects of self-esteem, life satisfaction, social anxiety, privacy concerns, public self-consciousness (SC), and perceived collectivism on positive self-disclosure on SNSs. Data were collected online via Qualtrics in April 2013. Respondents were undergraduate students from the University of Connecticut. Using ordinary least squares regression, the current study found that self-esteem and perceived collectivism increased positive self-disclosure, life satisfaction, and privacy concerns decreased positive self-disclosure, and the effects of social anxiety and public SC were not significant.

  18. Understanding logistic regression analysis.

    Science.gov (United States)

    Sperandei, Sandro

    2014-01-01

    Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using examples to make it as simple as possible. After definition of the technique, the basic interpretation of the results is highlighted and then some special issues are discussed.

  19. The Universal Patient Centredness Questionnaire: scaling approaches to reduce positive skew

    Directory of Open Access Journals (Sweden)

    Bjertnaes O

    2016-11-01

    Full Text Available Oyvind Bjertnaes, Hilde Hestad Iversen, Andrew M Garratt Unit for Patient-Reported Quality, Norwegian Institute of Public Health, Oslo, Norway Purpose: Surveys of patients’ experiences typically show results that are indicative of positive experiences. Unbalanced response scales have reduced positive skew for responses to items within the Universal Patient Centeredness Questionnaire (UPC-Q. The objective of this study was to compare the unbalanced response scale with another unbalanced approach to scaling to assess whether the positive skew might be further reduced. Patients and methods: The UPC-Q was included in a patient experience survey conducted at the ward level at six hospitals in Norway in 2015. The postal survey included two reminders to nonrespondents. For patients in the first month of inclusion, UPC-Q items had standard scaling: poor, fairly good, good, very good, and excellent. For patients in the second month, the scaling was more positive: poor, good, very good, exceptionally good, and excellent. The effect of scaling on UPC-Q scores was tested with independent samples t-tests and multilevel linear regression analysis, the latter controlling for the hierarchical structure of data and known predictors of patient-reported experiences. Results: The response rate was 54.6% (n=4,970. Significantly lower scores were found for all items of the more positively worded scale: UPC-Q total score difference was 7.9 (P<0.001, on a scale from 0 to 100 where 100 is the best possible score. Differences between the four items of the UPC-Q ranged from 7.1 (P<0.001 to 10.4 (P<0.001. Multivariate multilevel regression analysis confirmed the difference between the response groups, after controlling for other background variables; UPC-Q total score difference estimate was 8.3 (P<0.001. Conclusion: The more positively worded scaling significantly lowered the mean scores, potentially increasing the sensitivity of the UPC-Q to identify differences over

  20. A regression approach for Zircaloy-2 in-reactor creep constitutive equations

    International Nuclear Information System (INIS)

    Yung Liu, Y.; Bement, A.L.

    1977-01-01

    In this paper the methodology of multiple regressions as applied to Zircaloy-2 in-reactor creep data analysis and construction of constitutive equation are illustrated. While the resulting constitutive equation can be used in creep analysis of in-reactor Zircaloy structural components, the methodology itself is entirely general and can be applied to any creep data analysis. The promising aspects of multiple regression creep data analysis are briefly outlined as follows: (1) When there are more than one variable involved, there is no need to make the assumption that each variable affects the response independently. No separate normalizations are required either and the estimation of parameters is obtained by solving many simultaneous equations. The number of simultaneous equations is equal to the number of data sets. (2) Regression statistics such as R 2 - and F-statistics provide measures of the significance of regression creep equation in correlating the overall data. The relative weights of each variable on the response can also be obtained. (3) Special regression techniques such as step-wise, ridge, and robust regressions and residual plots, etc., provide diagnostic tools for model selections. Multiple regression analysis performed on a set of carefully selected Zircaloy-2 in-reactor creep data leads to a model which provides excellent correlations for the data. (Auth.)

  1. Investing in Global Markets: Big Data and Applications of Robust Regression

    Directory of Open Access Journals (Sweden)

    John eGuerard

    2016-02-01

    Full Text Available In this analysis of the risk and return of stocks in global markets, we apply several applications of robust regression techniques in producing stock selection models and several optimization techniques in portfolio construction in global stock universes. We find that (1 the robust regression applications are appropriate for modeling stock returns in global markets; and (2 mean-variance techniques continue to produce portfolios capable of generating excess returns above transaction costs and statistically significant asset selection. We estimate expected return models in a global equity markets using a given stock selection model and generate statistically significant active returns from various portfolio construction techniques.

  2. Test for positional candidate genes for body composition on pig chromosome 6

    Directory of Open Access Journals (Sweden)

    Pérez-Enciso Miguel

    2002-07-01

    Full Text Available Abstract One QTL affecting backfat thickness (BF, intramuscular fat content (IMF and eye muscle area (MA was previously localized on porcine chromosome 6 in an F2 cross between Iberian and Landrace pigs. This work was done to study the effect of two positional candidate genes on these traits: H-FABP and LEPR genes. The QTL mapping analysis was repeated with a regression method using genotypes for seven microsatellites and two PCR-RFLPs in the H-FABP and LEPR genes. H-FABP and LEPR genes were located at 85.4 and 107 cM respectively, by linkage analysis. The effects of the candidate gene polymorphisms were analyzed in two ways. When an animal model was fitted, both genes showed significant effects on fatness traits, the H-FABP polymorphism showed significant effects on IMF and MA, and the LEPR polymorphism on BF and IMF. But when the candidate gene effect was included in a QTL regression analysis these associations were not observed, suggesting that they must not be the causal mutations responsible for the effects found. Differences in the results of both analyses showed the inadequacy of the animal model approach for the evaluation of positional candidate genes in populations with linkage disequilibrium, when the probabilities of the parental origin of the QTL alleles are not included in the model.

  3. Logistic regression model for diagnosis of transition zone prostate cancer on multi-parametric MRI

    Energy Technology Data Exchange (ETDEWEB)

    Dikaios, Nikolaos; Halligan, Steve; Taylor, Stuart; Atkinson, David; Punwani, Shonit [University College London, Centre for Medical Imaging, London (United Kingdom); University College London Hospital, Departments of Radiology, London (United Kingdom); Alkalbani, Jokha; Sidhu, Harbir Singh; Fujiwara, Taiki [University College London, Centre for Medical Imaging, London (United Kingdom); Abd-Alazeez, Mohamed; Ahmed, Hashim; Emberton, Mark [University College London, Research Department of Urology, London (United Kingdom); Kirkham, Alex; Allen, Clare [University College London Hospital, Departments of Radiology, London (United Kingdom); Freeman, Alex [University College London Hospital, Department of Histopathology, London (United Kingdom)

    2014-09-17

    We aimed to develop logistic regression (LR) models for classifying prostate cancer within the transition zone on multi-parametric magnetic resonance imaging (mp-MRI). One hundred and fifty-five patients (training cohort, 70 patients; temporal validation cohort, 85 patients) underwent mp-MRI and transperineal-template-prostate-mapping (TPM) biopsy. Positive cores were classified by cancer definitions: (1) any-cancer; (2) definition-1 [≥Gleason 4 + 3 or ≥ 6 mm cancer core length (CCL)] [high risk significant]; and (3) definition-2 (≥Gleason 3 + 4 or ≥ 4 mm CCL) cancer [intermediate-high risk significant]. For each, logistic-regression mp-MRI models were derived from the training cohort and validated internally and with the temporal cohort. Sensitivity/specificity and the area under the receiver operating characteristic (ROC-AUC) curve were calculated. LR model performance was compared to radiologists' performance. Twenty-eight of 70 patients from the training cohort, and 25/85 patients from the temporal validation cohort had significant cancer on TPM. The ROC-AUC of the LR model for classification of cancer was 0.73/0.67 at internal/temporal validation. The radiologist A/B ROC-AUC was 0.65/0.74 (temporal cohort). For patients scored by radiologists as Prostate Imaging Reporting and Data System (Pi-RADS) score 3, sensitivity/specificity of radiologist A 'best guess' and LR model was 0.14/0.54 and 0.71/0.61, respectively; and radiologist B 'best guess' and LR model was 0.40/0.34 and 0.50/0.76, respectively. LR models can improve classification of Pi-RADS score 3 lesions similar to experienced radiologists. (orig.)

  4. Logistic regression model for diagnosis of transition zone prostate cancer on multi-parametric MRI

    International Nuclear Information System (INIS)

    Dikaios, Nikolaos; Halligan, Steve; Taylor, Stuart; Atkinson, David; Punwani, Shonit; Alkalbani, Jokha; Sidhu, Harbir Singh; Fujiwara, Taiki; Abd-Alazeez, Mohamed; Ahmed, Hashim; Emberton, Mark; Kirkham, Alex; Allen, Clare; Freeman, Alex

    2015-01-01

    We aimed to develop logistic regression (LR) models for classifying prostate cancer within the transition zone on multi-parametric magnetic resonance imaging (mp-MRI). One hundred and fifty-five patients (training cohort, 70 patients; temporal validation cohort, 85 patients) underwent mp-MRI and transperineal-template-prostate-mapping (TPM) biopsy. Positive cores were classified by cancer definitions: (1) any-cancer; (2) definition-1 [≥Gleason 4 + 3 or ≥ 6 mm cancer core length (CCL)] [high risk significant]; and (3) definition-2 (≥Gleason 3 + 4 or ≥ 4 mm CCL) cancer [intermediate-high risk significant]. For each, logistic-regression mp-MRI models were derived from the training cohort and validated internally and with the temporal cohort. Sensitivity/specificity and the area under the receiver operating characteristic (ROC-AUC) curve were calculated. LR model performance was compared to radiologists' performance. Twenty-eight of 70 patients from the training cohort, and 25/85 patients from the temporal validation cohort had significant cancer on TPM. The ROC-AUC of the LR model for classification of cancer was 0.73/0.67 at internal/temporal validation. The radiologist A/B ROC-AUC was 0.65/0.74 (temporal cohort). For patients scored by radiologists as Prostate Imaging Reporting and Data System (Pi-RADS) score 3, sensitivity/specificity of radiologist A 'best guess' and LR model was 0.14/0.54 and 0.71/0.61, respectively; and radiologist B 'best guess' and LR model was 0.40/0.34 and 0.50/0.76, respectively. LR models can improve classification of Pi-RADS score 3 lesions similar to experienced radiologists. (orig.)

  5. Minimax Regression Quantiles

    DEFF Research Database (Denmark)

    Bache, Stefan Holst

    A new and alternative quantile regression estimator is developed and it is shown that the estimator is root n-consistent and asymptotically normal. The estimator is based on a minimax ‘deviance function’ and has asymptotically equivalent properties to the usual quantile regression estimator. It is......, however, a different and therefore new estimator. It allows for both linear- and nonlinear model specifications. A simple algorithm for computing the estimates is proposed. It seems to work quite well in practice but whether it has theoretical justification is still an open question....

  6. Support vector methods for survival analysis: a comparison between ranking and regression approaches.

    Science.gov (United States)

    Van Belle, Vanya; Pelckmans, Kristiaan; Van Huffel, Sabine; Suykens, Johan A K

    2011-10-01

    To compare and evaluate ranking, regression and combined machine learning approaches for the analysis of survival data. The literature describes two approaches based on support vector machines to deal with censored observations. In the first approach the key idea is to rephrase the task as a ranking problem via the concordance index, a problem which can be solved efficiently in a context of structural risk minimization and convex optimization techniques. In a second approach, one uses a regression approach, dealing with censoring by means of inequality constraints. The goal of this paper is then twofold: (i) introducing a new model combining the ranking and regression strategy, which retains the link with existing survival models such as the proportional hazards model via transformation models; and (ii) comparison of the three techniques on 6 clinical and 3 high-dimensional datasets and discussing the relevance of these techniques over classical approaches fur survival data. We compare svm-based survival models based on ranking constraints, based on regression constraints and models based on both ranking and regression constraints. The performance of the models is compared by means of three different measures: (i) the concordance index, measuring the model's discriminating ability; (ii) the logrank test statistic, indicating whether patients with a prognostic index lower than the median prognostic index have a significant different survival than patients with a prognostic index higher than the median; and (iii) the hazard ratio after normalization to restrict the prognostic index between 0 and 1. Our results indicate a significantly better performance for models including regression constraints above models only based on ranking constraints. This work gives empirical evidence that svm-based models using regression constraints perform significantly better than svm-based models based on ranking constraints. Our experiments show a comparable performance for methods

  7. Regression with Sparse Approximations of Data

    DEFF Research Database (Denmark)

    Noorzad, Pardis; Sturm, Bob L.

    2012-01-01

    We propose sparse approximation weighted regression (SPARROW), a method for local estimation of the regression function that uses sparse approximation with a dictionary of measurements. SPARROW estimates the regression function at a point with a linear combination of a few regressands selected...... by a sparse approximation of the point in terms of the regressors. We show SPARROW can be considered a variant of \\(k\\)-nearest neighbors regression (\\(k\\)-NNR), and more generally, local polynomial kernel regression. Unlike \\(k\\)-NNR, however, SPARROW can adapt the number of regressors to use based...

  8. The effect of psychotherapeutic interventions on positive and negative affect in depression: A systematic review and meta-analysis.

    Science.gov (United States)

    Boumparis, Nikolaos; Karyotaki, Eirini; Kleiboer, Annet; Hofmann, Stefan G; Cuijpers, Pim

    2016-09-15

    Depression is a mental disorder characterized by high and dysregulated negative affect in addition to diminished positive affect. To our knowledge, there has been no systematic review of the impact of psychotherapeutic interventions on these affective dimensions. Two comprehensive literature searches for all randomized controlled trials of psychotherapy in adults with depression were performed. The first from 1996 to December 31, 2014 and the second from January 1, 2015 to December 31, 2015. The primary outcome was the mean score of positive and negative affect. Depressive symptoms were measured to be included as a predictor in the meta-regression analyses. Ten studies with 793 adults with depression were included. All studies assessed positive and negative affect. Psychotherapeutic interventions resulted in significantly increased positive affect (g=0.41; 95% CI: 0.16-0.66 p=0.001), and significantly decreased negative affect (g=0.32; 95% CI: 0.15-0.78, p=0.001) in depressed adults. Because of the small number and substantial heterogeneity of the existing studies the meta-regression analyses produced conflicting results. As a consequence, we were unable to sufficiently demonstrate whether NA and depressive symptoms are in fact correlated or not. Given the small number and heterogeneity of the included studies, the findings should be considered with caution. Psychotherapeutic interventions demonstrate low to moderate effects in enhancing positive and reducing negative affect in depressed adults. Copyright © 2016 Elsevier B.V. All rights reserved.

  9. Logistic regression applied to natural hazards: rare event logistic regression with replications

    Directory of Open Access Journals (Sweden)

    M. Guns

    2012-06-01

    Full Text Available Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logistic regression with replications, combines the strength of probabilistic and statistical methods, and allows overcoming some of the limitations of previous developments through robust variable selection. This technique was here developed for the analyses of landslide controlling factors, but the concept is widely applicable for statistical analyses of natural hazards.

  10. Influence of coronary artery disease prevalence on predictive values of coronary CT angiography: a meta-regression analysis

    Energy Technology Data Exchange (ETDEWEB)

    Schlattmann, Peter [University Hospital of Friedrich-Schiller University Jena, Department of Medical Statistics, Informatics and Documentation, Jena (Germany); Schuetz, Georg M. [Freie Universitaet Berlin, Charite, Medical School, Department of Radiology, Humboldt-Universitaet zu Berlin, Berlin (Germany); Dewey, Marc [Freie Universitaet Berlin, Charite, Medical School, Department of Radiology, Humboldt-Universitaet zu Berlin, Berlin (Germany); Charite, Institut fuer Radiologie, Berlin (Germany)

    2011-09-15

    To evaluate the impact of coronary artery disease (CAD) prevalence on the predictive values of coronary CT angiography. We performed a meta-regression based on a generalised linear mixed model using the binomial distribution and a logit link to analyse the influence of the prevalence of CAD in published studies on the per-patient negative and positive predictive values of CT in comparison to conventional coronary angiography as the reference standard. A prevalence range in which the negative predictive value was higher than 90%, while at the same time the positive predictive value was higher than 70% was considered appropriate. The summary negative and positive predictive values of coronary CT angiography were 93.7% (95% confidence interval [CI] 92.8-94.5%) and 87.5% (95% CI, 86.5-88.5%), respectively. With 95% confidence, negative and positive predictive values higher than 90% and 70% were available with CT for a CAD prevalence of 18-63%. CT systems with >16 detector rows met these requirements for the positive (P < 0.01) and negative (P < 0.05) predictive values in a significantly broader range than systems with {<=}16 detector rows. It is reasonable to perform coronary CT angiography as a rule-out test in patients with a low-to-intermediate likelihood of disease. (orig.)

  11. Influence of coronary artery disease prevalence on predictive values of coronary CT angiography: a meta-regression analysis

    International Nuclear Information System (INIS)

    Schlattmann, Peter; Schuetz, Georg M.; Dewey, Marc

    2011-01-01

    To evaluate the impact of coronary artery disease (CAD) prevalence on the predictive values of coronary CT angiography. We performed a meta-regression based on a generalised linear mixed model using the binomial distribution and a logit link to analyse the influence of the prevalence of CAD in published studies on the per-patient negative and positive predictive values of CT in comparison to conventional coronary angiography as the reference standard. A prevalence range in which the negative predictive value was higher than 90%, while at the same time the positive predictive value was higher than 70% was considered appropriate. The summary negative and positive predictive values of coronary CT angiography were 93.7% (95% confidence interval [CI] 92.8-94.5%) and 87.5% (95% CI, 86.5-88.5%), respectively. With 95% confidence, negative and positive predictive values higher than 90% and 70% were available with CT for a CAD prevalence of 18-63%. CT systems with >16 detector rows met these requirements for the positive (P < 0.01) and negative (P < 0.05) predictive values in a significantly broader range than systems with ≤16 detector rows. It is reasonable to perform coronary CT angiography as a rule-out test in patients with a low-to-intermediate likelihood of disease. (orig.)

  12. A simple approach to power and sample size calculations in logistic regression and Cox regression models.

    Science.gov (United States)

    Vaeth, Michael; Skovlund, Eva

    2004-06-15

    For a given regression problem it is possible to identify a suitably defined equivalent two-sample problem such that the power or sample size obtained for the two-sample problem also applies to the regression problem. For a standard linear regression model the equivalent two-sample problem is easily identified, but for generalized linear models and for Cox regression models the situation is more complicated. An approximately equivalent two-sample problem may, however, also be identified here. In particular, we show that for logistic regression and Cox regression models the equivalent two-sample problem is obtained by selecting two equally sized samples for which the parameters differ by a value equal to the slope times twice the standard deviation of the independent variable and further requiring that the overall expected number of events is unchanged. In a simulation study we examine the validity of this approach to power calculations in logistic regression and Cox regression models. Several different covariate distributions are considered for selected values of the overall response probability and a range of alternatives. For the Cox regression model we consider both constant and non-constant hazard rates. The results show that in general the approach is remarkably accurate even in relatively small samples. Some discrepancies are, however, found in small samples with few events and a highly skewed covariate distribution. Comparison with results based on alternative methods for logistic regression models with a single continuous covariate indicates that the proposed method is at least as good as its competitors. The method is easy to implement and therefore provides a simple way to extend the range of problems that can be covered by the usual formulas for power and sample size determination. Copyright 2004 John Wiley & Sons, Ltd.

  13. Significant changes in sexual behavior after a diagnosis of human papillomavirus-positive and human papillomavirus-negative oral cancer.

    Science.gov (United States)

    Taberna, Miren; Inglehart, Ronald C; Pickard, Robert K L; Fakhry, Carole; Agrawal, Amit; Katz, Mira L; Gillison, Maura L

    2017-04-01

    Sexual behavior and oral human papillomavirus (HPV) infection are risk factors for oral squamous cell carcinoma (OSCC). The effects of OSCC diagnosis and treatment on subsequent relationship stress and sexual behavior are unknown. Incident cases of HPV-positive or HPV-negative OSCC in patients who had a partnered relationship and partners of patients with oropharyngeal cancer were eligible for a study in which surveys were administered at diagnosis and at the 6-month follow-up time point to assess relationship distress, HPV transmission and concerns about health consequences, and sexual behavior. The frequency distributions of responses, stratified by tumor HPV status, were compared at baseline and follow-up. In total, 262 patients with OSCC and 81 partners were enrolled. Among the patients, 142 (54.2%) had HPV-positive OSCC, and 120 (45.8%) had HPV-negative OSCC. Relationship distress was infrequently reported, and 69% of patients felt that their relationship had strengthened since the cancer diagnosis. Both HPV-positive patients (25%) and their partners (14%) reported feelings of guilt or responsibility for the diagnosis of an HPV-caused cancer. Concern over sexual, but not nonsexual, HPV transmission to partners was reported by 50%. Significant declines in the frequency of vaginal and oral sexual behaviors were reported at follow-up, regardless of tumor HPV status. From baseline to 6 months, significant increases in abstinence from vaginal sex (from 10% to 34%; P oral sex (from 25% to 80%; P oral sex, regardless of tumor HPV status. Sexual behavior is an important quality-of-life outcome to assess within clinical trials. [See related editorial on pages 000-000, this issue.] Cancer 2017. © 2017 American Cancer Society. Cancer 2017;123:1156-1165. © 2016 American Cancer Society. © 2017 American Cancer Society.

  14. Factors associated with conception among a sample of HIV-positive ...

    African Journals Online (AJOL)

    positive status, the variables were compared for women in two groups: those who conceived while knowing their HIV-positive status and those who discovered their HIV status during pregnancy. Bivariate and logistic regression analyses were ...

  15. Associations Between Positive Body Image, Sexual Liberalism, and Unconventional Sexual Practices in U.S. Adults.

    Science.gov (United States)

    Swami, Viren; Weis, Laura; Barron, David; Furnham, Adrian

    2017-11-01

    While studies have documented robust relationships between body image and sexual health outcomes, few studies have looked beyond sexual functioning in women. Here, we hypothesized that more positive body image would be associated with greater sexual liberalism and more positive attitudes toward unconventional sexual practices. An online sample of 151 women and 164 men from the U.S. completed measures of sexual liberalism, attitudes toward unconventional sexual practices, and indices of positive body image (i.e., body appreciation, body acceptance by others, body image flexibility, and body pride), and provided their demographic details. Regression analyses indicated that, once the effects of sexual orientation, relationship status, age, and body mass index had been accounted for, higher body appreciation was significantly associated with greater sexual liberalism in women and men. Furthermore, higher body appreciation and body image flexibility were significantly associated with more positive attitudes toward unconventional sexual practices in women and men. These results may have implications for scholars working from a sex-positive perspective, particularly in terms of understanding the role body image plays in sexual attitudes and behaviors.

  16. Identification of sequence motifs significantly associated with antisense activity

    Directory of Open Access Journals (Sweden)

    Peek Andrew S

    2007-06-01

    Full Text Available Abstract Background Predicting the suppression activity of antisense oligonucleotide sequences is the main goal of the rational design of nucleic acids. To create an effective predictive model, it is important to know what properties of an oligonucleotide sequence associate significantly with antisense activity. Also, for the model to be efficient we must know what properties do not associate significantly and can be omitted from the model. This paper will discuss the results of a randomization procedure to find motifs that associate significantly with either high or low antisense suppression activity, analysis of their properties, as well as the results of support vector machine modelling using these significant motifs as features. Results We discovered 155 motifs that associate significantly with high antisense suppression activity and 202 motifs that associate significantly with low suppression activity. The motifs range in length from 2 to 5 bases, contain several motifs that have been previously discovered as associating highly with antisense activity, and have thermodynamic properties consistent with previous work associating thermodynamic properties of sequences with their antisense activity. Statistical analysis revealed no correlation between a motif's position within an antisense sequence and that sequences antisense activity. Also, many significant motifs existed as subwords of other significant motifs. Support vector regression experiments indicated that the feature set of significant motifs increased correlation compared to all possible motifs as well as several subsets of the significant motifs. Conclusion The thermodynamic properties of the significantly associated motifs support existing data correlating the thermodynamic properties of the antisense oligonucleotide with antisense efficiency, reinforcing our hypothesis that antisense suppression is strongly associated with probe/target thermodynamics, as there are no enzymatic

  17. Multiple regression as a preventive tool for determining the risk of Legionella spp.

    Directory of Open Access Journals (Sweden)

    Enrique Gea-Izquierdo

    2012-04-01

    Full Text Available To determine the interrelationship between health & hygiene conditions for prevention of legionellosis, the compositionof materials used in water distribution systems, the water origin and Legionella pneumophila risk. Material and methods. Include adescriptive study and multiple regression analysis on a sample of golf course sprinkler irrigation systems (n=31 pertaining to hotelslocated on the Costa del Sol (Malaga, Spain. The study was carried out in 2009. Results. Presented a significant lineal relation, withall the independent variables contributing significantly (p<0.05 to the model’s fit. The relationship between water type and the risk ofLegionella, as well as the material composition and the latter, is lineal and positive. In contrast, the relationship between health-hygieneconditions and Legionella risk is lineal and negative. Conclusion. The characterization of Legionella pneumophila concentration, asdefined by the risk in water and through use of the predictive method, can contribute to the consideration of new influence variables inthe development of the agent, resulting in improved control and prevention of the disease.

  18. Testing the Perturbation Sensitivity of Abortion-Crime Regressions

    Directory of Open Access Journals (Sweden)

    Michał Brzeziński

    2012-06-01

    Full Text Available The hypothesis that the legalisation of abortion contributed significantly to the reduction of crime in the United States in 1990s is one of the most prominent ideas from the recent “economics-made-fun” movement sparked by the book Freakonomics. This paper expands on the existing literature about the computational stability of abortion-crime regressions by testing the sensitivity of coefficients’ estimates to small amounts of data perturbation. In contrast to previous studies, we use a new data set on crime correlates for each of the US states, the original model specifica-tion and estimation methodology, and an improved data perturbation algorithm. We find that the coefficients’ estimates in abortion-crime regressions are not computationally stable and, therefore, are unreliable.

  19. Model building strategy for logistic regression: purposeful selection.

    Science.gov (United States)

    Zhang, Zhongheng

    2016-03-01

    Logistic regression is one of the most commonly used models to account for confounders in medical literature. The article introduces how to perform purposeful selection model building strategy with R. I stress on the use of likelihood ratio test to see whether deleting a variable will have significant impact on model fit. A deleted variable should also be checked for whether it is an important adjustment of remaining covariates. Interaction should be checked to disentangle complex relationship between covariates and their synergistic effect on response variable. Model should be checked for the goodness-of-fit (GOF). In other words, how the fitted model reflects the real data. Hosmer-Lemeshow GOF test is the most widely used for logistic regression model.

  20. Systematic changes in position sense accompany normal aging across adulthood.

    Science.gov (United States)

    Herter, Troy M; Scott, Stephen H; Dukelow, Sean P

    2014-03-25

    Development of clinical neurological assessments aimed at separating normal from abnormal capabilities requires a comprehensive understanding of how basic neurological functions change (or do not change) with increasing age across adulthood. In the case of proprioception, the research literature has failed to conclusively determine whether or not position sense in the upper limb deteriorates in elderly individuals. The present study was conducted a) to quantify whether upper limb position sense deteriorates with increasing age, and b) to generate a set of normative data that can be used for future comparisons with clinical populations. We examined position sense in 209 healthy males and females between the ages of 18 and 90 using a robotic arm position-matching task that is both objective and reliable. In this task, the robot moved an arm to one of nine positions and subjects attempted to mirror-match that position with the opposite limb. Measures of position sense were recorded by the robotic apparatus in hand-and joint-based coordinates, and linear regressions were used to quantify age-related changes and percentile boundaries of normal behaviour. For clinical comparisons, we also examined influences of sex (male versus female) and test-hand (dominant versus non-dominant) on all measures of position sense. Analyses of hand-based parameters identified several measures of position sense (Variability, Shift, Spatial Contraction, Absolute Error) with significant effects of age, sex, and test-hand. Joint-based parameters at the shoulder (Absolute Error) and elbow (Variability, Shift, Absolute Error) also exhibited significant effects of age and test-hand. The present study provides strong evidence that several measures of upper extremity position sense exhibit declines with age. Furthermore, this data provides a basis for quantifying when changes in position sense are related to normal aging or alternatively, pathology.

  1. Post-processing through linear regression

    Science.gov (United States)

    van Schaeybroeck, B.; Vannitsem, S.

    2011-03-01

    Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed, related to the effective correction of forecast error, the optimal variability of the corrected forecast and multicollinearity. The regression schemes under consideration include the ordinary least-square (OLS) method, a new time-dependent Tikhonov regularization (TDTR) method, the total least-square method, a new geometric-mean regression (GM), a recently introduced error-in-variables (EVMOS) method and, finally, a "best member" OLS method. The advantages and drawbacks of each method are clarified. These techniques are applied in the context of the 63 Lorenz system, whose model version is affected by both initial condition and model errors. For short forecast lead times, the number and choice of predictors plays an important role. Contrarily to the other techniques, GM degrades when the number of predictors increases. At intermediate lead times, linear regression is unable to provide corrections to the forecast and can sometimes degrade the performance (GM and the best member OLS with noise). At long lead times the regression schemes (EVMOS, TDTR) which yield the correct variability and the largest correlation between ensemble error and spread, should be preferred.

  2. Catheter placement for lysis of spontaneous intracerebral hematomas: does a catheter position in the core of the hematoma allow more effective and faster hematoma lysis?

    Science.gov (United States)

    Malinova, Vesna; Schlegel, Anna; Rohde, Veit; Mielke, Dorothee

    2017-07-01

    For the fibrinolytic therapy of intracerebral hematomas (ICH) using recombinant tissue plasminogen activator (rtPA), a catheter position in the core of the hematoma along the largest clot diameter was assumed to be optimal for an effective clot lysis. However, it never had been proven that core position indeed enhances clot lysis if compared with less optimal catheter positions. In this study, the impact of the catheter position on the effectiveness and on the time course of clot lysis was evaluated. We analyzed the catheter position using a relative error calculating the distance perpendicular to the catheter's center in relation to hematoma's diameter and evaluated the relative hematoma volume reduction (RVR). The correlation of the RVR with the catheter position was evaluated. Additionally, we tried to identify patterns of clot lysis with different catheter positions. The patient's outcome at discharge was evaluated using the Glasgow outcome score. A total of 105 patients were included in the study. The mean hematoma volume was 56 ml. The overall RVR was 62.7 %. In 69 patients, a catheter position in the core of the clot was achieved. We found no significant correlation between catheter position and hematoma RVR (linear regression, p = 0.14). Core catheter position leads to more symmetrical hematoma RVR. Faster clot lysis happens in the vicinity of the catheter openings. We found no significant difference in the patient's outcome dependent on the catheter position (linear regression, p = 0.90). The catheter position in the core of the hematoma along its largest diameter does not significantly influence the effectiveness of clot lysis after rtPA application.

  3. Regression modeling methods, theory, and computation with SAS

    CERN Document Server

    Panik, Michael

    2009-01-01

    Regression Modeling: Methods, Theory, and Computation with SAS provides an introduction to a diverse assortment of regression techniques using SAS to solve a wide variety of regression problems. The author fully documents the SAS programs and thoroughly explains the output produced by the programs.The text presents the popular ordinary least squares (OLS) approach before introducing many alternative regression methods. It covers nonparametric regression, logistic regression (including Poisson regression), Bayesian regression, robust regression, fuzzy regression, random coefficients regression,

  4. Mapping geogenic radon potential by regression kriging

    Energy Technology Data Exchange (ETDEWEB)

    Pásztor, László [Institute for Soil Sciences and Agricultural Chemistry, Centre for Agricultural Research, Hungarian Academy of Sciences, Department of Environmental Informatics, Herman Ottó út 15, 1022 Budapest (Hungary); Szabó, Katalin Zsuzsanna, E-mail: sz_k_zs@yahoo.de [Department of Chemistry, Institute of Environmental Science, Szent István University, Páter Károly u. 1, Gödöllő 2100 (Hungary); Szatmári, Gábor; Laborczi, Annamária [Institute for Soil Sciences and Agricultural Chemistry, Centre for Agricultural Research, Hungarian Academy of Sciences, Department of Environmental Informatics, Herman Ottó út 15, 1022 Budapest (Hungary); Horváth, Ákos [Department of Atomic Physics, Eötvös University, Pázmány Péter sétány 1/A, 1117 Budapest (Hungary)

    2016-02-15

    Radon ({sup 222}Rn) gas is produced in the radioactive decay chain of uranium ({sup 238}U) which is an element that is naturally present in soils. Radon is transported mainly by diffusion and convection mechanisms through the soil depending mainly on the physical and meteorological parameters of the soil and can enter and accumulate in buildings. Health risks originating from indoor radon concentration can be attributed to natural factors and is characterized by geogenic radon potential (GRP). Identification of areas with high health risks require spatial modeling, that is, mapping of radon risk. In addition to geology and meteorology, physical soil properties play a significant role in the determination of GRP. In order to compile a reliable GRP map for a model area in Central-Hungary, spatial auxiliary information representing GRP forming environmental factors were taken into account to support the spatial inference of the locally measured GRP values. Since the number of measured sites was limited, efficient spatial prediction methodologies were searched for to construct a reliable map for a larger area. Regression kriging (RK) was applied for the interpolation using spatially exhaustive auxiliary data on soil, geology, topography, land use and climate. RK divides the spatial inference into two parts. Firstly, the deterministic component of the target variable is determined by a regression model. The residuals of the multiple linear regression analysis represent the spatially varying but dependent stochastic component, which are interpolated by kriging. The final map is the sum of the two component predictions. Overall accuracy of the map was tested by Leave-One-Out Cross-Validation. Furthermore the spatial reliability of the resultant map is also estimated by the calculation of the 90% prediction interval of the local prediction values. The applicability of the applied method as well as that of the map is discussed briefly. - Highlights: • A new method

  5. Mapping geogenic radon potential by regression kriging

    International Nuclear Information System (INIS)

    Pásztor, László; Szabó, Katalin Zsuzsanna; Szatmári, Gábor; Laborczi, Annamária; Horváth, Ákos

    2016-01-01

    Radon ( 222 Rn) gas is produced in the radioactive decay chain of uranium ( 238 U) which is an element that is naturally present in soils. Radon is transported mainly by diffusion and convection mechanisms through the soil depending mainly on the physical and meteorological parameters of the soil and can enter and accumulate in buildings. Health risks originating from indoor radon concentration can be attributed to natural factors and is characterized by geogenic radon potential (GRP). Identification of areas with high health risks require spatial modeling, that is, mapping of radon risk. In addition to geology and meteorology, physical soil properties play a significant role in the determination of GRP. In order to compile a reliable GRP map for a model area in Central-Hungary, spatial auxiliary information representing GRP forming environmental factors were taken into account to support the spatial inference of the locally measured GRP values. Since the number of measured sites was limited, efficient spatial prediction methodologies were searched for to construct a reliable map for a larger area. Regression kriging (RK) was applied for the interpolation using spatially exhaustive auxiliary data on soil, geology, topography, land use and climate. RK divides the spatial inference into two parts. Firstly, the deterministic component of the target variable is determined by a regression model. The residuals of the multiple linear regression analysis represent the spatially varying but dependent stochastic component, which are interpolated by kriging. The final map is the sum of the two component predictions. Overall accuracy of the map was tested by Leave-One-Out Cross-Validation. Furthermore the spatial reliability of the resultant map is also estimated by the calculation of the 90% prediction interval of the local prediction values. The applicability of the applied method as well as that of the map is discussed briefly. - Highlights: • A new method, regression

  6. Use of probabilistic weights to enhance linear regression myoelectric control

    Science.gov (United States)

    Smith, Lauren H.; Kuiken, Todd A.; Hargrove, Levi J.

    2015-12-01

    Objective. Clinically available prostheses for transradial amputees do not allow simultaneous myoelectric control of degrees of freedom (DOFs). Linear regression methods can provide simultaneous myoelectric control, but frequently also result in difficulty with isolating individual DOFs when desired. This study evaluated the potential of using probabilistic estimates of categories of gross prosthesis movement, which are commonly used in classification-based myoelectric control, to enhance linear regression myoelectric control. Approach. Gaussian models were fit to electromyogram (EMG) feature distributions for three movement classes at each DOF (no movement, or movement in either direction) and used to weight the output of linear regression models by the probability that the user intended the movement. Eight able-bodied and two transradial amputee subjects worked in a virtual Fitts’ law task to evaluate differences in controllability between linear regression and probability-weighted regression for an intramuscular EMG-based three-DOF wrist and hand system. Main results. Real-time and offline analyses in able-bodied subjects demonstrated that probability weighting improved performance during single-DOF tasks (p < 0.05) by preventing extraneous movement at additional DOFs. Similar results were seen in experiments with two transradial amputees. Though goodness-of-fit evaluations suggested that the EMG feature distributions showed some deviations from the Gaussian, equal-covariance assumptions used in this experiment, the assumptions were sufficiently met to provide improved performance compared to linear regression control. Significance. Use of probability weights can improve the ability to isolate individual during linear regression myoelectric control, while maintaining the ability to simultaneously control multiple DOFs.

  7. Semiparametric regression during 2003–2007

    KAUST Repository

    Ruppert, David; Wand, M.P.; Carroll, Raymond J.

    2009-01-01

    Semiparametric regression is a fusion between parametric regression and nonparametric regression that integrates low-rank penalized splines, mixed model and hierarchical Bayesian methodology – thus allowing more streamlined handling of longitudinal and spatial correlation. We review progress in the field over the five-year period between 2003 and 2007. We find semiparametric regression to be a vibrant field with substantial involvement and activity, continual enhancement and widespread application.

  8. Unbalanced Regressions and the Predictive Equation

    DEFF Research Database (Denmark)

    Osterrieder, Daniela; Ventosa-Santaulària, Daniel; Vera-Valdés, J. Eduardo

    Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness in the theoreti......Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness...

  9. Linear Regression on Sparse Features for Single-Channel Speech Separation

    DEFF Research Database (Denmark)

    Schmidt, Mikkel N.; Olsson, Rasmus Kongsgaard

    2007-01-01

    In this work we address the problem of separating multiple speakers from a single microphone recording. We formulate a linear regression model for estimating each speaker based on features derived from the mixture. The employed feature representation is a sparse, non-negative encoding of the speech...... mixture in terms of pre-learned speaker-dependent dictionaries. Previous work has shown that this feature representation by itself provides some degree of separation. We show that the performance is significantly improved when regression analysis is performed on the sparse, non-negative features, both...

  10. Comparison of multinomial logistic regression and logistic regression: which is more efficient in allocating land use?

    Science.gov (United States)

    Lin, Yingzhi; Deng, Xiangzheng; Li, Xing; Ma, Enjun

    2014-12-01

    Spatially explicit simulation of land use change is the basis for estimating the effects of land use and cover change on energy fluxes, ecology and the environment. At the pixel level, logistic regression is one of the most common approaches used in spatially explicit land use allocation models to determine the relationship between land use and its causal factors in driving land use change, and thereby to evaluate land use suitability. However, these models have a drawback in that they do not determine/allocate land use based on the direct relationship between land use change and its driving factors. Consequently, a multinomial logistic regression method was introduced to address this flaw, and thereby, judge the suitability of a type of land use in any given pixel in a case study area of the Jiangxi Province, China. A comparison of the two regression methods indicated that the proportion of correctly allocated pixels using multinomial logistic regression was 92.98%, which was 8.47% higher than that obtained using logistic regression. Paired t-test results also showed that pixels were more clearly distinguished by multinomial logistic regression than by logistic regression. In conclusion, multinomial logistic regression is a more efficient and accurate method for the spatial allocation of land use changes. The application of this method in future land use change studies may improve the accuracy of predicting the effects of land use and cover change on energy fluxes, ecology, and environment.

  11. Interpretation of commonly used statistical regression models.

    Science.gov (United States)

    Kasza, Jessica; Wolfe, Rory

    2014-01-01

    A review of some regression models commonly used in respiratory health applications is provided in this article. Simple linear regression, multiple linear regression, logistic regression and ordinal logistic regression are considered. The focus of this article is on the interpretation of the regression coefficients of each model, which are illustrated through the application of these models to a respiratory health research study. © 2013 The Authors. Respirology © 2013 Asian Pacific Society of Respirology.

  12. Comparison of Positive Youth Development for Youth With Chronic Conditions With Healthy Peers.

    Science.gov (United States)

    Maslow, Gary R; Hill, Sherika N; Pollock, McLean D

    2016-12-01

    Adolescents with childhood-onset chronic condition (COCC) are at increased risk for physical and psychological problems. Despite being at greater risk and having to deal with traumatic experiences and uncertainty, most adolescents with COCC do well across many domains. The Positive Youth Development (PYD) perspective provides a framework for examining thriving in youth and has been useful in understanding positive outcomes for general populations of youth as well as at-risk youth. This study aimed to compare levels of PYD assets between youth with COCC and youth without illness. Participants with COCC were recruited from specialty pediatric clinics while healthy participants were recruited from a large pediatric primary care practice. Inclusion criteria for participants included being (1) English speaking, (2) no documented intellectual disability in electronic medical record, and (3) aged between 13 and 18 years during the recruitment period. Univariate and bivariate analyses on key variables were conducted for adolescents with and without COCC. Finally, we performed multivariable linear regressions for PYD and its subdomains. There were no significant differences between overall PYD or any of the subdomains between the two groups. Multivariable linear regression models showed no statistically significant relationship between chronic condition status and PYD or the subdomains. The findings from this study support the application of the PYD perspective to this population of youth. The results of this study suggest that approaches shown to benefit healthy youth, could be used to promote positive outcomes for youth with COCC. Copyright © 2016 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

  13. Significant factors for work attractiveness and how these differ from the current work situation among operating department nurses.

    Science.gov (United States)

    Björn, Catrine; Lindberg, Magnus; Rissén, Dag

    2016-01-01

    The aim was to examine significant factors for work attractiveness and how these differ from the current work situation among operating department nurses. A second objective was to examine the associations between age, gender, length of employment, work engagement, work ability, self-rated health indicators and attractiveness of the current work situation. The attractiveness of work is rarely taken into account in research on nurse retention. To expand this knowledge, it is relevant to examine factors that make work attractive and their associations with related concepts. Correlational, cross-sectional survey using a convenience sample. Questionnaires were answered by 147 nurses in four operating departments in Sweden. Correlation and regression analyses were conducted. The nurses rated the significance of all factors of work attractiveness higher than they rated those factors in their current work situation; salary, organisation and physical work environment had the largest differences. The most significant attractive factors were relationships, leadership and status. A statistically significant positive correlation between work engagement and attractive work was found. In the multiple regression model, the independent variables work engagement and older age significantly predicted work attractiveness. Several factors should be considered in the effort to increase work attractiveness in operating departments and thereby to encourage nurse retention. Positive aspects of work seem to unite work engagement and attractive work, while work ability and self-rated health indicators are other important dimensions in nurse retention. The great discrepancies between the significance of attractive factors and the current work situation in salary, organisation and physical work environment suggest ways in which work attractiveness may be increased. To discover exactly what needs to be improved may require a deeper look into the construct of the examined factors. © 2015 John

  14. Spontaneous regression of residual low-grade cerebellar pilocytic astrocytomas in children

    International Nuclear Information System (INIS)

    Gunny, Roxana S.; Saunders, Dawn E.; Hayward, Richard D.; Phipps, Kim P.; Harding, Brian N.

    2005-01-01

    Cerebellar low-grade astrocytomas (CLGAs) of childhood are benign tumours and are usually curable by surgical resection alone or combined with adjuvant radiotherapy. To undertake a retrospective study of our children with CLGA to determine the optimum schedule for surveillance imaging following initial surgery. In this report we describe the phenomenon of spontaneous regression of residual tumour and discuss its prognostic significance regarding future imaging. A retrospective review was conducted of children treated for histologically proven CLGA at Great Ormond Street Hospital from 1988 to 1998. Of 83 children with CLGA identified, 13 (15.7%) had incomplete resections. Two children with large residual tumours associated with persistent symptoms underwent additional treatment. Eleven children were followed by surveillance imaging alone for a mean of 6.83 years (range 2-13.25 years). Spontaneous tumour regression was seen in 5 (45.5%) of the 11 children. There were no differences in age, gender, symptomatology, histological grade or Ki-67 fractions between those with spontaneous tumour regression and those with progression. There was a non-significant trend that larger volume residual tumours progressed. Residual tumour followed by surveillance imaging may either regress or progress. For children with residual disease we recommend surveillance imaging every 6 months for the first 2 years, every year for years 3, 4 and 5, then every second year if residual tumour is still present 5 years after initial surgery. This would detect not only progressive or recurrent disease, but also spontaneous regression which can occur later than disease progression. (orig.)

  15. Linear regression

    CERN Document Server

    Olive, David J

    2017-01-01

    This text covers both multiple linear regression and some experimental design models. The text uses the response plot to visualize the model and to detect outliers, does not assume that the error distribution has a known parametric distribution, develops prediction intervals that work when the error distribution is unknown, suggests bootstrap hypothesis tests that may be useful for inference after variable selection, and develops prediction regions and large sample theory for the multivariate linear regression model that has m response variables. A relationship between multivariate prediction regions and confidence regions provides a simple way to bootstrap confidence regions. These confidence regions often provide a practical method for testing hypotheses. There is also a chapter on generalized linear models and generalized additive models. There are many R functions to produce response and residual plots, to simulate prediction intervals and hypothesis tests, to detect outliers, and to choose response trans...

  16. Regression modeling of ground-water flow

    Science.gov (United States)

    Cooley, R.L.; Naff, R.L.

    1985-01-01

    Nonlinear multiple regression methods are developed to model and analyze groundwater flow systems. Complete descriptions of regression methodology as applied to groundwater flow models allow scientists and engineers engaged in flow modeling to apply the methods to a wide range of problems. Organization of the text proceeds from an introduction that discusses the general topic of groundwater flow modeling, to a review of basic statistics necessary to properly apply regression techniques, and then to the main topic: exposition and use of linear and nonlinear regression to model groundwater flow. Statistical procedures are given to analyze and use the regression models. A number of exercises and answers are included to exercise the student on nearly all the methods that are presented for modeling and statistical analysis. Three computer programs implement the more complex methods. These three are a general two-dimensional, steady-state regression model for flow in an anisotropic, heterogeneous porous medium, a program to calculate a measure of model nonlinearity with respect to the regression parameters, and a program to analyze model errors in computed dependent variables such as hydraulic head. (USGS)

  17. Examination of Parameters Affecting the House Prices by Multiple Regression Analysis and its Contributions to Earthquake-Based Urban Transformation

    Science.gov (United States)

    Denli, H. H.; Durmus, B.

    2016-12-01

    The purpose of this study is to examine the factors which may affect the apartment prices with multiple linear regression analysis models and visualize the results by value maps. The study is focused on a county of Istanbul - Turkey. Totally 390 apartments around the county Umraniye are evaluated due to their physical and locational conditions. The identification of factors affecting the price of apartments in the county with a population of approximately 600k is expected to provide a significant contribution to the apartment market.Physical factors are selected as the age, number of rooms, size, floor numbers of the building and the floor that the apartment is positioned in. Positional factors are selected as the distances to the nearest hospital, school, park and police station. Totally ten physical and locational parameters are examined by regression analysis.After the regression analysis has been performed, value maps are composed from the parameters age, price and price per square meters. The most significant of the composed maps is the price per square meters map. Results show that the location of the apartment has the most influence to the square meter price information of the apartment. A different practice is developed from the composed maps by searching the ability of using price per square meters map in urban transformation practices. By marking the buildings older than 15 years in the price per square meters map, a different and new interpretation has been made to determine the buildings, to which should be given priority during an urban transformation in the county.This county is very close to the North Anatolian Fault zone and is under the threat of earthquakes. By marking the apartments older than 15 years on the price per square meters map, both older and expensive square meters apartments list can be gathered. By the help of this list, the priority could be given to the selected higher valued old apartments to support the economy of the country

  18. Quantum algorithm for linear regression

    Science.gov (United States)

    Wang, Guoming

    2017-07-01

    We present a quantum algorithm for fitting a linear regression model to a given data set using the least-squares approach. Differently from previous algorithms which yield a quantum state encoding the optimal parameters, our algorithm outputs these numbers in the classical form. So by running it once, one completely determines the fitted model and then can use it to make predictions on new data at little cost. Moreover, our algorithm works in the standard oracle model, and can handle data sets with nonsparse design matrices. It runs in time poly( log2(N ) ,d ,κ ,1 /ɛ ) , where N is the size of the data set, d is the number of adjustable parameters, κ is the condition number of the design matrix, and ɛ is the desired precision in the output. We also show that the polynomial dependence on d and κ is necessary. Thus, our algorithm cannot be significantly improved. Furthermore, we also give a quantum algorithm that estimates the quality of the least-squares fit (without computing its parameters explicitly). This algorithm runs faster than the one for finding this fit, and can be used to check whether the given data set qualifies for linear regression in the first place.

  19. High serum coenzyme Q10, positively correlated with age, selenium and cholesterol, in Inuit of Greenland. A pilot study

    DEFF Research Database (Denmark)

    Pedersen, H S; Mortensen, S A; Rohde, M

    1999-01-01

    .001) compared to a Danish population. In a linear multiple regression model the S-CoQ10 level is significantly positively associated with age and S-selenium in males, and S-total cholesterol in females. The high level of CoQ10 in Greenlanders probably reflects diet, since no bioaccumulation takes place...

  20. MULGRES: a computer program for stepwise multiple regression analysis

    Science.gov (United States)

    A. Jeff Martin

    1971-01-01

    MULGRES is a computer program source deck that is designed for multiple regression analysis employing the technique of stepwise deletion in the search for most significant variables. The features of the program, along with inputs and outputs, are briefly described, with a note on machine compatibility.

  1. Inferring gene expression dynamics via functional regression analysis

    Directory of Open Access Journals (Sweden)

    Leng Xiaoyan

    2008-01-01

    Full Text Available Abstract Background Temporal gene expression profiles characterize the time-dynamics of expression of specific genes and are increasingly collected in current gene expression experiments. In the analysis of experiments where gene expression is obtained over the life cycle, it is of interest to relate temporal patterns of gene expression associated with different developmental stages to each other to study patterns of long-term developmental gene regulation. We use tools from functional data analysis to study dynamic changes by relating temporal gene expression profiles of different developmental stages to each other. Results We demonstrate that functional regression methodology can pinpoint relationships that exist between temporary gene expression profiles for different life cycle phases and incorporates dimension reduction as needed for these high-dimensional data. By applying these tools, gene expression profiles for pupa and adult phases are found to be strongly related to the profiles of the same genes obtained during the embryo phase. Moreover, one can distinguish between gene groups that exhibit relationships with positive and others with negative associations between later life and embryonal expression profiles. Specifically, we find a positive relationship in expression for muscle development related genes, and a negative relationship for strictly maternal genes for Drosophila, using temporal gene expression profiles. Conclusion Our findings point to specific reactivation patterns of gene expression during the Drosophila life cycle which differ in characteristic ways between various gene groups. Functional regression emerges as a useful tool for relating gene expression patterns from different developmental stages, and avoids the problems with large numbers of parameters and multiple testing that affect alternative approaches.

  2. Multiple Imputation of a Randomly Censored Covariate Improves Logistic Regression Analysis.

    Science.gov (United States)

    Atem, Folefac D; Qian, Jing; Maye, Jacqueline E; Johnson, Keith A; Betensky, Rebecca A

    2016-01-01

    Randomly censored covariates arise frequently in epidemiologic studies. The most commonly used methods, including complete case and single imputation or substitution, suffer from inefficiency and bias. They make strong parametric assumptions or they consider limit of detection censoring only. We employ multiple imputation, in conjunction with semi-parametric modeling of the censored covariate, to overcome these shortcomings and to facilitate robust estimation. We develop a multiple imputation approach for randomly censored covariates within the framework of a logistic regression model. We use the non-parametric estimate of the covariate distribution or the semiparametric Cox model estimate in the presence of additional covariates in the model. We evaluate this procedure in simulations, and compare its operating characteristics to those from the complete case analysis and a survival regression approach. We apply the procedures to an Alzheimer's study of the association between amyloid positivity and maternal age of onset of dementia. Multiple imputation achieves lower standard errors and higher power than the complete case approach under heavy and moderate censoring and is comparable under light censoring. The survival regression approach achieves the highest power among all procedures, but does not produce interpretable estimates of association. Multiple imputation offers a favorable alternative to complete case analysis and ad hoc substitution methods in the presence of randomly censored covariates within the framework of logistic regression.

  3. Maximum Entropy Discrimination Poisson Regression for Software Reliability Modeling.

    Science.gov (United States)

    Chatzis, Sotirios P; Andreou, Andreas S

    2015-11-01

    Reliably predicting software defects is one of the most significant tasks in software engineering. Two of the major components of modern software reliability modeling approaches are: 1) extraction of salient features for software system representation, based on appropriately designed software metrics and 2) development of intricate regression models for count data, to allow effective software reliability data modeling and prediction. Surprisingly, research in the latter frontier of count data regression modeling has been rather limited. More specifically, a lack of simple and efficient algorithms for posterior computation has made the Bayesian approaches appear unattractive, and thus underdeveloped in the context of software reliability modeling. In this paper, we try to address these issues by introducing a novel Bayesian regression model for count data, based on the concept of max-margin data modeling, effected in the context of a fully Bayesian model treatment with simple and efficient posterior distribution updates. Our novel approach yields a more discriminative learning technique, making more effective use of our training data during model inference. In addition, it allows of better handling uncertainty in the modeled data, which can be a significant problem when the training data are limited. We derive elegant inference algorithms for our model under the mean-field paradigm and exhibit its effectiveness using the publicly available benchmark data sets.

  4. A quantile regression analysis of the rebound effect: Evidence from the 2009 National Household Transportation Survey in the United States

    International Nuclear Information System (INIS)

    Su Qing

    2012-01-01

    This paper applies quantile regression method to measure the rebound effect and differentiate it with respect to demand for mobility using the 2009 National Household Transportation Survey (NHTS). The quantile regression results indicate that the rebound effect varies with the distribution of vehicle miles traveled (VMT), ranging between 0.11 and 0.19. Road network density and population density also play an important role in determining travel demand. Regression results indicate that travelers living in areas with higher road network density travel more miles although this positive impact consistently declines along the VMT distribution. Travelers living in areas with population density of at most 3000 persons per square miles travel more miles than those living in higher density areas. The quantile regression results also indicate that the impact of income is positive but declines consistently along the VMT distribution. - Highlights: ► This paper examines the magnitude of rebound effect using the 2009 National Household Transportation Survey data. ► Quantile regression method is applied to capture the variation of the rebound effect given the heterogeneous travelers. ► The regression results indicate that the rebound effect varies with VMT distribution. ► The estimated rebound effect fluctuates between 0.11 and 0.19.

  5. Post-processing through linear regression

    Directory of Open Access Journals (Sweden)

    B. Van Schaeybroeck

    2011-03-01

    Full Text Available Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed, related to the effective correction of forecast error, the optimal variability of the corrected forecast and multicollinearity. The regression schemes under consideration include the ordinary least-square (OLS method, a new time-dependent Tikhonov regularization (TDTR method, the total least-square method, a new geometric-mean regression (GM, a recently introduced error-in-variables (EVMOS method and, finally, a "best member" OLS method. The advantages and drawbacks of each method are clarified.

    These techniques are applied in the context of the 63 Lorenz system, whose model version is affected by both initial condition and model errors. For short forecast lead times, the number and choice of predictors plays an important role. Contrarily to the other techniques, GM degrades when the number of predictors increases. At intermediate lead times, linear regression is unable to provide corrections to the forecast and can sometimes degrade the performance (GM and the best member OLS with noise. At long lead times the regression schemes (EVMOS, TDTR which yield the correct variability and the largest correlation between ensemble error and spread, should be preferred.

  6. A comparison of random forest regression and multiple linear regression for prediction in neuroscience.

    Science.gov (United States)

    Smith, Paul F; Ganesh, Siva; Liu, Ping

    2013-10-30

    Regression is a common statistical tool for prediction in neuroscience. However, linear regression is by far the most common form of regression used, with regression trees receiving comparatively little attention. In this study, the results of conventional multiple linear regression (MLR) were compared with those of random forest regression (RFR), in the prediction of the concentrations of 9 neurochemicals in the vestibular nucleus complex and cerebellum that are part of the l-arginine biochemical pathway (agmatine, putrescine, spermidine, spermine, l-arginine, l-ornithine, l-citrulline, glutamate and γ-aminobutyric acid (GABA)). The R(2) values for the MLRs were higher than the proportion of variance explained values for the RFRs: 6/9 of them were ≥ 0.70 compared to 4/9 for RFRs. Even the variables that had the lowest R(2) values for the MLRs, e.g. ornithine (0.50) and glutamate (0.61), had much lower proportion of variance explained values for the RFRs (0.27 and 0.49, respectively). The RSE values for the MLRs were lower than those for the RFRs in all but two cases. In general, MLRs seemed to be superior to the RFRs in terms of predictive value and error. In the case of this data set, MLR appeared to be superior to RFR in terms of its explanatory value and error. This result suggests that MLR may have advantages over RFR for prediction in neuroscience with this kind of data set, but that RFR can still have good predictive value in some cases. Copyright © 2013 Elsevier B.V. All rights reserved.

  7. Logistic regression applied to natural hazards: rare event logistic regression with replications

    OpenAIRE

    Guns, M.; Vanacker, Veerle

    2012-01-01

    Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logisti...

  8. Solving the Omitted Variables Problem of Regression Analysis Using the Relative Vertical Position of Observations

    Directory of Open Access Journals (Sweden)

    Jonathan E. Leightner

    2012-01-01

    Full Text Available The omitted variables problem is one of regression analysis’ most serious problems. The standard approach to the omitted variables problem is to find instruments, or proxies, for the omitted variables, but this approach makes strong assumptions that are rarely met in practice. This paper introduces best projection reiterative truncated projected least squares (BP-RTPLS, the third generation of a technique that solves the omitted variables problem without using proxies or instruments. This paper presents a theoretical argument that BP-RTPLS produces unbiased reduced form estimates when there are omitted variables. This paper also provides simulation evidence that shows OLS produces between 250% and 2450% more errors than BP-RTPLS when there are omitted variables and when measurement and round-off error is 1 percent or less. In an example, the government spending multiplier, , is estimated using annual data for the USA between 1929 and 2010.

  9. Prevalence and correlates of positive mental health in Chinese adolescents.

    Science.gov (United States)

    Guo, Cheng; Tomson, Göran; Keller, Christina; Söderqvist, Fredrik

    2018-02-17

    Studies investigating the prevalence of positive mental health and its correlates are still scarce compared to the studies on mental disorders, although there is growing interest of assessing positive mental health in adolescents. So far, no other study examining the prevalence and determinants of positive mental health in Chinese adolescents has been found. The purpose of this study was to assess the prevalence and correlates of positive mental health in Chinese adolescents. This cross-sectional study used a questionnaire including Mental Health Continuum-Short Form (MHC-SF) and items regarding multiple aspects of adolescent life. The sample involved a total of 5399 students from grade 8 and 10 in Weifang, China. Multivariate Logistic regression analyses were performed to evaluate the associations between potential indicators regarding socio-economic situations, life style, social support and school life and positive mental health and calculate odds ratios and 95% confidence intervals. More than half (57.4%) of the participants were diagnosed as flourishing. The correlated factors of positive mental health in regression models included gender, perceived family economy, the occurrence of sibling(s), satisfaction of self-appearance, physical activity, sleep quality, stress, social trust, desire to learn, support from teachers and parents as well as whether being bullied at school (OR ranging from 1.23 to 2.75). The Hosmer-Lemeshow p-value for the final regression model (0.45) indicated adequate model fit. This study gives the first overview on prevalence and correlates of positive mental health in Chinese adolescents. The prevalence of positive mental health in Chinese adolescents is higher than reported in most of the previous studies also using MHC-SF. Our findings suggest that adolescents with advantageous socio-economic situations, life style, social support and school life are experiencing better positive mental health than others.

  10. Statistical approach for selection of regression model during validation of bioanalytical method

    Directory of Open Access Journals (Sweden)

    Natalija Nakov

    2014-06-01

    Full Text Available The selection of an adequate regression model is the basis for obtaining accurate and reproducible results during the bionalytical method validation. Given the wide concentration range, frequently present in bioanalytical assays, heteroscedasticity of the data may be expected. Several weighted linear and quadratic regression models were evaluated during the selection of the adequate curve fit using nonparametric statistical tests: One sample rank test and Wilcoxon signed rank test for two independent groups of samples. The results obtained with One sample rank test could not give statistical justification for the selection of linear vs. quadratic regression models because slight differences between the error (presented through the relative residuals were obtained. Estimation of the significance of the differences in the RR was achieved using Wilcoxon signed rank test, where linear and quadratic regression models were treated as two independent groups. The application of this simple non-parametric statistical test provides statistical confirmation of the choice of an adequate regression model.

  11. A Seemingly Unrelated Poisson Regression Model

    OpenAIRE

    King, Gary

    1989-01-01

    This article introduces a new estimator for the analysis of two contemporaneously correlated endogenous event count variables. This seemingly unrelated Poisson regression model (SUPREME) estimator combines the efficiencies created by single equation Poisson regression model estimators and insights from "seemingly unrelated" linear regression models.

  12. Impact of Dobutamine in Patients With Septic Shock: A Meta-Regression Analysis.

    Science.gov (United States)

    Nadeem, Rashid; Sockanathan, Shivani; Singh, Mukesh; Hussain, Tamseela; Kent, Patrick; AbuAlreesh, Sarah

    2017-05-01

    Septic shock frequently requires vasopressor agents. Conflicting evidence exists for use of inotropes in patients with septic shock. Data from English studies on human adult septic shock patients were collected. A total of 83 studies were reviewed, while 11 studies with 21 data sets including 239 patients were pooled for meta-regression analysis. For VO2, pooled difference in means (PDM) was 0.274. For cardiac index (CI), PDM was 0.783. For delivery of oxygen, PDM was -0.890. For heart rate, PDM was -0.714. For left ventricle stroke work index, PDM was 0.375. For mean arterial pressure, PDM was -0.204. For mean pulmonary artery pressure, PDM was 0.085. For O2 extraction, PDM was 0.647. For PaCO2, PDM was -0.053. For PaO2, PDM was 0.282. For pulmonary artery occlusive pressure, PDM was 0.270. For pulmonary capillary wedge pressure, PDM was 0.300. For PVO2, PDM was -0.492. For right atrial pressure, PDM was 0.246. For SaO2, PDM was 0.604. For stroke volume index, PDM was 0.446. For SvO2, PDM was -0.816. For systemic vascular resistance, PDM was -0.600. For systemic vascular resistance index, PDM was 0.319. Meta-regression analysis was performed for VO2, DO2, CI, and O2 extraction. Age was found to be significant confounding factor for CI, DO2, and O2 extraction. APACHE score was not found to be a significant confounding factor for any of the parameters. Dobutamine seems to have a positive effect on cardiovascular parameters in patients with septic shock. Prospective studies with larger samples are required to further validate this observation.

  13. Modelling infant mortality rate in Central Java, Indonesia use generalized poisson regression method

    Science.gov (United States)

    Prahutama, Alan; Sudarno

    2018-05-01

    The infant mortality rate is the number of deaths under one year of age occurring among the live births in a given geographical area during a given year, per 1,000 live births occurring among the population of the given geographical area during the same year. This problem needs to be addressed because it is an important element of a country’s economic development. High infant mortality rate will disrupt the stability of a country as it relates to the sustainability of the population in the country. One of regression model that can be used to analyze the relationship between dependent variable Y in the form of discrete data and independent variable X is Poisson regression model. Recently The regression modeling used for data with dependent variable is discrete, among others, poisson regression, negative binomial regression and generalized poisson regression. In this research, generalized poisson regression modeling gives better AIC value than poisson regression. The most significant variable is the Number of health facilities (X1), while the variable that gives the most influence to infant mortality rate is the average breastfeeding (X9).

  14. A Noise Trimming and Positional Significance of Transposon Insertion System to Identify Essential Genes in Yersinia pestis

    Science.gov (United States)

    Yang, Zheng Rong; Bullifent, Helen L.; Moore, Karen; Paszkiewicz, Konrad; Saint, Richard J.; Southern, Stephanie J.; Champion, Olivia L.; Senior, Nicola J.; Sarkar-Tyson, Mitali; Oyston, Petra C. F.; Atkins, Timothy P.; Titball, Richard W.

    2017-02-01

    Massively parallel sequencing technology coupled with saturation mutagenesis has provided new and global insights into gene functions and roles. At a simplistic level, the frequency of mutations within genes can indicate the degree of essentiality. However, this approach neglects to take account of the positional significance of mutations - the function of a gene is less likely to be disrupted by a mutation close to the distal ends. Therefore, a systematic bioinformatics approach to improve the reliability of essential gene identification is desirable. We report here a parametric model which introduces a novel mutation feature together with a noise trimming approach to predict the biological significance of Tn5 mutations. We show improved performance of essential gene prediction in the bacterium Yersinia pestis, the causative agent of plague. This method would have broad applicability to other organisms and to the identification of genes which are essential for competitiveness or survival under a broad range of stresses.

  15. The Relationship Between Brain-Behavioral Systems and Negative and Positive Affect in Patients With Migraine

    Directory of Open Access Journals (Sweden)

    Jovharifard

    2015-08-01

    Full Text Available Background Migraine is a chronic headache disorder that affects approximately 12% of the general population. Migraine is known as recurrent headache, pulsating, moderate with severe power, which lasts for 4 to 72 hours, aggravated by daily physical activity along with nausea, vomiting, photophobia or photophobia. Objectives The purpose of this study was to investigate the relationship between brain-behavioral systems and negative and positive affects in patients with migraine. Patients and Methods The research population included patients, who had referred to neurology clinics. One hundred and twenty cases were selected by accessible sampling based on the neurologist’s diagnosis of migraine headaches. They completed the Gray-Wilson (1989 Personality Questionnaire as well as Watson, Clark and Telligent (1988 positive and negative affect scale. The data were analyzed using the SPSS 19 software, correlation and stepwise regression. Results The results showed that positive affect had a significant positive correlation with active avoidance parameters and negative significant correlation with passive avoidance and extinction parameters. The findings also indicated that negative affect had a positive and significant relationship with passive avoidance and extinction. Conclusions It can be concluded that brain-behavioral systems may be the foundation of behavioral and emotional tendencies in patients with migraine headaches.

  16. Recursive Algorithm For Linear Regression

    Science.gov (United States)

    Varanasi, S. V.

    1988-01-01

    Order of model determined easily. Linear-regression algorithhm includes recursive equations for coefficients of model of increased order. Algorithm eliminates duplicative calculations, facilitates search for minimum order of linear-regression model fitting set of data satisfactory.

  17. Applied regression analysis a research tool

    CERN Document Server

    Pantula, Sastry; Dickey, David

    1998-01-01

    Least squares estimation, when used appropriately, is a powerful research tool. A deeper understanding of the regression concepts is essential for achieving optimal benefits from a least squares analysis. This book builds on the fundamentals of statistical methods and provides appropriate concepts that will allow a scientist to use least squares as an effective research tool. Applied Regression Analysis is aimed at the scientist who wishes to gain a working knowledge of regression analysis. The basic purpose of this book is to develop an understanding of least squares and related statistical methods without becoming excessively mathematical. It is the outgrowth of more than 30 years of consulting experience with scientists and many years of teaching an applied regression course to graduate students. Applied Regression Analysis serves as an excellent text for a service course on regression for non-statisticians and as a reference for researchers. It also provides a bridge between a two-semester introduction to...

  18. Estimate the contribution of incubation parameters influence egg hatchability using multiple linear regression analysis.

    Science.gov (United States)

    Khalil, Mohamed H; Shebl, Mostafa K; Kosba, Mohamed A; El-Sabrout, Karim; Zaki, Nesma

    2016-08-01

    This research was conducted to determine the most affecting parameters on hatchability of indigenous and improved local chickens' eggs. Five parameters were studied (fertility, early and late embryonic mortalities, shape index, egg weight, and egg weight loss) on four strains, namely Fayoumi, Alexandria, Matrouh, and Montazah. Multiple linear regression was performed on the studied parameters to determine the most influencing one on hatchability. The results showed significant differences in commercial and scientific hatchability among strains. Alexandria strain has the highest significant commercial hatchability (80.70%). Regarding the studied strains, highly significant differences in hatching chick weight among strains were observed. Using multiple linear regression analysis, fertility made the greatest percent contribution (71.31%) to hatchability, and the lowest percent contributions were made by shape index and egg weight loss. A prediction of hatchability using multiple regression analysis could be a good tool to improve hatchability percentage in chickens.

  19. Standards for Standardized Logistic Regression Coefficients

    Science.gov (United States)

    Menard, Scott

    2011-01-01

    Standardized coefficients in logistic regression analysis have the same utility as standardized coefficients in linear regression analysis. Although there has been no consensus on the best way to construct standardized logistic regression coefficients, there is now sufficient evidence to suggest a single best approach to the construction of a…

  20. [Application of negative binomial regression and modified Poisson regression in the research of risk factors for injury frequency].

    Science.gov (United States)

    Cao, Qingqing; Wu, Zhenqiang; Sun, Ying; Wang, Tiezhu; Han, Tengwei; Gu, Chaomei; Sun, Yehuan

    2011-11-01

    To Eexplore the application of negative binomial regression and modified Poisson regression analysis in analyzing the influential factors for injury frequency and the risk factors leading to the increase of injury frequency. 2917 primary and secondary school students were selected from Hefei by cluster random sampling method and surveyed by questionnaire. The data on the count event-based injuries used to fitted modified Poisson regression and negative binomial regression model. The risk factors incurring the increase of unintentional injury frequency for juvenile students was explored, so as to probe the efficiency of these two models in studying the influential factors for injury frequency. The Poisson model existed over-dispersion (P Poisson regression and negative binomial regression model, was fitted better. respectively. Both showed that male gender, younger age, father working outside of the hometown, the level of the guardian being above junior high school and smoking might be the results of higher injury frequencies. On a tendency of clustered frequency data on injury event, both the modified Poisson regression analysis and negative binomial regression analysis can be used. However, based on our data, the modified Poisson regression fitted better and this model could give a more accurate interpretation of relevant factors affecting the frequency of injury.

  1. Relationship of condylar position to disc position and morphology

    Energy Technology Data Exchange (ETDEWEB)

    Incesu, L.; Taskaya-Yilmaz, N. E-mail: nergizy@omu.edu.tr; Oeguetcen-Toller, M.; Uzun, E

    2004-09-01

    Introduction/objective: The purpose of this study was to assess whether condylar position, as depicted by magnetic resonance imaging, was an indicator of disc morphology and position. Methods and material: One hundred and twenty two TMJs of 61 patients with temporomandibular joint disorder were examined. Condylar position, disc deformity and degree of anterior disc displacement were evaluated by using magnetic resonance imaging. Results and discussion: Posterior condyle position was found to be the main feature of temporomandibular joints with slight and moderate anterior disc displacement. No statistical significance was found between the condylar position, and reducing and nonreducing disc positions. On the other hand, superior disc position was found to be statistically significant for centric condylar position. Conclusion: It was concluded that posterior condyle position could indicate anterior disc displacement whereas there was no relation between the position of condyle and the disc deformity.

  2. Association of depression with social support and self-esteem among HIV positives.

    Science.gov (United States)

    Jagannath, Vinita; Unnikrishnan, B; Hegde, Supriya; Ramapuram, John T; Rao, S; Achappa, B; Madi, D; Kotian, M S

    2011-12-01

    Depression in Human Immunodeficiency Virus (HIV) positives has implications such as poor drug compliance, lower quality of life, faster progression to full blown Acquired Immunodeficiency Syndrome (AIDS) and higher mortality. To assess depression, social support and self-esteem in HIV positives and to find out the association of depression with social support and self-esteem among HIV positive patients. Kasturba Medical College (KMC) Hospital, a tertiary care hospital, Mangalore, India and cross-sectional design. Study constituted of 105 HIV positive subjects; depression was assessed using BDI (Beck depression inventory), social support was assessed using Lubben social network scale and self-esteem was assessed using Rosenberg self-esteem scale. Kappa statistics was used to measure the agreement of depression assessed by BDI with clinical diagnosis of depression. Logistic regression analyses were done to find out predictors of depression among HIV positives. All analyses were conducted using Statistical Package for Social Sciences (SPSS) version 11.5. Depression was found to be present in 43.8% of HIV positives. Among the study subjects, 10.5% had high risk for isolation and low self-esteem was found only among 5.7%. In univariate analysis both gender and self-esteem were significantly associated with depression whereas in multivariate analysis only self-esteem was found to be significantly associated with depression. The present study shows a high prevalence of depression in HIV positive patients along with the importance of self-esteem. Copyright © 2011 Elsevier B.V. All rights reserved.

  3. The Effect of a Sports Stadium on Housing Rents: An Application of Geographically Weighted Regression

    Directory of Open Access Journals (Sweden)

    Jorge Enrique Agudelo Torres

    2015-06-01

    Full Text Available Researchers have determined that real estate prices vary in continuous ways as a function of spatial characteristics.  In this study we examine whether geographically weighted regression (GWR provides different estimates of price effects around a sports stadium than more traditional regression techniques.  We find that an application of GWR with hedonic prices finds that the stadium has a negative external effect on housing rents that extends outward 560 meters, in contrast to the positive external effect on housing rents found using a conventional estimation technique.

  4. Bayesian ARTMAP for regression.

    Science.gov (United States)

    Sasu, L M; Andonie, R

    2013-10-01

    Bayesian ARTMAP (BA) is a recently introduced neural architecture which uses a combination of Fuzzy ARTMAP competitive learning and Bayesian learning. Training is generally performed online, in a single-epoch. During training, BA creates input data clusters as Gaussian categories, and also infers the conditional probabilities between input patterns and categories, and between categories and classes. During prediction, BA uses Bayesian posterior probability estimation. So far, BA was used only for classification. The goal of this paper is to analyze the efficiency of BA for regression problems. Our contributions are: (i) we generalize the BA algorithm using the clustering functionality of both ART modules, and name it BA for Regression (BAR); (ii) we prove that BAR is a universal approximator with the best approximation property. In other words, BAR approximates arbitrarily well any continuous function (universal approximation) and, for every given continuous function, there is one in the set of BAR approximators situated at minimum distance (best approximation); (iii) we experimentally compare the online trained BAR with several neural models, on the following standard regression benchmarks: CPU Computer Hardware, Boston Housing, Wisconsin Breast Cancer, and Communities and Crime. Our results show that BAR is an appropriate tool for regression tasks, both for theoretical and practical reasons. Copyright © 2013 Elsevier Ltd. All rights reserved.

  5. Mechanisms of neuroblastoma regression

    Science.gov (United States)

    Brodeur, Garrett M.; Bagatell, Rochelle

    2014-01-01

    Recent genomic and biological studies of neuroblastoma have shed light on the dramatic heterogeneity in the clinical behaviour of this disease, which spans from spontaneous regression or differentiation in some patients, to relentless disease progression in others, despite intensive multimodality therapy. This evidence also suggests several possible mechanisms to explain the phenomena of spontaneous regression in neuroblastomas, including neurotrophin deprivation, humoral or cellular immunity, loss of telomerase activity and alterations in epigenetic regulation. A better understanding of the mechanisms of spontaneous regression might help to identify optimal therapeutic approaches for patients with these tumours. Currently, the most druggable mechanism is the delayed activation of developmentally programmed cell death regulated by the tropomyosin receptor kinase A pathway. Indeed, targeted therapy aimed at inhibiting neurotrophin receptors might be used in lieu of conventional chemotherapy or radiation in infants with biologically favourable tumours that require treatment. Alternative approaches consist of breaking immune tolerance to tumour antigens or activating neurotrophin receptor pathways to induce neuronal differentiation. These approaches are likely to be most effective against biologically favourable tumours, but they might also provide insights into treatment of biologically unfavourable tumours. We describe the different mechanisms of spontaneous neuroblastoma regression and the consequent therapeutic approaches. PMID:25331179

  6. Using the Ridge Regression Procedures to Estimate the Multiple Linear Regression Coefficients

    Science.gov (United States)

    Gorgees, HazimMansoor; Mahdi, FatimahAssim

    2018-05-01

    This article concerns with comparing the performance of different types of ordinary ridge regression estimators that have been already proposed to estimate the regression parameters when the near exact linear relationships among the explanatory variables is presented. For this situations we employ the data obtained from tagi gas filling company during the period (2008-2010). The main result we reached is that the method based on the condition number performs better than other methods since it has smaller mean square error (MSE) than the other stated methods.

  7. Gender effects in gaming research: a case for regression residuals?

    Science.gov (United States)

    Pfister, Roland

    2011-10-01

    Numerous recent studies have examined the impact of video gaming on various dependent variables, including the players' affective reactions, positive as well as detrimental cognitive effects, and real-world aggression. These target variables are typically analyzed as a function of game characteristics and player attributes-especially gender. However, findings on the uneven distribution of gaming experience between males and females, on the one hand, and the effect of gaming experience on several target variables, on the other hand, point at a possible confound when gaming experiments are analyzed with a standard analysis of variance. This study uses simulated data to exemplify analysis of regression residuals as a potentially beneficial data analysis strategy for such datasets. As the actual impact of gaming experience on each of the various dependent variables differs, the ultimate benefits of analysis of regression residuals entirely depend on the research question, but it offers a powerful statistical approach to video game research whenever gaming experience is a confounding factor.

  8. Panel Smooth Transition Regression Models

    DEFF Research Database (Denmark)

    González, Andrés; Terasvirta, Timo; Dijk, Dick van

    We introduce the panel smooth transition regression model. This new model is intended for characterizing heterogeneous panels, allowing the regression coefficients to vary both across individuals and over time. Specifically, heterogeneity is allowed for by assuming that these coefficients are bou...

  9. Credit Scoring Problem Based on Regression Analysis

    OpenAIRE

    Khassawneh, Bashar Suhil Jad Allah

    2014-01-01

    ABSTRACT: This thesis provides an explanatory introduction to the regression models of data mining and contains basic definitions of key terms in the linear, multiple and logistic regression models. Meanwhile, the aim of this study is to illustrate fitting models for the credit scoring problem using simple linear, multiple linear and logistic regression models and also to analyze the found model functions by statistical tools. Keywords: Data mining, linear regression, logistic regression....

  10. Does higher severity really correlate with a worse quality of life in obsessive–compulsive disorder? A meta-regression

    Directory of Open Access Journals (Sweden)

    Pozza A

    2018-04-01

    Full Text Available Andrea Pozza,1 Christine Lochner,2 Fabio Ferretti,1 Alessandro Cuomo,3 Anna Coluccia1 1Department of Medical Sciences, Surgery and Neurosciences, Santa Maria alle Scotte University Hospital of Siena, Siena, Italy; 2SU/UCT MRC Unit on Anxiety and Stress Disorders, Department of Psychiatry, Stellenbosch University, Cape Town, South Africa; 3Department of Molecular Medicine, University of Siena School of Medicine and Department of Mental Health, University of Siena Medical Center (AOUS, Siena, Italy Background: Obsessive–compulsive disorder (OCD is one of the leading causes of disability and reduced quality of life (QOL, with impairment in a number of domains. However, there is a paucity of literature on the association between severity of OCD symptoms and QOL, and the data that do exist are inconsistent. In addition, the role of severity in QOL has not been summarized as yet from a cross-generational perspective (ie, across childhood/adolescence and adulthood. Through meta-regression techniques, the current study summarized evidence about the moderator role of severity of OCD symptoms on differences in global QOL between individuals with OCD and controls. Methods: Online databases were searched, and cross-sectional case–control studies comparing participants of all ages with OCD with controls on self-report QOL measures were included. Random-effect meta-regression techniques were used to comment on the role of illness severity in global QOL in individuals with OCD. Results: Thirteen studies were included. A positive significant association emerged between OCD severity and effect sizes on global QOL: in samples with higher severity, there were narrower differences in QOL between patients with OCD and controls than in samples with lower severity. Such positive association was confirmed by a sensitivity analysis conducted on studies including only adults, where the difference in QOL ratings between patients and controls was significantly narrower

  11. Robust geographically weighted regression of modeling the Air Polluter Standard Index (APSI)

    Science.gov (United States)

    Warsito, Budi; Yasin, Hasbi; Ispriyanti, Dwi; Hoyyi, Abdul

    2018-05-01

    The Geographically Weighted Regression (GWR) model has been widely applied to many practical fields for exploring spatial heterogenity of a regression model. However, this method is inherently not robust to outliers. Outliers commonly exist in data sets and may lead to a distorted estimate of the underlying regression model. One of solution to handle the outliers in the regression model is to use the robust models. So this model was called Robust Geographically Weighted Regression (RGWR). This research aims to aid the government in the policy making process related to air pollution mitigation by developing a standard index model for air polluter (Air Polluter Standard Index - APSI) based on the RGWR approach. In this research, we also consider seven variables that are directly related to the air pollution level, which are the traffic velocity, the population density, the business center aspect, the air humidity, the wind velocity, the air temperature, and the area size of the urban forest. The best model is determined by the smallest AIC value. There are significance differences between Regression and RGWR in this case, but Basic GWR using the Gaussian kernel is the best model to modeling APSI because it has smallest AIC.

  12. Whole-body computed tomography in trauma patients: optimization of the patient scanning position significantly shortens examination time while maintaining diagnostic image quality

    Directory of Open Access Journals (Sweden)

    Hickethier T

    2018-05-01

    Full Text Available Tilman Hickethier,1,* Kamal Mammadov,1,* Bettina Baeßler,1 Thorsten Lichtenstein,1 Jochen Hinkelbein,2 Lucy Smith,3 Patrick Sven Plum,4 Seung-Hun Chon,4 David Maintz,1 De-Hua Chang1 1Department of Radiology, University Hospital of Cologne, Cologne, Germany; 2Department of Anesthesiology and Intensive Care Medicine, University Hospital of Cologne, Cologne, Germany; 3Faculty of Medicine, Memorial University of Newfoundland, St. John’s, Canada; 4Department of General, Visceral and Cancer Surgery, University Hospital of Cologne, Cologne, Germany *These authors contributed equally to this work Background: The study was conducted to compare examination time and artifact vulnerability of whole-body computed tomographies (wbCTs for trauma patients using conventional or optimized patient positioning. Patients and methods: Examination time was measured in 100 patients scanned with conventional protocol (Group A: arms positioned alongside the body for head and neck imaging and over the head for trunk imaging and 100 patients scanned with optimized protocol (Group B: arms flexed on a chest pillow without repositioning. Additionally, influence of two different scanning protocols on image quality in the most relevant body regions was assessed by two blinded readers. Results: Total wbCT duration was about 35% or 3:46 min shorter in B than in A. Artifacts in aorta (27 vs 6%, liver (40 vs 8% and spleen (27 vs 5% occurred significantly more often in B than in A. No incident of non-diagnostic image quality was reported, and no significant differences for lungs and spine were found. Conclusion: An optimized wbCT positioning protocol for trauma patients allows a significant reduction of examination time while still maintaining diagnostic image quality. Keywords: CT scan, polytrauma, acute care, time requirement, positioning

  13. A Quantile Regression Approach to Estimating the Distribution of Anesthetic Procedure Time during Induction.

    Directory of Open Access Journals (Sweden)

    Hsin-Lun Wu

    Full Text Available Although procedure time analyses are important for operating room management, it is not easy to extract useful information from clinical procedure time data. A novel approach was proposed to analyze procedure time during anesthetic induction. A two-step regression analysis was performed to explore influential factors of anesthetic induction time (AIT. Linear regression with stepwise model selection was used to select significant correlates of AIT and then quantile regression was employed to illustrate the dynamic relationships between AIT and selected variables at distinct quantiles. A total of 1,060 patients were analyzed. The first and second-year residents (R1-R2 required longer AIT than the third and fourth-year residents and attending anesthesiologists (p = 0.006. Factors prolonging AIT included American Society of Anesthesiologist physical status ≧ III, arterial, central venous and epidural catheterization, and use of bronchoscopy. Presence of surgeon before induction would decrease AIT (p < 0.001. Types of surgery also had significant influence on AIT. Quantile regression satisfactorily estimated extra time needed to complete induction for each influential factor at distinct quantiles. Our analysis on AIT demonstrated the benefit of quantile regression analysis to provide more comprehensive view of the relationships between procedure time and related factors. This novel two-step regression approach has potential applications to procedure time analysis in operating room management.

  14. Unbalanced Regressions and the Predictive Equation

    DEFF Research Database (Denmark)

    Osterrieder, Daniela; Ventosa-Santaulària, Daniel; Vera-Valdés, J. Eduardo

    Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness in the theoreti......Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness...... in the theoretical predictive equation by suggesting a data generating process, where returns are generated as linear functions of a lagged latent I(0) risk process. The observed predictor is a function of this latent I(0) process, but it is corrupted by a fractionally integrated noise. Such a process may arise due...... to aggregation or unexpected level shifts. In this setup, the practitioner estimates a misspecified, unbalanced, and endogenous predictive regression. We show that the OLS estimate of this regression is inconsistent, but standard inference is possible. To obtain a consistent slope estimate, we then suggest...

  15. [From clinical judgment to linear regression model.

    Science.gov (United States)

    Palacios-Cruz, Lino; Pérez, Marcela; Rivas-Ruiz, Rodolfo; Talavera, Juan O

    2013-01-01

    When we think about mathematical models, such as linear regression model, we think that these terms are only used by those engaged in research, a notion that is far from the truth. Legendre described the first mathematical model in 1805, and Galton introduced the formal term in 1886. Linear regression is one of the most commonly used regression models in clinical practice. It is useful to predict or show the relationship between two or more variables as long as the dependent variable is quantitative and has normal distribution. Stated in another way, the regression is used to predict a measure based on the knowledge of at least one other variable. Linear regression has as it's first objective to determine the slope or inclination of the regression line: Y = a + bx, where "a" is the intercept or regression constant and it is equivalent to "Y" value when "X" equals 0 and "b" (also called slope) indicates the increase or decrease that occurs when the variable "x" increases or decreases in one unit. In the regression line, "b" is called regression coefficient. The coefficient of determination (R 2 ) indicates the importance of independent variables in the outcome.

  16. Autistic Regression

    Science.gov (United States)

    Matson, Johnny L.; Kozlowski, Alison M.

    2010-01-01

    Autistic regression is one of the many mysteries in the developmental course of autism and pervasive developmental disorders not otherwise specified (PDD-NOS). Various definitions of this phenomenon have been used, further clouding the study of the topic. Despite this problem, some efforts at establishing prevalence have been made. The purpose of…

  17. Ridge regression estimator: combining unbiased and ordinary ridge regression methods of estimation

    Directory of Open Access Journals (Sweden)

    Sharad Damodar Gore

    2009-10-01

    Full Text Available Statistical literature has several methods for coping with multicollinearity. This paper introduces a new shrinkage estimator, called modified unbiased ridge (MUR. This estimator is obtained from unbiased ridge regression (URR in the same way that ordinary ridge regression (ORR is obtained from ordinary least squares (OLS. Properties of MUR are derived. Results on its matrix mean squared error (MMSE are obtained. MUR is compared with ORR and URR in terms of MMSE. These results are illustrated with an example based on data generated by Hoerl and Kennard (1975.

  18. Regression of oral lichenoid lesions after replacement of dental restorations.

    Science.gov (United States)

    Mårell, L; Tillberg, A; Widman, L; Bergdahl, J; Berglund, A

    2014-05-01

    The aim of the study was to determine the prognosis and to evaluate the regression of lichenoid contact reactions (LCR) and oral lichen planus (OLP) after replacement of dental restorative materials suspected as causing the lesions. Forty-four referred patients with oral lesions participated in a follow-up study that was initiated an average of 6 years after the first examination at the Department of Odontology, i.e. the baseline examination. The patients underwent odontological clinical examination and answered a questionnaire with questions regarding dental health, medical and psychological health, and treatments undertaken from baseline to follow-up. After exchange of dental materials, regression of oral lesions was significantly higher among patients with LCR than with OLP. As no cases with OLP regressed after an exchange of materials, a proper diagnosis has to be made to avoid unnecessary exchanges of intact restorations on patients with OLP.

  19. [Application of detecting and taking overdispersion into account in Poisson regression model].

    Science.gov (United States)

    Bouche, G; Lepage, B; Migeot, V; Ingrand, P

    2009-08-01

    Researchers often use the Poisson regression model to analyze count data. Overdispersion can occur when a Poisson regression model is used, resulting in an underestimation of variance of the regression model parameters. Our objective was to take overdispersion into account and assess its impact with an illustration based on the data of a study investigating the relationship between use of the Internet to seek health information and number of primary care consultations. Three methods, overdispersed Poisson, a robust estimator, and negative binomial regression, were performed to take overdispersion into account in explaining variation in the number (Y) of primary care consultations. We tested overdispersion in the Poisson regression model using the ratio of the sum of Pearson residuals over the number of degrees of freedom (chi(2)/df). We then fitted the three models and compared parameter estimation to the estimations given by Poisson regression model. Variance of the number of primary care consultations (Var[Y]=21.03) was greater than the mean (E[Y]=5.93) and the chi(2)/df ratio was 3.26, which confirmed overdispersion. Standard errors of the parameters varied greatly between the Poisson regression model and the three other regression models. Interpretation of estimates from two variables (using the Internet to seek health information and single parent family) would have changed according to the model retained, with significant levels of 0.06 and 0.002 (Poisson), 0.29 and 0.09 (overdispersed Poisson), 0.29 and 0.13 (use of a robust estimator) and 0.45 and 0.13 (negative binomial) respectively. Different methods exist to solve the problem of underestimating variance in the Poisson regression model when overdispersion is present. The negative binomial regression model seems to be particularly accurate because of its theorical distribution ; in addition this regression is easy to perform with ordinary statistical software packages.

  20. Detecting overdispersion in count data: A zero-inflated Poisson regression analysis

    Science.gov (United States)

    Afiqah Muhamad Jamil, Siti; Asrul Affendi Abdullah, M.; Kek, Sie Long; Nor, Maria Elena; Mohamed, Maryati; Ismail, Norradihah

    2017-09-01

    This study focusing on analysing count data of butterflies communities in Jasin, Melaka. In analysing count dependent variable, the Poisson regression model has been known as a benchmark model for regression analysis. Continuing from the previous literature that used Poisson regression analysis, this study comprising the used of zero-inflated Poisson (ZIP) regression analysis to gain acute precision on analysing the count data of butterfly communities in Jasin, Melaka. On the other hands, Poisson regression should be abandoned in the favour of count data models, which are capable of taking into account the extra zeros explicitly. By far, one of the most popular models include ZIP regression model. The data of butterfly communities which had been called as the number of subjects in this study had been taken in Jasin, Melaka and consisted of 131 number of subjects visits Jasin, Melaka. Since the researchers are considering the number of subjects, this data set consists of five families of butterfly and represent the five variables involve in the analysis which are the types of subjects. Besides, the analysis of ZIP used the SAS procedure of overdispersion in analysing zeros value and the main purpose of continuing the previous study is to compare which models would be better than when exists zero values for the observation of the count data. The analysis used AIC, BIC and Voung test of 5% level significance in order to achieve the objectives. The finding indicates that there is a presence of over-dispersion in analysing zero value. The ZIP regression model is better than Poisson regression model when zero values exist.

  1. Discriminative Elastic-Net Regularized Linear Regression.

    Science.gov (United States)

    Zhang, Zheng; Lai, Zhihui; Xu, Yong; Shao, Ling; Wu, Jian; Xie, Guo-Sen

    2017-03-01

    In this paper, we aim at learning compact and discriminative linear regression models. Linear regression has been widely used in different problems. However, most of the existing linear regression methods exploit the conventional zero-one matrix as the regression targets, which greatly narrows the flexibility of the regression model. Another major limitation of these methods is that the learned projection matrix fails to precisely project the image features to the target space due to their weak discriminative capability. To this end, we present an elastic-net regularized linear regression (ENLR) framework, and develop two robust linear regression models which possess the following special characteristics. First, our methods exploit two particular strategies to enlarge the margins of different classes by relaxing the strict binary targets into a more feasible variable matrix. Second, a robust elastic-net regularization of singular values is introduced to enhance the compactness and effectiveness of the learned projection matrix. Third, the resulting optimization problem of ENLR has a closed-form solution in each iteration, which can be solved efficiently. Finally, rather than directly exploiting the projection matrix for recognition, our methods employ the transformed features as the new discriminate representations to make final image classification. Compared with the traditional linear regression model and some of its variants, our method is much more accurate in image classification. Extensive experiments conducted on publicly available data sets well demonstrate that the proposed framework can outperform the state-of-the-art methods. The MATLAB codes of our methods can be available at http://www.yongxu.org/lunwen.html.

  2. A generalized right truncated bivariate Poisson regression model with applications to health data.

    Science.gov (United States)

    Islam, M Ataharul; Chowdhury, Rafiqul I

    2017-01-01

    A generalized right truncated bivariate Poisson regression model is proposed in this paper. Estimation and tests for goodness of fit and over or under dispersion are illustrated for both untruncated and right truncated bivariate Poisson regression models using marginal-conditional approach. Estimation and test procedures are illustrated for bivariate Poisson regression models with applications to Health and Retirement Study data on number of health conditions and the number of health care services utilized. The proposed test statistics are easy to compute and it is evident from the results that the models fit the data very well. A comparison between the right truncated and untruncated bivariate Poisson regression models using the test for nonnested models clearly shows that the truncated model performs significantly better than the untruncated model.

  3. Antecedents of positive self-disclosure online: an empirical study of US college students’ Facebook usage

    Directory of Open Access Journals (Sweden)

    Chen H

    2017-05-01

    Full Text Available Hongliang Chen Department of Communication, Texas A&M University, College Station, TX, USA Abstract: This study investigates the factors predicting positive self-disclosure on social networking sites (SNSs. There is a formidable body of empirical research relating to online self-disclosure, but very few studies have assessed the antecedents of positive self-disclosure. To address this literature gap, the current study tests the effects of self-esteem, life satisfaction, social anxiety, privacy concerns, public self-consciousness (SC, and perceived collectivism on positive self-disclosure on SNSs. Data were collected online via Qualtrics in April 2013. Respondents were undergraduate students from the University of Connecticut. Using ordinary least squares regression, the current study found that self-esteem and perceived collectivism increased positive self-disclosure, life satisfaction, and privacy concerns decreased positive self-disclosure, and the effects of social anxiety and public SC were not significant. Keywords: positive self-disclosure, self-esteem, life satisfaction, social anxiety, privacy concerns, public self-consciousness, perceived collectivism

  4. Clinicopathologic Risk Factor Distributions for MLH1 Promoter Region Methylation in CIMP-Positive Tumors.

    Science.gov (United States)

    Levine, A Joan; Phipps, Amanda I; Baron, John A; Buchanan, Daniel D; Ahnen, Dennis J; Cohen, Stacey A; Lindor, Noralane M; Newcomb, Polly A; Rosty, Christophe; Haile, Robert W; Laird, Peter W; Weisenberger, Daniel J

    2016-01-01

    The CpG island methylator phenotype (CIMP) is a major molecular pathway in colorectal cancer. Approximately 25% to 60% of CIMP tumors are microsatellite unstable (MSI-H) due to DNA hypermethylation of the MLH1 gene promoter. Our aim was to determine if the distributions of clinicopathologic factors in CIMP-positive tumors with MLH1 DNA methylation differed from those in CIMP-positive tumors without DNA methylation of MLH1. We assessed the associations between age, sex, tumor-site, MSI status BRAF and KRAS mutations, and family colorectal cancer history with MLH1 methylation status in a large population-based sample of CIMP-positive colorectal cancers defined by a 5-marker panel using unconditional logistic regression to assess the odds of MLH1 methylation by study variables. Subjects with CIMP-positive tumors without MLH1 methylation were significantly younger, more likely to be male, and more likely to have distal colon or rectal primaries and the MSI-L phenotype. CIMP-positive MLH1-unmethylated tumors were significantly less likely than CIMP-positive MLH1-methylated tumors to harbor a BRAF V600E mutation and significantly more likely to harbor a KRAS mutation. MLH1 methylation was associated with significantly better overall survival (HR, 0.50; 95% confidence interval, 0.31-0.82). These data suggest that MLH1 methylation in CIMP-positive tumors is not a completely random event and implies that there are environmental or genetic determinants that modify the probability that MLH1 will become methylated during CIMP pathogenesis. MLH1 DNA methylation status should be taken into account in etiologic studies. ©2015 American Association for Cancer Research.

  5. Clinicopathological risk factor distributions for MLH1 promoter region methylation in CIMP positive tumors

    Science.gov (United States)

    Levine, A. Joan; Phipps, Amanda I.; Baron, John A.; Buchanan, Daniel D.; Ahnen, Dennis J.; Cohen, Stacey A.; Lindor, Noralane M.; Newcomb, Polly A.; Rosty, Christophe; Haile, Robert W.; Laird, Peter W.; Weisenberger, Daniel J.

    2015-01-01

    Background The CpG Island Methylator Phenotype (CIMP) is a major molecular pathway in colorectal cancer (CRC). Approximately 25% to 60% of CIMP tumors are microsatellite unstable (MSI-H) due to DNA hypermethylation of the MLH1 gene promoter. Our aim was to determine if the distributions of clinicopathologic factors in CIMP-positive tumors with MLH1 DNA methylation differed from those in CIMP-positive tumors without DNA methylation of MLH1. Methods We assessed the associations between age, sex, tumor-site, MSI status BRAF and KRAS mutations and family CRC history with MLH1 methylation status in a large population-based sample of CIMP-positive CRCs defined by a 5-marker panel using unconditional logistic regression to assess the odds of MLH1 methylation by study variables. Results Subjects with CIMP-positive tumors without MLH1 methylation were significantly younger, more likely to be male, more likely to have distal colon or rectal primaries and the MSI-L phenotype. CIMP-positive MLH1-unmethylated tumors were significantly less likely than CIMP-positive MLH1-methylated tumors to harbor a BRAF V600E mutation and significantly more likely to harbor a KRAS mutation. MLH1 methylation was associated with significantly better overall survival (HR=0.50; 95% Confidence Interval (0.31, 0.82)). Conclusions These data suggest that MLH1 methylation in CIMP-positive tumors is not a completely random event and implies that there are environmental or genetic determinants that modify the probability that MLH1 will become methylated during CIMP pathogenesis. Impact MLH1 DNA methylation status should be taken into account in etiologic studies. PMID:26512054

  6. Lateral decubitus position vs. lithotomy position: which is the best way to minimize patient’s pain perception during transrectal prostate biopsy?

    Directory of Open Access Journals (Sweden)

    Phil Hyun Song

    Full Text Available ABSTRACT Introduction Considering the distinctive nature in terms of psychological stress and anal tone of position which is generally selected between lithotomy and left lateral decubitus (LLD, we postulated its effect on pain perception during biopsy, and investigated their association. Materials and Methods A prospective study for comparison of two biopsy positions which were perform in a different working day was conducted for 208 men (lithotomy position=86, LLD=122. The decision on the position was made solely based on the patient’s preference for the biopsy day, and all procedures were performed according to the identical protocol (12-core biopsy with intrarectal lidocaine gel, probe, and needle. The maximal degree of pain during the entire process was assessed using a visual analogue scale (VAS, immediately after biopsy. After propensity matching, a total of 152 patients were finally selected (lithotomy group=76, LLD=76, then peri-biopsy parameters were compared. Results Between groups, no differences were observed across all variables including age, obesity, prostate volume, serum PSA, international prostate symptom score, and cancer detection rate, except mean (±standard deviation VAS score (3.89±2.01 vs. 4.58±2.22, p=0.049. VAS score showed significant association solely with patient’s position (Pearson’s coefficient=-0.165, p=0.042. In multiple linear regression models regarding the effect of clinical variables on VAS score, patient position was a single independent predictor favoring lithotomy position to decrease perceived pain (B=-0.928, p=0.024. Conclusions These data suggest lithotomy position as a proper way to perform transrectal prostate biopsy with routine use of topical lidocaine gel in comparison with conventional LLD position.

  7. Trend Estimation and Regression Analysis in Climatological Time Series: An Application of Structural Time Series Models and the Kalman Filter.

    Science.gov (United States)

    Visser, H.; Molenaar, J.

    1995-05-01

    The detection of trends in climatological data has become central to the discussion on climate change due to the enhanced greenhouse effect. To prove detection, a method is needed (i) to make inferences on significant rises or declines in trends, (ii) to take into account natural variability in climate series, and (iii) to compare output from GCMs with the trends in observed climate data. To meet these requirements, flexible mathematical tools are needed. A structural time series model is proposed with which a stochastic trend, a deterministic trend, and regression coefficients can be estimated simultaneously. The stochastic trend component is described using the class of ARIMA models. The regression component is assumed to be linear. However, the regression coefficients corresponding with the explanatory variables may be time dependent to validate this assumption. The mathematical technique used to estimate this trend-regression model is the Kaiman filter. The main features of the filter are discussed.Examples of trend estimation are given using annual mean temperatures at a single station in the Netherlands (1706-1990) and annual mean temperatures at Northern Hemisphere land stations (1851-1990). The inclusion of explanatory variables is shown by regressing the latter temperature series on four variables: Southern Oscillation index (SOI), volcanic dust index (VDI), sunspot numbers (SSN), and a simulated temperature signal, induced by increasing greenhouse gases (GHG). In all analyses, the influence of SSN on global temperatures is found to be negligible. The correlations between temperatures and SOI and VDI appear to be negative. For SOI, this correlation is significant, but for VDI it is not, probably because of a lack of volcanic eruptions during the sample period. The relation between temperatures and GHG is positive, which is in agreement with the hypothesis of a warming climate because of increasing levels of greenhouse gases. The prediction performance of

  8. Categorical regression dose-response modeling

    Science.gov (United States)

    The goal of this training is to provide participants with training on the use of the U.S. EPA’s Categorical Regression soft¬ware (CatReg) and its application to risk assessment. Categorical regression fits mathematical models to toxicity data that have been assigned ord...

  9. Length bias correction in gene ontology enrichment analysis using logistic regression.

    Science.gov (United States)

    Mi, Gu; Di, Yanming; Emerson, Sarah; Cumbie, Jason S; Chang, Jeff H

    2012-01-01

    When assessing differential gene expression from RNA sequencing data, commonly used statistical tests tend to have greater power to detect differential expression of genes encoding longer transcripts. This phenomenon, called "length bias", will influence subsequent analyses such as Gene Ontology enrichment analysis. In the presence of length bias, Gene Ontology categories that include longer genes are more likely to be identified as enriched. These categories, however, are not necessarily biologically more relevant. We show that one can effectively adjust for length bias in Gene Ontology analysis by including transcript length as a covariate in a logistic regression model. The logistic regression model makes the statistical issue underlying length bias more transparent: transcript length becomes a confounding factor when it correlates with both the Gene Ontology membership and the significance of the differential expression test. The inclusion of the transcript length as a covariate allows one to investigate the direct correlation between the Gene Ontology membership and the significance of testing differential expression, conditional on the transcript length. We present both real and simulated data examples to show that the logistic regression approach is simple, effective, and flexible.

  10. Multinomial logistic regression in workers' health

    Science.gov (United States)

    Grilo, Luís M.; Grilo, Helena L.; Gonçalves, Sónia P.; Junça, Ana

    2017-11-01

    In European countries, namely in Portugal, it is common to hear some people mentioning that they are exposed to excessive and continuous psychosocial stressors at work. This is increasing in diverse activity sectors, such as, the Services sector. A representative sample was collected from a Portuguese Services' organization, by applying a survey (internationally validated), which variables were measured in five ordered categories in Likert-type scale. A multinomial logistic regression model is used to estimate the probability of each category of the dependent variable general health perception where, among other independent variables, burnout appear as statistically significant.

  11. Abstract Expression Grammar Symbolic Regression

    Science.gov (United States)

    Korns, Michael F.

    This chapter examines the use of Abstract Expression Grammars to perform the entire Symbolic Regression process without the use of Genetic Programming per se. The techniques explored produce a symbolic regression engine which has absolutely no bloat, which allows total user control of the search space and output formulas, which is faster, and more accurate than the engines produced in our previous papers using Genetic Programming. The genome is an all vector structure with four chromosomes plus additional epigenetic and constraint vectors, allowing total user control of the search space and the final output formulas. A combination of specialized compiler techniques, genetic algorithms, particle swarm, aged layered populations, plus discrete and continuous differential evolution are used to produce an improved symbolic regression sytem. Nine base test cases, from the literature, are used to test the improvement in speed and accuracy. The improved results indicate that these techniques move us a big step closer toward future industrial strength symbolic regression systems.

  12. The study of logistic regression of risk factor on the death cause of uranium miners

    International Nuclear Information System (INIS)

    Wen Jinai; Yuan Liyun; Jiang Ruyi

    1999-01-01

    Logistic regression model has widely been used in the field of medicine. The computer software on this model is popular, but it is worth to discuss how to use this model correctly. Using SPSS (Statistical Package for the Social Science) software, unconditional logistic regression method was adopted to carry out multi-factor analyses on the cause of total death, cancer death and lung cancer death of uranium miners. The data is from radioepidemiological database of one uranium mine. The result show that attained age is a risk factor in the logistic regression analyses of total death, cancer death and lung cancer death. In the logistic regression analysis of cancer death, there is a negative correlation between the age of exposure and cancer death. This shows that the younger the age at exposure, the bigger the risk of cancer death. In the logistic regression analysis of lung cancer death, there is a positive correlation between the cumulated exposure and lung cancer death, this show that cumulated exposure is a most important risk factor of lung cancer death on uranium miners. It has been documented by many foreign reports that the lung cancer death rate is higher in uranium miners

  13. Michael Jackson, Bin Laden and I: functions of positive and negative, public and private flashbulb memories.

    Science.gov (United States)

    Demiray, Burcu; Freund, Alexandra M

    2015-01-01

    This study examined the perceived psychosocial functions of flashbulb memories: It compared positive and negative public flashbulb memories (positive: Bin Laden's death, negative: Michael Jackson's death) with private ones (positive: pregnancy, negative: death of a loved one). A sample of n = 389 young and n = 176 middle-aged adults answered canonical category questions used to identify flashbulb memories and rated the personal significance, the psychological temporal distance, and the functions of each memory (i.e., self-continuity, social-boding, directive functions). Hierarchical regressions showed that, in general, private memories were rated more functional than public memories. Positive and negative private memories were comparable in self-continuity and directionality, but the positive private memory more strongly served social functions. In line with the positivity bias in autobiographical memory, positive flashbulb memories felt psychologically closer than negative ones. Finally, middle-aged adults rated their memories as less functional regarding self-continuity and social-bonding than young adults. Results are discussed regarding the tripartite model of autobiographical memory functions.

  14. Clinical significance of coryneform Gram-positive rods from blood identified by MALDI-TOF mass spectrometry and their susceptibility profiles - a retrospective chart review.

    Science.gov (United States)

    Mushtaq, Ammara; Chen, Derrick J; Strand, Gregory J; Dylla, Brenda L; Cole, Nicolynn C; Mandrekar, Jayawant; Patel, Robin

    2016-07-01

    With the advent of matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS), most Gram-positive rods (GPRs) are readily identified; however, their clinical relevance in blood cultures remains unclear. Herein, we assessed the clinical significance of GPRs isolated from blood and identified in the era of MALDI-TOF MS. A retrospective chart review of patients presenting to the Mayo Clinic, Rochester, MN, from January 1, 2013, to October 13, 2015, was performed. Any episode of a positive blood culture for a GPR was included. We assessed the number of bottles positive for a given isolate, time to positivity of blood cultures, patient age, medical history, interpretation of culture results by the healthcare team and whether infectious diseases consultation was obtained. We also evaluated the susceptibility profiles of a larger collection of GPRs tested in the clinical microbiology laboratory of the Mayo Clinic, Rochester, MN from January 1, 2013, to October 31, 2015. There were a total of 246 GPRs isolated from the blood of 181 patients during the study period. 56% (n = 101) were deemed contaminants by the healthcare team and were not treated; 33% (n = 59) were clinically determined to represent true bacteremia and were treated; and 8% (n = 14) were considered of uncertain significance, with patients prescribed treatment regardless. Patient characteristics associated with an isolate being treated on univariate analysis included younger age (P = 0.02), identification to the species level (P = 0.02), higher number of positive blood culture sets (P < 0.0001), lower time to positivity (P < 0.0001), immunosuppression (P = 0.03), and recommendation made by an infectious disease consultant (P = 0.0005). On multivariable analysis, infectious diseases consultation (P = 0.03), higher number of positive blood culture sets (P = 0.0005) and lower time to positivity (P = 0.03) were associated with an isolate being treated. 100, 83, 48 and 34% of GPRs

  15. Regression Analysis for Multivariate Dependent Count Data Using Convolved Gaussian Processes

    OpenAIRE

    Sofro, A'yunin; Shi, Jian Qing; Cao, Chunzheng

    2017-01-01

    Research on Poisson regression analysis for dependent data has been developed rapidly in the last decade. One of difficult problems in a multivariate case is how to construct a cross-correlation structure and at the meantime make sure that the covariance matrix is positive definite. To address the issue, we propose to use convolved Gaussian process (CGP) in this paper. The approach provides a semi-parametric model and offers a natural framework for modeling common mean structure and covarianc...

  16. Eyeball Position in Facial Approximation: Accuracy of Methods for Predicting Globe Positioning in Lateral View.

    Science.gov (United States)

    Zednikova Mala, Pavla; Veleminska, Jana

    2018-01-01

    This study measured the accuracy of traditional and validated newly proposed methods for globe positioning in lateral view. Eighty lateral head cephalograms of adult subjects from Central Europe were taken, and the actual and predicted dimensions were compared. The anteroposterior eyeball position was estimated as the most accurate method based on the proportion of the orbital height (SEE = 1.9 mm) and was followed by the "tangent to the iris method" showing SEE = 2.4 mm. The traditional "tangent to the cornea method" underestimated the eyeball projection by SEE = 5.8 mm. Concerning the superoinferior eyeball position, the results showed a deviation from a central to a more superior position by 0.3 mm, on average, and the traditional method of central positioning of the globe could not be rejected as inaccurate (SEE = 0.3 mm). Based on regression analyzes or proportionality of the orbital height, the SEE = 2.1 mm. © 2017 American Academy of Forensic Sciences.

  17. Analysis of designed experiments by stabilised PLS Regression and jack-knifing

    DEFF Research Database (Denmark)

    Martens, Harald; Høy, M.; Westad, F.

    2001-01-01

    Pragmatical, visually oriented methods for assessing and optimising bi-linear regression models are described, and applied to PLS Regression (PLSR) analysis of multi-response data from controlled experiments. The paper outlines some ways to stabilise the PLSR method to extend its range...... the reliability of the linear and bi-linear model parameter estimates. The paper illustrates how the obtained PLSR "significance" probabilities are similar to those from conventional factorial ANOVA, but the PLSR is shown to give important additional overview plots of the main relevant structures in the multi....... An Introduction, Wiley, Chichester, UK, 2001]....

  18. Comparison of Classical Linear Regression and Orthogonal Regression According to the Sum of Squares Perpendicular Distances

    OpenAIRE

    KELEŞ, Taliha; ALTUN, Murat

    2016-01-01

    Regression analysis is a statistical technique for investigating and modeling the relationship between variables. The purpose of this study was the trivial presentation of the equation for orthogonal regression (OR) and the comparison of classical linear regression (CLR) and OR techniques with respect to the sum of squared perpendicular distances. For that purpose, the analyses were shown by an example. It was found that the sum of squared perpendicular distances of OR is smaller. Thus, it wa...

  19. Predictors for benign paroxysmal positional vertigo with positive Dix–Hallpike test

    Directory of Open Access Journals (Sweden)

    Noda K

    2011-12-01

    Full Text Available Kazutaka Noda, Masatomi Ikusaka, Yoshiyuki Ohira, Toshihiko Takada, Tomoko TsukamotoDepartment of General Medicine, Chiba University Hospital, Chiba, JapanObjective: Patient medical history is important for making a diagnosis of causes of dizziness, but there have been no studies on the diagnostic value of individual items in the history. This study was performed to identify and validate useful questions for suspecting a diagnosis of benign paroxysmal positional vertigo (BPPV.Methods: Construction and validation of a disease prediction model was performed at the outpatient clinic in the Department of General Medicine of Chiba University Hospital. Patients with dizziness were enrolled (145 patients for construction of the disease prediction model and 61 patients for its validation. This study targeted BPPV of the posterior semicircular canals only with a positive Dix–Hallpike test (DHT + BPPV to avoid diagnostic ambiguity. Binomial logistic regression analysis was performed to identify the items that were useful for diagnosis or exclusion of DHT + BPPV.Results: Twelve patients from the derivation set and six patients from the validation set had DHT + BPPV. Binomial logistic regression analysis selected a "duration of dizziness ≤15 seconds" and "onset when turning over in bed" as independent predictors of DHT + BPPV with an odds ratio (95% confidence interval of 4.36 (1.18–16.19 and 10.17 (2.49–41.63, respectively. Affirmative answers to both questions yielded a likelihood ratio of 6.81 (5.11–9.10 for diagnosis of DHT + BPPV, while negative answers to both had a likelihood ratio of 0.19 (0.08–0.47.Conclusion: A "duration of dizziness ≤15 seconds" and "onset when turning over in bed" were the two most important questions among various historical features of BPPV.Keywords: benign paroxysmal positional vertigo, likelihood ratio, diagnosis, screening, prediction rules

  20. Temporal trends in sperm count: a systematic review and meta-regression analysis.

    Science.gov (United States)

    Levine, Hagai; Jørgensen, Niels; Martino-Andrade, Anderson; Mendiola, Jaime; Weksler-Derri, Dan; Mindlis, Irina; Pinotti, Rachel; Swan, Shanna H

    2017-11-01

    Reported declines in sperm counts remain controversial today and recent trends are unknown. A definitive meta-analysis is critical given the predictive value of sperm count for fertility, morbidity and mortality. To provide a systematic review and meta-regression analysis of recent trends in sperm counts as measured by sperm concentration (SC) and total sperm count (TSC), and their modification by fertility and geographic group. PubMed/MEDLINE and EMBASE were searched for English language studies of human SC published in 1981-2013. Following a predefined protocol 7518 abstracts were screened and 2510 full articles reporting primary data on SC were reviewed. A total of 244 estimates of SC and TSC from 185 studies of 42 935 men who provided semen samples in 1973-2011 were extracted for meta-regression analysis, as well as information on years of sample collection and covariates [fertility group ('Unselected by fertility' versus 'Fertile'), geographic group ('Western', including North America, Europe Australia and New Zealand versus 'Other', including South America, Asia and Africa), age, ejaculation abstinence time, semen collection method, method of measuring SC and semen volume, exclusion criteria and indicators of completeness of covariate data]. The slopes of SC and TSC were estimated as functions of sample collection year using both simple linear regression and weighted meta-regression models and the latter were adjusted for pre-determined covariates and modification by fertility and geographic group. Assumptions were examined using multiple sensitivity analyses and nonlinear models. SC declined significantly between 1973 and 2011 (slope in unadjusted simple regression models -0.70 million/ml/year; 95% CI: -0.72 to -0.69; P regression analysis reports a significant decline in sperm counts (as measured by SC and TSC) between 1973 and 2011, driven by a 50-60% decline among men unselected by fertility from North America, Europe, Australia and New Zealand. Because

  1. Pathological assessment of liver fibrosis regression

    Directory of Open Access Journals (Sweden)

    WANG Bingqiong

    2017-03-01

    Full Text Available Hepatic fibrosis is the common pathological outcome of chronic hepatic diseases. An accurate assessment of fibrosis degree provides an important reference for a definite diagnosis of diseases, treatment decision-making, treatment outcome monitoring, and prognostic evaluation. At present, many clinical studies have proven that regression of hepatic fibrosis and early-stage liver cirrhosis can be achieved by effective treatment, and a correct evaluation of fibrosis regression has become a hot topic in clinical research. Liver biopsy has long been regarded as the gold standard for the assessment of hepatic fibrosis, and thus it plays an important role in the evaluation of fibrosis regression. This article reviews the clinical application of current pathological staging systems in the evaluation of fibrosis regression from the perspectives of semi-quantitative scoring system, quantitative approach, and qualitative approach, in order to propose a better pathological evaluation system for the assessment of fibrosis regression.

  2. Positive mental health among health professionals working at a psychiatric hospital.

    Directory of Open Access Journals (Sweden)

    Louisa Picco

    Full Text Available Positive mental health (PMH is a combination of emotional, psychological and social well-being that is necessary for an individual to be mentally healthy. The current study aims to examine the socio-demographic differences of PMH among mental health professionals and to explore the association between job satisfaction and total PMH.Doctors, nurses and allied health staff (n = 462 completed the online survey which included the multidimensional 47-item PMH instrument as well as a single item job satisfaction question. Associations of PMH with job satisfaction were investigated via linear regression models.Significant differences in PMH total and domain specific scores were observed across socio-demographic characteristics. Age and ethnicity were significantly correlated with PMH total scores as well as various domain scores, while gender, marital and residency status and the staff's position were only significantly correlated with domain specific scores. Job satisfaction was also found to be a significantly associated with total PMH.The workplace is a key environment that affects the mental health and well-being of working adults. In order to promote and foster PMH, workplaces need to consider the importance of psychosocial well-being and the wellness of staff whilst providing an environment that supports and maintains overall health and work efficiency.

  3. Logistic Regression: Concept and Application

    Science.gov (United States)

    Cokluk, Omay

    2010-01-01

    The main focus of logistic regression analysis is classification of individuals in different groups. The aim of the present study is to explain basic concepts and processes of binary logistic regression analysis intended to determine the combination of independent variables which best explain the membership in certain groups called dichotomous…

  4. Phylogeny, ecology, and heart position in snakes

    DEFF Research Database (Denmark)

    Gartner, Gabriel E.A.; Hicks, James W.; Manzani, Paulo R.

    2010-01-01

    both conventional and phylogenetically based statistical methods. General linear models regressing log10 snout‐heart position on log10 snout‐vent length (SVL), as well as dummy variables coding for habitat and/or clade, were compared using likelihood ratio tests and the Akaike Information Criterion...

  5. Predictors of course in obsessive-compulsive disorder: logistic regression versus Cox regression for recurrent events.

    Science.gov (United States)

    Kempe, P T; van Oppen, P; de Haan, E; Twisk, J W R; Sluis, A; Smit, J H; van Dyck, R; van Balkom, A J L M

    2007-09-01

    Two methods for predicting remissions in obsessive-compulsive disorder (OCD) treatment are evaluated. Y-BOCS measurements of 88 patients with a primary OCD (DSM-III-R) diagnosis were performed over a 16-week treatment period, and during three follow-ups. Remission at any measurement was defined as a Y-BOCS score lower than thirteen combined with a reduction of seven points when compared with baseline. Logistic regression models were compared with a Cox regression for recurrent events model. Logistic regression yielded different models at different evaluation times. The recurrent events model remained stable when fewer measurements were used. Higher baseline levels of neuroticism and more severe OCD symptoms were associated with a lower chance of remission, early age of onset and more depressive symptoms with a higher chance. Choice of outcome time affects logistic regression prediction models. Recurrent events analysis uses all information on remissions and relapses. Short- and long-term predictors for OCD remission show overlap.

  6. Positive emotion communication: Fostering well-being at end of life.

    Science.gov (United States)

    Terrill, Alexandra L; Ellington, Lee; John, Kevin K; Latimer, Seth; Xu, Jiayun; Reblin, Maija; Clayton, Margaret F

    2018-04-01

    Little is known about positive emotion communication (PEC) in end-of-life care. This study aims to identify types and patterns of PEC among hospice nurses, caregivers, and patients. A coding system based on positive psychology theory was applied as a secondary analysis to audio recordings of hospice nurse home visits with cancer patients and family caregivers, collected as part of a prospective longitudinal study. Eighty recordings (4 visits from 20 triads) were coded for humor, connection, praise, positive focus, gratitude, taking joy/savoring, and perfunctory statements. Descriptive statistics revealed the greatest proportion of PEC was made by nurses. Humor was most frequently used across all speakers. Cluster analysis revealed four PEC visit types: Savor/Take Joy; Humor; Perfunctory; and Other-focused Expressions of Positive Emotions. Linear mixed effect regression was used to estimate the trajectory of PEC over time, but no significant change was found. We found that positive emotions are common in nurse, caregiver and patient communication at end-of-life and do not decline closer to death. This study is among the first to explore PEC at end-of-life, and offers a way to bring strengths-based approaches into end of life communication research. Copyright © 2017 Elsevier B.V. All rights reserved.

  7. Sparse reduced-rank regression with covariance estimation

    KAUST Repository

    Chen, Lisha

    2014-12-08

    Improving the predicting performance of the multiple response regression compared with separate linear regressions is a challenging question. On the one hand, it is desirable to seek model parsimony when facing a large number of parameters. On the other hand, for certain applications it is necessary to take into account the general covariance structure for the errors of the regression model. We assume a reduced-rank regression model and work with the likelihood function with general error covariance to achieve both objectives. In addition we propose to select relevant variables for reduced-rank regression by using a sparsity-inducing penalty, and to estimate the error covariance matrix simultaneously by using a similar penalty on the precision matrix. We develop a numerical algorithm to solve the penalized regression problem. In a simulation study and real data analysis, the new method is compared with two recent methods for multivariate regression and exhibits competitive performance in prediction and variable selection.

  8. Sparse reduced-rank regression with covariance estimation

    KAUST Repository

    Chen, Lisha; Huang, Jianhua Z.

    2014-01-01

    Improving the predicting performance of the multiple response regression compared with separate linear regressions is a challenging question. On the one hand, it is desirable to seek model parsimony when facing a large number of parameters. On the other hand, for certain applications it is necessary to take into account the general covariance structure for the errors of the regression model. We assume a reduced-rank regression model and work with the likelihood function with general error covariance to achieve both objectives. In addition we propose to select relevant variables for reduced-rank regression by using a sparsity-inducing penalty, and to estimate the error covariance matrix simultaneously by using a similar penalty on the precision matrix. We develop a numerical algorithm to solve the penalized regression problem. In a simulation study and real data analysis, the new method is compared with two recent methods for multivariate regression and exhibits competitive performance in prediction and variable selection.

  9. The Development and Demonstration of Multiple Regression Models for Operant Conditioning Questions.

    Science.gov (United States)

    Fanning, Fred; Newman, Isadore

    Based on the assumption that inferential statistics can make the operant conditioner more sensitive to possible significant relationships, regressions models were developed to test the statistical significance between slopes and Y intercepts of the experimental and control group subjects. These results were then compared to the traditional operant…

  10. A 3-Year Study of Predictive Factors for Positive and Negative Appendicectomies.

    Science.gov (United States)

    Chang, Dwayne T S; Maluda, Melissa; Lee, Lisa; Premaratne, Chandrasiri; Khamhing, Srisongham

    2018-03-06

    Early and accurate identification or exclusion of acute appendicitis is the key to avoid the morbidity of delayed treatment for true appendicitis or unnecessary appendicectomy, respectively. We aim (i) to identify potential predictive factors for positive and negative appendicectomies; and (ii) to analyse the use of ultrasound scans (US) and computed tomography (CT) scans for acute appendicitis. All appendicectomies that took place at our hospital from the 1st of January 2013 to the 31st of December 2015 were retrospectively recorded. Test results of potential predictive factors of acute appendicitis were recorded. Statistical analysis was performed using Fisher exact test, logistic regression analysis, sensitivity, specificity, and positive and negative predictive values calculation. 208 patients were included in this study. 184 patients had histologically proven acute appendicitis. The other 24 patients had either nonappendicitis pathology or normal appendix. Logistic regression analysis showed statistically significant associations between appendicitis and white cell count, neutrophil count, C-reactive protein, and bilirubin. Neutrophil count was the test with the highest sensitivity and negative predictive values, whereas bilirubin was the test with the highest specificity and positive predictive values (PPV). US and CT scans had high sensitivity and PPV for diagnosing appendicitis. No single test was sufficient to diagnose or exclude acute appendicitis by itself. Combining tests with high sensitivity (abnormal neutrophil count, and US and CT scans) and high specificity (raised bilirubin) may predict acute appendicitis more accurately.

  11. Hidden marker position estimation during sit-to-stand with walker.

    Science.gov (United States)

    Yoon, Sang Ho; Jun, Hong Gul; Dan, Byung Ju; Jo, Byeong Rim; Min, Byung Hoon

    2012-01-01

    Motion capture analysis of sit-to-stand task with assistive device is hard to achieve due to obstruction on reflective makers. Previously developed robotic system, Smart Mobile Walker, is used as an assistive device to perform motion capture analysis in sit-to-stand task. All lower limb markers except hip markers are invisible through whole session. The link-segment and regression method is applied to estimate the marker position during sit-to-stand. Applying a new method, the lost marker positions are restored and the biomechanical evaluation of the sit-to-stand movement with a Smart Mobile Walker could be carried out. The accuracy of the marker position estimation is verified with normal sit-to-stand data from more than 30 clinical trials. Moreover, further research on improving the link segment and regression method is addressed.

  12. Positive Disposition in the Prediction of Strategic Independence among Millennials

    Directory of Open Access Journals (Sweden)

    Robert Konopaske

    2017-11-01

    Full Text Available Research on the dispositional traits of Millennials (born in 1980–2000 finds that this generation, compared to earlier generations, tends to be more narcissistic, hold themselves in higher regard and feel more entitled to rewards. The purpose of this intragenerational study is to counter balance extant research by exploring how the positive dispositional traits of proactive personality, core self-evaluation, grit and self-control predict strategic independence in a sample of 311 young adults. Strategic independence is a composite variable measuring a person’s tendency to make plans and achieve long-term goals. A confirmatory factor analysis and hierarchical regression found evidence of discriminant validity across the scales and that three of the four independent variables were statistically significant and positive predictors of strategic independence in the study. The paper discusses research and practical implications, strengths and limitations and areas for future research.

  13. A Gene Expression Classifier of Node-Positive Colorectal Cancer

    Directory of Open Access Journals (Sweden)

    Paul F. Meeh

    2009-10-01

    Full Text Available We used digital long serial analysis of gene expression to discover gene expression differences between node-negative and node-positive colorectal tumors and developed a multigene classifier able to discriminate between these two tumor types. We prepared and sequenced long serial analysis of gene expression libraries from one node-negative and one node-positive colorectal tumor, sequenced to a depth of 26,060 unique tags, and identified 262 tags significantly differentially expressed between these two tumors (P < 2 x 10-6. We confirmed the tag-to-gene assignments and differential expression of 31 genes by quantitative real-time polymerase chain reaction, 12 of which were elevated in the node-positive tumor. We analyzed the expression levels of these 12 upregulated genes in a validation panel of 23 additional tumors and developed an optimized seven-gene logistic regression classifier. The classifier discriminated between node-negative and node-positive tumors with 86% sensitivity and 80% specificity. Receiver operating characteristic analysis of the classifier revealed an area under the curve of 0.86. Experimental manipulation of the function of one classification gene, Fibronectin, caused profound effects on invasion and migration of colorectal cancer cells in vitro. These results suggest that the development of node-positive colorectal cancer occurs in part through elevated epithelial FN1 expression and suggest novel strategies for the diagnosis and treatment of advanced disease.

  14. Regression models of reactor diagnostic signals

    International Nuclear Information System (INIS)

    Vavrin, J.

    1989-01-01

    The application is described of an autoregression model as the simplest regression model of diagnostic signals in experimental analysis of diagnostic systems, in in-service monitoring of normal and anomalous conditions and their diagnostics. The method of diagnostics is described using a regression type diagnostic data base and regression spectral diagnostics. The diagnostics is described of neutron noise signals from anomalous modes in the experimental fuel assembly of a reactor. (author)

  15. Regression and Sparse Regression Methods for Viscosity Estimation of Acid Milk From it’s Sls Features

    DEFF Research Database (Denmark)

    Sharifzadeh, Sara; Skytte, Jacob Lercke; Nielsen, Otto Højager Attermann

    2012-01-01

    Statistical solutions find wide spread use in food and medicine quality control. We investigate the effect of different regression and sparse regression methods for a viscosity estimation problem using the spectro-temporal features from new Sub-Surface Laser Scattering (SLS) vision system. From...... with sparse LAR, lasso and Elastic Net (EN) sparse regression methods. Due to the inconsistent measurement condition, Locally Weighted Scatter plot Smoothing (Loess) has been employed to alleviate the undesired variation in the estimated viscosity. The experimental results of applying different methods show...

  16. Testing discontinuities in nonparametric regression

    KAUST Repository

    Dai, Wenlin

    2017-01-19

    In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13 H.-G. Müller and U. Stadtmüller, Discontinuous versus smooth regression, Ann. Stat. 27 (1999), pp. 299–337. doi: 10.1214/aos/1018031100

  17. Testing discontinuities in nonparametric regression

    KAUST Repository

    Dai, Wenlin; Zhou, Yuejin; Tong, Tiejun

    2017-01-01

    In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13 H.-G. Müller and U. Stadtmüller, Discontinuous versus smooth regression, Ann. Stat. 27 (1999), pp. 299–337. doi: 10.1214/aos/1018031100

  18. Prognostic and Predictive Value of the 21-Gene Recurrence Score Assay in a Randomized Trial of Chemotherapy for Postmenopausal, Node-Positive, Estrogen Receptor-Positive Breast Cancer

    Science.gov (United States)

    Albain, Kathy S.; Barlow, William E.; Shak, Steven; Hortobagyi, Gabriel N.; Livingston, Robert B.; Yeh, I-Tien; Ravdin, Peter; Bugarini, Roberto; Baehner, Frederick L.; Davidson, Nancy E.; Sledge, George W.; Winer, Eric P.; Hudis, Clifford; Ingle, James N.; Perez, Edith A.; Pritchard, Kathleen I.; Shepherd, Lois; Gralow, Julie R.; Yoshizawa, Carl; Allred, D. Craig; Osborne, C. Kent; Hayes, Daniel F.

    2010-01-01

    SUMMARY Background The 21-gene Recurrence Score assay (RS) is prognostic for women with node-negative, estrogen receptor (ER)-positive breast cancer (BC) treated with tamoxifen. A low RS predicts little benefit of chemotherapy. For node-positive BC, we investigated whether RS was prognostic in women treated with tamoxifen alone and whether it identified those who might not benefit from anthracycline-based chemotherapy, despite higher recurrence risks. Methods The phase III trial S8814 for postmenopausal women with node-positive, ER-positive BC showed that CAF chemotherapy prior to tamoxifen (CAF-T) added survival benefit to tamoxifen alone. Optional tumor banking yielded specimens for RS determination by RT-PCR. We evaluated the effect of RS on disease-free survival (DFS) by treatment group (tamoxifen versus CAF-T) using Cox regression adjusting for number of positive nodes. Findings There were 367 specimens (40% of parent trial) with sufficient RNA (tamoxifen, 148; CAF-T, 219). The RS was prognostic in the tamoxifen arm (p=0.006). There was no CAF benefit in the low RS group (logrank p=0.97; HR=1.02, 95% CI (0.54,1.93)), but major DFS improvement for the high RS subset (logrank p=.03; HR=0.59, 95% CI (0.35, 1.01)), adjusting for number of positive nodes. The RS-by-treatment interaction was significant in the first 5 years (p=0.029), with no additional prediction beyond 5 years (p=0.58), though the cumulative benefit remained at 10 years. Results were similar for overall survival and BC-specific survival. Interpretation In this retrospective analysis, the RS is prognostic for tamoxifen-treated patients with positive nodes and predicts significant CAF benefit in tumors with a high RS. A low RS identifies women who may not benefit from anthracycline-based chemotherapy despite positive nodes. PMID:20005174

  19. High Prevalence and Significance of Hepatitis D Virus Infection among Treatment-Naïve HBsAg-Positive Patients in Northern Vietnam

    Science.gov (United States)

    Sy, Bui Tien; Ratsch, Boris A.; Toan, Nguyen Linh; Song, Le Huu; Wollboldt, Christian; Bryniok, Agnes; Nguyen, Hung Minh; Luong, Hoang Van; Velavan, Thirumalaisamy P.; Wedemeyer, Heiner; Kremsner, Peter G.; Bock, C.-Thomas

    2013-01-01

    Background Hepatitis D virus (HDV) infection is considered to cause more severe hepatitis than hepatitis B virus (HBV) monoinfection. With more than 9.5 million HBV-infected people, Vietnam will face an enormous health burden. The prevalence of HDV in Vietnamese HBsAg-positive patients is speculative. Therefore, we assessed the prevalence of HDV in Vietnamese patients, determined the HDV-genotype distribution and compared the findings with the clinical outcome. Methods 266 sera of well-characterized HBsAg-positive patients in Northern Vietnam were analysed for the presence of HDV using newly developed HDV-specific RT-PCRs. Sequencing and phylogenetic analysis were performed for HDV-genotyping. Results The HDV-genome prevalence observed in the Vietnamese HBsAg-positive patients was high with 15.4% while patients with acute hepatitis showed 43.3%. Phylogenetic analysis demonstrated a predominance of HDV-genotype 1 clustering in an Asian clade while HDV-genotype 2 could be also detected. The serum aminotransferase levels (AST, ALT) as well as total and direct bilirubin were significantly elevated in HDV-positive individuals (p<0.05). HDV loads were mainly low (<300 to 4.108 HDV-copies/ml). Of note, higher HDV loads were mainly found in HBV-genotype mix samples in contrast to single HBV-infections. In HBV/HDV-coinfections, HBV loads were significantly higher in HBV-genotype C in comparison to HBV-genotype A samples (p<0.05). Conclusion HDV prevalence is high in Vietnamese individuals, especially in patients with acute hepatitis B. HDV replication activity showed a HBV-genotype dependency and could be associated with elevated liver parameters. Besides serological assays molecular tests are recommended for diagnosis of HDV. Finally, the high prevalence of HBV and HDV prompts the urgent need for HBV-vaccination coverage. PMID:24205106

  20. On Solving Lq-Penalized Regressions

    Directory of Open Access Journals (Sweden)

    Tracy Zhou Wu

    2007-01-01

    Full Text Available Lq-penalized regression arises in multidimensional statistical modelling where all or part of the regression coefficients are penalized to achieve both accuracy and parsimony of statistical models. There is often substantial computational difficulty except for the quadratic penalty case. The difficulty is partly due to the nonsmoothness of the objective function inherited from the use of the absolute value. We propose a new solution method for the general Lq-penalized regression problem based on space transformation and thus efficient optimization algorithms. The new method has immediate applications in statistics, notably in penalized spline smoothing problems. In particular, the LASSO problem is shown to be polynomial time solvable. Numerical studies show promise of our approach.

  1. Boosted regression trees, multivariate adaptive regression splines and their two-step combinations with multiple linear regression or partial least squares to predict blood-brain barrier passage: a case study.

    Science.gov (United States)

    Deconinck, E; Zhang, M H; Petitet, F; Dubus, E; Ijjaali, I; Coomans, D; Vander Heyden, Y

    2008-02-18

    The use of some unconventional non-linear modeling techniques, i.e. classification and regression trees and multivariate adaptive regression splines-based methods, was explored to model the blood-brain barrier (BBB) passage of drugs and drug-like molecules. The data set contains BBB passage values for 299 structural and pharmacological diverse drugs, originating from a structured knowledge-based database. Models were built using boosted regression trees (BRT) and multivariate adaptive regression splines (MARS), as well as their respective combinations with stepwise multiple linear regression (MLR) and partial least squares (PLS) regression in two-step approaches. The best models were obtained using combinations of MARS with either stepwise MLR or PLS. It could be concluded that the use of combinations of a linear with a non-linear modeling technique results in some improved properties compared to the individual linear and non-linear models and that, when the use of such a combination is appropriate, combinations using MARS as non-linear technique should be preferred over those with BRT, due to some serious drawbacks of the BRT approaches.

  2. Using the classical linear regression model in analysis of the dependences of conveyor belt life

    Directory of Open Access Journals (Sweden)

    Miriam Andrejiová

    2013-12-01

    Full Text Available The paper deals with the classical linear regression model of the dependence of conveyor belt life on some selected parameters: thickness of paint layer, width and length of the belt, conveyor speed and quantity of transported material. The first part of the article is about regression model design, point and interval estimation of parameters, verification of statistical significance of the model, and about the parameters of the proposed regression model. The second part of the article deals with identification of influential and extreme values that can have an impact on estimation of regression model parameters. The third part focuses on assumptions of the classical regression model, i.e. on verification of independence assumptions, normality and homoscedasticity of residuals.

  3. Testing Heteroscedasticity in Robust Regression

    Czech Academy of Sciences Publication Activity Database

    Kalina, Jan

    2011-01-01

    Roč. 1, č. 4 (2011), s. 25-28 ISSN 2045-3345 Grant - others:GA ČR(CZ) GA402/09/0557 Institutional research plan: CEZ:AV0Z10300504 Keywords : robust regression * heteroscedasticity * regression quantiles * diagnostics Subject RIV: BB - Applied Statistics , Operational Research http://www.researchjournals.co.uk/documents/Vol4/06%20Kalina.pdf

  4. Prediction-Oriented Marker Selection (PROMISE): With Application to High-Dimensional Regression.

    Science.gov (United States)

    Kim, Soyeon; Baladandayuthapani, Veerabhadran; Lee, J Jack

    2017-06-01

    In personalized medicine, biomarkers are used to select therapies with the highest likelihood of success based on an individual patient's biomarker/genomic profile. Two goals are to choose important biomarkers that accurately predict treatment outcomes and to cull unimportant biomarkers to reduce the cost of biological and clinical verifications. These goals are challenging due to the high dimensionality of genomic data. Variable selection methods based on penalized regression (e.g., the lasso and elastic net) have yielded promising results. However, selecting the right amount of penalization is critical to simultaneously achieving these two goals. Standard approaches based on cross-validation (CV) typically provide high prediction accuracy with high true positive rates but at the cost of too many false positives. Alternatively, stability selection (SS) controls the number of false positives, but at the cost of yielding too few true positives. To circumvent these issues, we propose prediction-oriented marker selection (PROMISE), which combines SS with CV to conflate the advantages of both methods. Our application of PROMISE with the lasso and elastic net in data analysis shows that, compared to CV, PROMISE produces sparse solutions, few false positives, and small type I + type II error, and maintains good prediction accuracy, with a marginal decrease in the true positive rates. Compared to SS, PROMISE offers better prediction accuracy and true positive rates. In summary, PROMISE can be applied in many fields to select regularization parameters when the goals are to minimize false positives and maximize prediction accuracy.

  5. Circulating CD34-positive cells, glomerular filtration rate and triglycerides in relation to hypertension.

    Science.gov (United States)

    Shimizu, Yuji; Sato, Shimpei; Koyamatsu, Jun; Yamanashi, Hirotomo; Nagayoshi, Mako; Kadota, Koichiro; Maeda, Takahiro

    2015-11-01

    Serum triglycerides have been reported to be independently associated with the development of chronic kidney disease (CKD), which is known to play a role in vascular disturbance. On the other hand, circulating CD34-positve cells, including endothelial progenitor cells, are reported to contribute to vascular repair. However, no studies have reported on the correlation between triglycerides and the number of CD34-positive cells. Since hypertension is well known factor for vascular impairment, the degree of correlation between serum triglycerides and circulating CD34-positve cells should account for hypertension status. We conducted a cross-sectional study of 274 elderly Japanese men aged ≥ 60 years (range 60-79 years) undergoing general health checkups. Multiple linear regression analysis of non-hypertensive subjects adjusting for classical cardiovascular risk factors showed that although triglyceride levels (1SD increments; 64 mg/dL) did not significantly correlate with glomerular filtration rate (GFR) (β = -2.06, p = 0.163), a significant positive correlation was seen between triglycerides and the number of circulating CD34-positive cells (β = 0.50, p = 0.004). In hypertensive subjects, a significant inverse correlation between triglycerides and GFR was observed (β = -2.66, p = 0.035), whereas no significant correlation between triglycerides and the number of circulating CD34-positive cells was noted (β = -0.004, p = 0.974). Since endothelial progenitor cells (CD34-positive cells) have been reported to contribute to vascular repair, our results indicate that in non-hypertensive subjects, triglycerides may stimulate an increase in circulating CD34-positive cells (vascular repair) by inducing vascular disturbance. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  6. Academic stress and positive affect: Asian value and self-worth contingency as moderators among Chinese international students.

    Science.gov (United States)

    Liao, Kelly Yu-Hsin; Wei, Meifen

    2014-01-01

    The theoretical model proposed by Berry and colleagues (Berry, 1997; Berry, Kim, Minde, & Mok, 1987) highlights the importance of identifying moderators in the acculturation process. Accordingly, the current study examined the Asian cultural value of family recognition through achievement (FRTA) and contingency of self-worth on academic competence (CSW-AC) as moderators in the association between academic stress and positive affect among Chinese international students. A total of 370 Chinese international students completed online surveys. Results from a hierarchical regression indicated that while academic stress was negatively associated with positive affect, FRTA was positively associated with positive affect. In other words, those with high academic stress reported a lower level of positive affect. However, individuals who endorsed high levels of FRTA reported a higher level of positive affect. In addition, results also revealed a significant interaction between academic stress and CSW-AC on positive affect. Thus, the study's finding supported the moderator role of CSW-AC. Simple effect analyses were conducted to examine the significant interaction. The results showed that higher levels of CSW-AC strengthened the negative association between academic stress and positive affect but lower levels of CSW-AC did not. Future research directions and implications are discussed.

  7. Virtual machine consolidation enhancement using hybrid regression algorithms

    Directory of Open Access Journals (Sweden)

    Amany Abdelsamea

    2017-11-01

    Full Text Available Cloud computing data centers are growing rapidly in both number and capacity to meet the increasing demands for highly-responsive computing and massive storage. Such data centers consume enormous amounts of electrical energy resulting in high operating costs and carbon dioxide emissions. The reason for this extremely high energy consumption is not just the quantity of computing resources and the power inefficiency of hardware, but rather lies in the inefficient usage of these resources. VM consolidation involves live migration of VMs hence the capability of transferring a VM between physical servers with a close to zero down time. It is an effective way to improve the utilization of resources and increase energy efficiency in cloud data centers. VM consolidation consists of host overload/underload detection, VM selection and VM placement. Most of the current VM consolidation approaches apply either heuristic-based techniques, such as static utilization thresholds, decision-making based on statistical analysis of historical data; or simply periodic adaptation of the VM allocation. Most of those algorithms rely on CPU utilization only for host overload detection. In this paper we propose using hybrid factors to enhance VM consolidation. Specifically we developed a multiple regression algorithm that uses CPU utilization, memory utilization and bandwidth utilization for host overload detection. The proposed algorithm, Multiple Regression Host Overload Detection (MRHOD, significantly reduces energy consumption while ensuring a high level of adherence to Service Level Agreements (SLA since it gives a real indication of host utilization based on three parameters (CPU, Memory, Bandwidth utilizations instead of one parameter only (CPU utilization. Through simulations we show that our approach reduces power consumption by 6 times compared to single factor algorithms using random workload. Also using PlanetLab workload traces we show that MRHOD improves

  8. Spontaneous regression of a congenital melanocytic nevus

    Directory of Open Access Journals (Sweden)

    Amiya Kumar Nath

    2011-01-01

    Full Text Available Congenital melanocytic nevus (CMN may rarely regress which may also be associated with a halo or vitiligo. We describe a 10-year-old girl who presented with CMN on the left leg since birth, which recently started to regress spontaneously with associated depigmentation in the lesion and at a distant site. Dermoscopy performed at different sites of the regressing lesion demonstrated loss of epidermal pigments first followed by loss of dermal pigments. Histopathology and Masson-Fontana stain demonstrated lymphocytic infiltration and loss of pigment production in the regressing area. Immunohistochemistry staining (S100 and HMB-45, however, showed that nevus cells were present in the regressing areas.

  9. Identifying the Safety Factors over Traffic Signs in State Roads using a Panel Quantile Regression Approach.

    Science.gov (United States)

    Šarić, Željko; Xu, Xuecai; Duan, Li; Babić, Darko

    2018-06-20

    This study intended to investigate the interactions between accident rate and traffic signs in state roads located in Croatia, and accommodate the heterogeneity attributed to unobserved factors. The data from 130 state roads between 2012 and 2016 were collected from Traffic Accident Database System maintained by the Republic of Croatia Ministry of the Interior. To address the heterogeneity, a panel quantile regression model was proposed, in which quantile regression model offers a more complete view and a highly comprehensive analysis of the relationship between accident rate and traffic signs, while the panel data model accommodates the heterogeneity attributed to unobserved factors. Results revealed that (1) low visibility of material damage (MD) and death or injured (DI) increased the accident rate; (2) the number of mandatory signs and the number of warning signs were more likely to reduce the accident rate; (3)average speed limit and the number of invalid traffic signs per km exhibited a high accident rate. To our knowledge, it's the first attempt to analyze the interactions between accident consequences and traffic signs by employing a panel quantile regression model; by involving the visibility, the present study demonstrates that the low visibility causes a relatively higher risk of MD and DI; It is noteworthy that average speed limit corresponds with accident rate positively; The number of mandatory signs and the number of warning signs are more likely to reduce the accident rate; The number of invalid traffic signs per km are significant for accident rate, thus regular maintenance should be kept for a safer roadway environment.

  10. Kinetic microplate bioassays for relative potency of antibiotics improved by partial Least Square (PLS) regression.

    Science.gov (United States)

    Francisco, Fabiane Lacerda; Saviano, Alessandro Morais; Almeida, Túlia de Souza Botelho; Lourenço, Felipe Rebello

    2016-05-01

    Microbiological assays are widely used to estimate the relative potencies of antibiotics in order to guarantee the efficacy, safety, and quality of drug products. Despite of the advantages of turbidimetric bioassays when compared to other methods, it has limitations concerning the linearity and range of the dose-response curve determination. Here, we proposed to use partial least squares (PLS) regression to solve these limitations and to improve the prediction of relative potencies of antibiotics. Kinetic-reading microplate turbidimetric bioassays for apramacyin and vancomycin were performed using Escherichia coli (ATCC 8739) and Bacillus subtilis (ATCC 6633), respectively. Microbial growths were measured as absorbance up to 180 and 300min for apramycin and vancomycin turbidimetric bioassays, respectively. Conventional dose-response curves (absorbances or area under the microbial growth curve vs. log of antibiotic concentration) showed significant regression, however there were significant deviation of linearity. Thus, they could not be used for relative potency estimations. PLS regression allowed us to construct a predictive model for estimating the relative potencies of apramycin and vancomycin without over-fitting and it improved the linear range of turbidimetric bioassay. In addition, PLS regression provided predictions of relative potencies equivalent to those obtained from agar diffusion official methods. Therefore, we conclude that PLS regression may be used to estimate the relative potencies of antibiotics with significant advantages when compared to conventional dose-response curve determination. Copyright © 2016 Elsevier B.V. All rights reserved.

  11. Regression Analysis by Example. 5th Edition

    Science.gov (United States)

    Chatterjee, Samprit; Hadi, Ali S.

    2012-01-01

    Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. "Regression Analysis by Example, Fifth Edition" has been expanded and thoroughly…

  12. Graph Regularized Meta-path Based Transductive Regression in Heterogeneous Information Network.

    Science.gov (United States)

    Wan, Mengting; Ouyang, Yunbo; Kaplan, Lance; Han, Jiawei

    2015-01-01

    A number of real-world networks are heterogeneous information networks, which are composed of different types of nodes and links. Numerical prediction in heterogeneous information networks is a challenging but significant area because network based information for unlabeled objects is usually limited to make precise estimations. In this paper, we consider a graph regularized meta-path based transductive regression model ( Grempt ), which combines the principal philosophies of typical graph-based transductive classification methods and transductive regression models designed for homogeneous networks. The computation of our method is time and space efficient and the precision of our model can be verified by numerical experiments.

  13. Gaussian process regression analysis for functional data

    CERN Document Server

    Shi, Jian Qing

    2011-01-01

    Gaussian Process Regression Analysis for Functional Data presents nonparametric statistical methods for functional regression analysis, specifically the methods based on a Gaussian process prior in a functional space. The authors focus on problems involving functional response variables and mixed covariates of functional and scalar variables.Covering the basics of Gaussian process regression, the first several chapters discuss functional data analysis, theoretical aspects based on the asymptotic properties of Gaussian process regression models, and new methodological developments for high dime

  14. Regression Models for Market-Shares

    DEFF Research Database (Denmark)

    Birch, Kristina; Olsen, Jørgen Kai; Tjur, Tue

    2005-01-01

    On the background of a data set of weekly sales and prices for three brands of coffee, this paper discusses various regression models and their relation to the multiplicative competitive-interaction model (the MCI model, see Cooper 1988, 1993) for market-shares. Emphasis is put on the interpretat......On the background of a data set of weekly sales and prices for three brands of coffee, this paper discusses various regression models and their relation to the multiplicative competitive-interaction model (the MCI model, see Cooper 1988, 1993) for market-shares. Emphasis is put...... on the interpretation of the parameters in relation to models for the total sales based on discrete choice models.Key words and phrases. MCI model, discrete choice model, market-shares, price elasitcity, regression model....

  15. Clinical value of regression of electrocardiographic left ventricular hypertrophy after aortic valve replacement.

    Science.gov (United States)

    Yamabe, Sayuri; Dohi, Yoshihiro; Higashi, Akifumi; Kinoshita, Hiroki; Sada, Yoshiharu; Hidaka, Takayuki; Kurisu, Satoshi; Shiode, Nobuo; Kihara, Yasuki

    2016-09-01

    Electrocardiographic left ventricular hypertrophy (ECG-LVH) gradually regressed after aortic valve replacement (AVR) in patients with severe aortic stenosis. Sokolow-Lyon voltage (SV1 + RV5/6) is possibly the most widely used criterion for ECG-LVH. The aim of this study was to determine whether decrease in Sokolow-Lyon voltage reflects left ventricular reverse remodeling detected by echocardiography after AVR. Of 129 consecutive patients who underwent AVR for severe aortic stenosis, 38 patients with preoperative ECG-LVH, defined by SV1 + RV5/6 of ≥3.5 mV, were enrolled in this study. Electrocardiography and echocardiography were performed preoperatively and 1 year postoperatively. The patients were divided into ECG-LVH regression group (n = 19) and non-regression group (n = 19) according to the median value of the absolute regression in SV1 + RV5/6. Multivariate logistic regression analysis was performed to assess determinants of ECG-LVH regression among echocardiographic indices. ECG-LVH regression group showed significantly greater decrease in left ventricular mass index and left ventricular dimensions than Non-regression group. ECG-LVH regression was independently determined by decrease in the left ventricular mass index [odds ratio (OR) 1.28, 95 % confidence interval (CI) 1.03-1.69, p = 0.048], left ventricular end-diastolic dimension (OR 1.18, 95 % CI 1.03-1.41, p = 0.014), and left ventricular end-systolic dimension (OR 1.24, 95 % CI 1.06-1.52, p = 0.0047). ECG-LVH regression could be a marker of the effect of AVR on both reducing the left ventricular mass index and left ventricular dimensions. The effect of AVR on reverse remodeling can be estimated, at least in part, by regression of ECG-LVH.

  16. Using Edge Voxel Information to Improve Motion Regression for rs-fMRI Connectivity Studies.

    Science.gov (United States)

    Patriat, Rémi; Molloy, Erin K; Birn, Rasmus M

    2015-11-01

    Recent fMRI studies have outlined the critical impact of in-scanner head motion, particularly on estimates of functional connectivity. Common strategies to reduce the influence of motion include realignment as well as the inclusion of nuisance regressors, such as the 6 realignment parameters, their first derivatives, time-shifted versions of the realignment parameters, and the squared parameters. However, these regressors have limited success at noise reduction. We hypothesized that using nuisance regressors consisting of the principal components (PCs) of edge voxel time series would be better able to capture slice-specific and nonlinear signal changes, thus explaining more variance, improving data quality (i.e., lower DVARS and temporal SNR), and reducing the effect of motion on default-mode network connectivity. Functional MRI data from 22 healthy adult subjects were preprocessed using typical motion regression approaches as well as nuisance regression derived from edge voxel time courses. Results were evaluated in the presence and absence of both global signal regression and motion censoring. Nuisance regressors derived from signal intensity time courses at the edge of the brain significantly improved motion correction compared to using only the realignment parameters and their derivatives. Of the models tested, only the edge voxel regression models were able to eliminate significant differences in default-mode network connectivity between high- and low-motion subjects regardless of the use of global signal regression or censoring.

  17. Forecasting urban water demand: A meta-regression analysis.

    Science.gov (United States)

    Sebri, Maamar

    2016-12-01

    Water managers and planners require accurate water demand forecasts over the short-, medium- and long-term for many purposes. These range from assessing water supply needs over spatial and temporal patterns to optimizing future investments and planning future allocations across competing sectors. This study surveys the empirical literature on the urban water demand forecasting using the meta-analytical approach. Specifically, using more than 600 estimates, a meta-regression analysis is conducted to identify explanations of cross-studies variation in accuracy of urban water demand forecasting. Our study finds that accuracy depends significantly on study characteristics, including demand periodicity, modeling method, forecasting horizon, model specification and sample size. The meta-regression results remain robust to different estimators employed as well as to a series of sensitivity checks performed. The importance of these findings lies in the conclusions and implications drawn out for regulators and policymakers and for academics alike. Copyright © 2016. Published by Elsevier Ltd.

  18. Regression of left ventricular hypertrophy and microalbuminuria changes during antihypertensive treatment.

    Science.gov (United States)

    Rodilla, Enrique; Pascual, Jose Maria; Costa, Jose Antonio; Martin, Joaquin; Gonzalez, Carmen; Redon, Josep

    2013-08-01

    The objective of the present study was to assess the regression of left ventricular hypertrophy (LVH) during antihypertensive treatment, and its relationship with the changes in microalbuminuria. One hundred and sixty-eight previously untreated patients with echocardiographic LVH, 46 (27%) with microalbuminuria, were followed during a median period of 13 months (range 6-23 months) and treated with lifestyle changes and antihypertensive drugs. Twenty-four-hour ambulatory blood pressure monitoring, echocardiography and urinary albumin excretion were assessed at the beginning and at the end of the study period. Left ventricular mass index (LVMI) was reduced from 137 [interquartile interval (IQI), 129-154] to 121 (IQI, 104-137) g/m (P 50%) had the same odds of achieving regression of LVH as patients with normoalbuminuria (ORm 1.1; 95% CI 0.38-3.25; P = 0.85). However, those with microalbuminuria at baseline, who did not regress, had less probability of achieving LVH regression than the normoalbuminuric patients (OR 0.26; 95% CI 0.07-0.90; P = 0.03) even when adjusted for age, sex, initial LVMI, GFR, blood pressure and angiotensin-converting enzyme inhibitor (ACE-I) or angiotensin receptor blocker (ARB) treatment during the follow-up. Patients who do not have a significant reduction in microalbuminuria have less chance of achieving LVH regression, independent of blood pressure reduction.

  19. A simple linear regression method for quantitative trait loci linkage analysis with censored observations.

    Science.gov (United States)

    Anderson, Carl A; McRae, Allan F; Visscher, Peter M

    2006-07-01

    Standard quantitative trait loci (QTL) mapping techniques commonly assume that the trait is both fully observed and normally distributed. When considering survival or age-at-onset traits these assumptions are often incorrect. Methods have been developed to map QTL for survival traits; however, they are both computationally intensive and not available in standard genome analysis software packages. We propose a grouped linear regression method for the analysis of continuous survival data. Using simulation we compare this method to both the Cox and Weibull proportional hazards models and a standard linear regression method that ignores censoring. The grouped linear regression method is of equivalent power to both the Cox and Weibull proportional hazards methods and is significantly better than the standard linear regression method when censored observations are present. The method is also robust to the proportion of censored individuals and the underlying distribution of the trait. On the basis of linear regression methodology, the grouped linear regression model is computationally simple and fast and can be implemented readily in freely available statistical software.

  20. Poisson regression approach for modeling fatal injury rates amongst Malaysian workers

    International Nuclear Information System (INIS)

    Kamarulzaman Ibrahim; Heng Khai Theng

    2005-01-01

    Many safety studies are based on the analysis carried out on injury surveillance data. The injury surveillance data gathered for the analysis include information on number of employees at risk of injury in each of several strata where the strata are defined in terms of a series of important predictor variables. Further insight into the relationship between fatal injury rates and predictor variables may be obtained by the poisson regression approach. Poisson regression is widely used in analyzing count data. In this study, poisson regression is used to model the relationship between fatal injury rates and predictor variables which are year (1995-2002), gender, recording system and industry type. Data for the analysis were obtained from PERKESO and Jabatan Perangkaan Malaysia. It is found that the assumption that the data follow poisson distribution has been violated. After correction for the problem of over dispersion, the predictor variables that are found to be significant in the model are gender, system of recording, industry type, two interaction effects (interaction between recording system and industry type and between year and industry type). Introduction Regression analysis is one of the most popular

  1. A robust regression based on weighted LSSVM and penalized trimmed squares

    International Nuclear Information System (INIS)

    Liu, Jianyong; Wang, Yong; Fu, Chengqun; Guo, Jie; Yu, Qin

    2016-01-01

    Least squares support vector machine (LS-SVM) for nonlinear regression is sensitive to outliers in the field of machine learning. Weighted LS-SVM (WLS-SVM) overcomes this drawback by adding weight to each training sample. However, as the number of outliers increases, the accuracy of WLS-SVM may decrease. In order to improve the robustness of WLS-SVM, a new robust regression method based on WLS-SVM and penalized trimmed squares (WLSSVM–PTS) has been proposed. The algorithm comprises three main stages. The initial parameters are obtained by least trimmed squares at first. Then, the significant outliers are identified and eliminated by the Fast-PTS algorithm. The remaining samples with little outliers are estimated by WLS-SVM at last. The statistical tests of experimental results carried out on numerical datasets and real-world datasets show that the proposed WLSSVM–PTS is significantly robust than LS-SVM, WLS-SVM and LSSVM–LTS.

  2. Spatially resolved regression analysis of pre-treatment FDG, FLT and Cu-ATSM PET from post-treatment FDG PET: an exploratory study

    Science.gov (United States)

    Bowen, Stephen R; Chappell, Richard J; Bentzen, Søren M; Deveau, Michael A; Forrest, Lisa J; Jeraj, Robert

    2012-01-01

    Purpose To quantify associations between pre-radiotherapy and post-radiotherapy PET parameters via spatially resolved regression. Materials and methods Ten canine sinonasal cancer patients underwent PET/CT scans of [18F]FDG (FDGpre), [18F]FLT (FLTpre), and [61Cu]Cu-ATSM (Cu-ATSMpre). Following radiotherapy regimens of 50 Gy in 10 fractions, veterinary patients underwent FDG PET/CT scans at three months (FDGpost). Regression of standardized uptake values in baseline FDGpre, FLTpre and Cu-ATSMpre tumour voxels to those in FDGpost images was performed for linear, log-linear, generalized-linear and mixed-fit linear models. Goodness-of-fit in regression coefficients was assessed by R2. Hypothesis testing of coefficients over the patient population was performed. Results Multivariate linear model fits of FDGpre to FDGpost were significantly positive over the population (FDGpost~0.17 FDGpre, p=0.03), and classified slopes of RECIST non-responders and responders to be different (0.37 vs. 0.07, p=0.01). Generalized-linear model fits related FDGpre to FDGpost by a linear power law (FDGpost~FDGpre0.93, pregression analysis indicates that pre-treatment FDG PET uptake is most strongly associated with three-month post-treatment FDG PET uptake in this patient population, though associations are histopathology-dependent. PMID:22682748

  3. [Evaluation of estimation of prevalence ratio using bayesian log-binomial regression model].

    Science.gov (United States)

    Gao, W L; Lin, H; Liu, X N; Ren, X W; Li, J S; Shen, X P; Zhu, S L

    2017-03-10

    To evaluate the estimation of prevalence ratio ( PR ) by using bayesian log-binomial regression model and its application, we estimated the PR of medical care-seeking prevalence to caregivers' recognition of risk signs of diarrhea in their infants by using bayesian log-binomial regression model in Openbugs software. The results showed that caregivers' recognition of infant' s risk signs of diarrhea was associated significantly with a 13% increase of medical care-seeking. Meanwhile, we compared the differences in PR 's point estimation and its interval estimation of medical care-seeking prevalence to caregivers' recognition of risk signs of diarrhea and convergence of three models (model 1: not adjusting for the covariates; model 2: adjusting for duration of caregivers' education, model 3: adjusting for distance between village and township and child month-age based on model 2) between bayesian log-binomial regression model and conventional log-binomial regression model. The results showed that all three bayesian log-binomial regression models were convergence and the estimated PRs were 1.130(95 %CI : 1.005-1.265), 1.128(95 %CI : 1.001-1.264) and 1.132(95 %CI : 1.004-1.267), respectively. Conventional log-binomial regression model 1 and model 2 were convergence and their PRs were 1.130(95 % CI : 1.055-1.206) and 1.126(95 % CI : 1.051-1.203), respectively, but the model 3 was misconvergence, so COPY method was used to estimate PR , which was 1.125 (95 %CI : 1.051-1.200). In addition, the point estimation and interval estimation of PRs from three bayesian log-binomial regression models differed slightly from those of PRs from conventional log-binomial regression model, but they had a good consistency in estimating PR . Therefore, bayesian log-binomial regression model can effectively estimate PR with less misconvergence and have more advantages in application compared with conventional log-binomial regression model.

  4. An Empirical Illustration of Positive Stigma towards Child Labor

    OpenAIRE

    Harry A Patrinos; Najeeb Shafiq

    2010-01-01

    This empirical note complements the qualitative and theoretical research on positive household stigma towards child labor. We use data from Guatemala and two instruments for measuring stigma: a child's indigenous background and household head's childhood work experience. We then adopt binomial probit regression methods to illustrate that positive stigma has a large effect on child labor practices, and a modest effect on school enrollment.

  5. Poisson Mixture Regression Models for Heart Disease Prediction.

    Science.gov (United States)

    Mufudza, Chipo; Erol, Hamza

    2016-01-01

    Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.

  6. Poisson Mixture Regression Models for Heart Disease Prediction

    Science.gov (United States)

    Erol, Hamza

    2016-01-01

    Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611

  7. Hospital financial position and the adoption of electronic health records.

    Science.gov (United States)

    Ginn, Gregory O; Shen, Jay J; Moseley, Charles B

    2011-01-01

    The objective of this study was to examine the relationship between financial position and adoption of electronic health records (EHRs) in 2442 acute care hospitals. The study was cross-sectional and utilized a general linear mixed model with the multinomial distribution specification for data analysis. We verified the results by also running a multinomial logistic regression model. To measure our variables, we used data from (1) the 2007 American Hospital Association (AHA) electronic health record implementation survey, (2) the 2006 Centers for Medicare and Medicaid Cost Reports, and (3) the 2006 AHA Annual Survey containing organizational and operational data. Our dependent variable was an ordinal variable with three levels used to indicate the extent of EHR adoption by hospitals. Our independent variables were five financial ratios: (1) net days revenue in accounts receivable, (2) total margin, (3) the equity multiplier, (4) total asset turnover, and (5) the ratio of total payroll to total expenses. For control variables, we used (1) bed size, (2) ownership type, (3) teaching affiliation, (4) system membership, (5) network participation, (6) fulltime equivalent nurses per adjusted average daily census, (7) average daily census per staffed bed, (8) Medicare patients percentage, (9) Medicaid patients percentage, (10) capitation-based reimbursement, and (11) nonconcentrated market. Only liquidity was significant and positively associated with EHR adoption. Asset turnover ratio was significant but, unexpectedly, was negatively associated with EHR adoption. However, many control variables, most notably bed size, showed significant positive associations with EHR adoption. Thus, it seems that hospitals adopt EHRs as a strategic move to better align themselves with their environment.

  8. Regression analysis using dependent Polya trees.

    Science.gov (United States)

    Schörgendorfer, Angela; Branscum, Adam J

    2013-11-30

    Many commonly used models for linear regression analysis force overly simplistic shape and scale constraints on the residual structure of data. We propose a semiparametric Bayesian model for regression analysis that produces data-driven inference by using a new type of dependent Polya tree prior to model arbitrary residual distributions that are allowed to evolve across increasing levels of an ordinal covariate (e.g., time, in repeated measurement studies). By modeling residual distributions at consecutive covariate levels or time points using separate, but dependent Polya tree priors, distributional information is pooled while allowing for broad pliability to accommodate many types of changing residual distributions. We can use the proposed dependent residual structure in a wide range of regression settings, including fixed-effects and mixed-effects linear and nonlinear models for cross-sectional, prospective, and repeated measurement data. A simulation study illustrates the flexibility of our novel semiparametric regression model to accurately capture evolving residual distributions. In an application to immune development data on immunoglobulin G antibodies in children, our new model outperforms several contemporary semiparametric regression models based on a predictive model selection criterion. Copyright © 2013 John Wiley & Sons, Ltd.

  9. The Influence of Ethical Ideologies on Promotive Extra Role Behaviors and Positive Work Behavior of Individuals

    Directory of Open Access Journals (Sweden)

    Işıl Mendeş Pekdemir

    2015-08-01

    Full Text Available Following the previous studies on ‘extra-role behavior’, this study focuses especially on ‘promotive extra-role behavior’ as well as ‘positive work behavior’, and explores of ethical ideologies on them. On that framework, this paper aims to achieve the effect of ‘ethical ideologies’ (idealism and relativism on promotive extra-role behaviors (helping and voice and positive work behavior. Moreover, we examine the impact of being high and low idealist personality as well as high and low relativist personality on level of ‘helping extra-role behavior’, ‘voice behavior’, ‘extra-role behavior’, and ‘positive work behaviors’ that individuals exhibit. This paper also aims to explore the influence of demographic variables on helping, voice, and positive work behavior. In order to achieve the goals mentioned, we collected data from 356 MBA students, and used the ordinal logistic regression analysis. Results indicate that idealism significantly correlates to helping, voice, and positive work behavior.

  10. Bias in logistic regression due to imperfect diagnostic test results and practical correction approaches.

    Science.gov (United States)

    Valle, Denis; Lima, Joanna M Tucker; Millar, Justin; Amratia, Punam; Haque, Ubydul

    2015-11-04

    Logistic regression is a statistical model widely used in cross-sectional and cohort studies to identify and quantify the effects of potential disease risk factors. However, the impact of imperfect tests on adjusted odds ratios (and thus on the identification of risk factors) is under-appreciated. The purpose of this article is to draw attention to the problem associated with modelling imperfect diagnostic tests, and propose simple Bayesian models to adequately address this issue. A systematic literature review was conducted to determine the proportion of malaria studies that appropriately accounted for false-negatives/false-positives in a logistic regression setting. Inference from the standard logistic regression was also compared with that from three proposed Bayesian models using simulations and malaria data from the western Brazilian Amazon. A systematic literature review suggests that malaria epidemiologists are largely unaware of the problem of using logistic regression to model imperfect diagnostic test results. Simulation results reveal that statistical inference can be substantially improved when using the proposed Bayesian models versus the standard logistic regression. Finally, analysis of original malaria data with one of the proposed Bayesian models reveals that microscopy sensitivity is strongly influenced by how long people have lived in the study region, and an important risk factor (i.e., participation in forest extractivism) is identified that would have been missed by standard logistic regression. Given the numerous diagnostic methods employed by malaria researchers and the ubiquitous use of logistic regression to model the results of these diagnostic tests, this paper provides critical guidelines to improve data analysis practice in the presence of misclassification error. Easy-to-use code that can be readily adapted to WinBUGS is provided, enabling straightforward implementation of the proposed Bayesian models.

  11. Applied Regression Modeling A Business Approach

    CERN Document Server

    Pardoe, Iain

    2012-01-01

    An applied and concise treatment of statistical regression techniques for business students and professionals who have little or no background in calculusRegression analysis is an invaluable statistical methodology in business settings and is vital to model the relationship between a response variable and one or more predictor variables, as well as the prediction of a response value given values of the predictors. In view of the inherent uncertainty of business processes, such as the volatility of consumer spending and the presence of market uncertainty, business professionals use regression a

  12. Regression of environmental noise in LIGO data

    International Nuclear Information System (INIS)

    Tiwari, V; Klimenko, S; Mitselmakher, G; Necula, V; Drago, M; Prodi, G; Frolov, V; Yakushin, I; Re, V; Salemi, F; Vedovato, G

    2015-01-01

    We address the problem of noise regression in the output of gravitational-wave (GW) interferometers, using data from the physical environmental monitors (PEM). The objective of the regression analysis is to predict environmental noise in the GW channel from the PEM measurements. One of the most promising regression methods is based on the construction of Wiener–Kolmogorov (WK) filters. Using this method, the seismic noise cancellation from the LIGO GW channel has already been performed. In the presented approach the WK method has been extended, incorporating banks of Wiener filters in the time–frequency domain, multi-channel analysis and regulation schemes, which greatly enhance the versatility of the regression analysis. Also we present the first results on regression of the bi-coherent noise in the LIGO data. (paper)

  13. Network support, technology use, depression, and ART adherence among HIV-positive MSM of color.

    Science.gov (United States)

    Holloway, I W; Tan, D; Dunlap, S L; Palmer, L; Beougher, S; Cederbaum, J A

    2017-09-01

    Depression is associated with poor antiretroviral therapy (ART) adherence among people living with HIV/AIDS. This relationship may be moderated by an individual's social network characteristics. Our study sought to examine social network correlates of treatment adherence among HIV-positive men recruited from social service agencies throughout Los Angeles County (N = 150) to inform technology-driven social support interventions for this population. We administered egocentric social network and computer-assisted survey interviews focused on demographic characteristics, health history, depressive symptoms, and ART adherence, where adherence was assessed by the number of reasons participants missed taking their medication, if ever. Significant univariate correlates of adherence were included in a multivariable regression analysis, where the moderating effect of having a network member who reminds participants to take their HIV medication on the relationship between depression and adherence was tested. Over 60% of participants reported clinically significant depressive symptoms; this was significantly associated with lower adherence among those without someone in their social network to remind them about taking their HIV medication, even after adjusting for covariates in an ordinary least squares regression (adjusted mean difference b = -1.61, SE = 0.42, p = 0.0003). Having a network member who reminds participants to take their ART medication significantly ameliorated the negative association between depression and treatment adherence, especially for those reporting greater depressive symptoms (p = 0.0394). Additionally, participants demonstrated high rates of technology use to communicate with social network members. In order to achieve the aims of the National HIV/AIDS Strategy, innovative interventions addressing mental health to improve ART adherence are needed. Network strategies that leverage technology may be helpful for improving ART

  14. Employees' negative and positive work-home interaction and their association with depressive symptoms.

    Science.gov (United States)

    Nitzsche, Anika; Jung, Julia; Pfaff, Holger; Driller, Elke

    2013-05-01

    Depression is the leading cause of disability and high costs worldwide. One possibility for preventing depression in the workplace, which has received little consideration so far, is the promotion of a successful balance between work and personal life. The aim of this study was to investigate employees' negative and positive work-home interaction and their association with depressive symptoms. A cross-sectional study was conducted in the micro- and nanotechnology sector in Germany. A stepwise multivariate logistic regression analysis was performed using data from N = 213 employees. The results suggest that while negative work-home interaction is associated with an increased risk for depressive symptoms, positive work-home interaction is correlated with a lower risk. Neither positive nor negative interaction in the home-to-work direction demonstrated a significant association with depressive symptoms. When attempting to prevent mental illnesses, such as depression, it is important to develop strategies aimed at reducing conflict between work and personal life and promoting a positive exchange between these two domains. Copyright © 2012 Wiley Periodicals, Inc.

  15. The role of rumination in the occurrence of positive effects of experienced traumatic events

    Directory of Open Access Journals (Sweden)

    Nina Ogińska-Bulik

    2016-07-01

    Full Text Available Background Cognitive processes play a significant role in both the negative and positive consequences of traumatic experiences. The aim of this research was to investigate the role of rumination in the occurrence of positive effects, in the form of posttraumatic growth, of experienced traumatic events. Participants and procedure Data were collected from 227 subjects who had experienced traumatic events, including cancer patients (31.30%, women who had experienced domestic violence (39.20%, and medical rescue workers exposed to traumatic events at work (29.50%. The age of participants ranged from 19 to 67 years (M = 40.12, SD = 13.28. The Posttraumatic Growth Inventory was used to measure positive changes, and the Event Related Rumination Inventory was used to assess the two types of ruminations (intrusive and deliberate. Results Both types of ruminations (intrusive and deliberate were positively correlated with the level of posttraumatic growth in the group of cancer patients, and deliberate ruminations were associated with posttraumatic growth in the group of women who had experienced domestic violence and in the medical rescue workers. The results of regression analysis confirmed a significant role of deliberate rumination. Conclusions The study of ruminations allows us to better explain the mechanisms underlying the consequences of traumatic experiences.

  16. Prognostic factors in advanced breast cancer: Race and receptor status are significant after development of metastasis.

    Science.gov (United States)

    Ren, Zhiyong; Li, Yufeng; Shen, Tiansheng; Hameed, Omar; Siegal, Gene P; Wei, Shi

    2016-01-01

    Prognostic factors are well established in early-stage breast cancer (BC), but less well-defined in advanced disease. We analyzed 323 BC patients who had distant relapse during follow-up from 1997 to 2010 to determine the significant clinicopathologic factors predicting survival outcomes. By univariate analysis, race, tumor grade, estrogen and progesterone receptors (ER/PR) and HER2 status were significantly associated with overall survival (OS) and post-metastasis survival (PMS). Applying a Cox regression model revealed that all these factors remained significant for PMS, while race, tumor grade and HER2 were independent factors for OS. Tumor grade was the only significant factor for metastasis-free survival by univariate and multivariate analyses. Our findings demonstrated that being Caucasian, hormonal receptor positive (HR+) and HER2 positive (HER2+) were all associated with a decreased hazard of death and that patients with HR+/HER2+ tumors had superior outcomes to those with HR+/HER2- disease. Further, PR status held a prognostic value over ER, thus reflecting the biologic mechanism of the importance of the functional ER pathway and the heterogeneity in the response to endocrine therapy. These observations indicate that the patients' genetic makeup and the intrinsic nature of the tumor principally govern BC progression and prognosticate the long-term outcomes in advanced disease. Copyright © 2015 Elsevier GmbH. All rights reserved.

  17. Height and Weight Estimation From Anthropometric Measurements Using Machine Learning Regressions.

    Science.gov (United States)

    Rativa, Diego; Fernandes, Bruno J T; Roque, Alexandre

    2018-01-01

    Height and weight are measurements explored to tracking nutritional diseases, energy expenditure, clinical conditions, drug dosages, and infusion rates. Many patients are not ambulant or may be unable to communicate, and a sequence of these factors may not allow accurate estimation or measurements; in those cases, it can be estimated approximately by anthropometric means. Different groups have proposed different linear or non-linear equations which coefficients are obtained by using single or multiple linear regressions. In this paper, we present a complete study of the application of different learning models to estimate height and weight from anthropometric measurements: support vector regression, Gaussian process, and artificial neural networks. The predicted values are significantly more accurate than that obtained with conventional linear regressions. In all the cases, the predictions are non-sensitive to ethnicity, and to gender, if more than two anthropometric parameters are analyzed. The learning model analysis creates new opportunities for anthropometric applications in industry, textile technology, security, and health care.

  18. Risk factors for positive margins in conservative surgery for breast cancer after neoadjuvant chemotherapy.

    Science.gov (United States)

    Bouzón, Alberto; Acea, Benigno; García, Alejandra; Iglesias, Ángela; Mosquera, Joaquín; Santiago, Paz; Seoane, Teresa

    2016-01-01

    Breast conservative surgery after neoadjuvant chemotherapy intends to remove any residual tumor with negative margins. The purpose of this study was to analyze the preoperative clinical-pathological factors influencing the margin status after conservative surgery in breast cancer patients receiving neoadjuvant chemotherapy. A retrospective study of 91 breast cancer patients undergoing neoadjuvant chemotherapy (92 breast lesions) during the period 2006 to 2013. A Cox regression analysis to identify baseline tumor characteristics associated with positive margins after breast conservative surgery was performed. Of all cases, 71 tumors were initially treated with conservative surgery after neoadjuvant chemotherapy. Pathologic exam revealed positive margins in 16 of the 71 cases (22.5%). The incidence of positive margins was significantly higher in cancers with initial size >5cm (P=.021), in cancers with low tumor grade (P=.031), and in patients with hormone receptor-positive cancer (P=.006). After a median follow-up of 45.2 months, 7 patients of the 71 treated with conservative surgery had disease recurrence (9.8%). There was no significant difference in terms of disease-free survival according to the margin status (P=.596). A baseline tumor size >5cm, low tumor grade and hormone receptor-positive status increase the risk for surgical margin involvement in breast conservative surgery after neoadjuvant chemotherapy. Copyright © 2016 AEC. Publicado por Elsevier España, S.L.U. All rights reserved.

  19. Age group differences in positive and negative affect among oldest-old adults: findings from the Georgia Centenarian Study.

    Science.gov (United States)

    Cho, Jinmyoung; Martin, Peter; Poon, Leonard W; MacDonald, M; Jazwinski, S M; Green, R C; Gearing, M; Johnson, M A; Markesbery, W R; Woodard, J L; Tenover, J S; Siegler, L C; Rott, C; Rodgers, W L; Hausman, D; Arnold, J; Davey, A

    2013-01-01

    The developmental adaptation model (Martin & Martin, 2002) provides insights into how current experiences and resources (proximal variables) and past experiences (distal variables) are correlated with outcomes (e.g., well-being) in later life. Applying this model, the current study examined proximal and distal variables associated with positive and negative affect in oldest-old adults, investigating age differences. Data from 306 octogenarians and centenarians who participated in Phase III of the Georgia Centenarian Study were used. Proximal variables included physical functioning, cognitive functioning, self-rated health, number of chronic conditions, social resources, and perceived economic status; distal variables included education, social productive activities, management of personal assets, and other learning experiences. Analysis of variance and block-wise regression analyses were conducted. Octogenarians showed significantly higher levels of positive emotion than centenarians. Cognitive functioning was significantly associated with positive affect, and number of health problems was significantly associated with negative affect after controlling for gender, ethnicity, residence, and marital status. Furthermore, four significant interaction effects suggested that positive affect significantly depended on the levels of cognitive and physical functioning among centenarians, whereas positive affect was dependent on the levels of physical health problems and learning experiences among octogenarians. Findings of this study addressed the importance of current and past experiences and resources in subjective well-being among oldest-old adults as a life-long process. Mechanisms connecting aging processes at the end of a long life to subjective well-being should be explored in future studies.

  20. Forecasting with Dynamic Regression Models

    CERN Document Server

    Pankratz, Alan

    2012-01-01

    One of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author's earlier work, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, the present text pulls together recent time series ideas and gives special attention to possible intertemporal patterns, distributed lag responses of output to input series and the auto correlation patterns of regression disturbance. It also includes six case studies.

  1. [A new method of calibration and positioning in quantitative analysis of multicomponents by single marker].

    Science.gov (United States)

    He, Bing; Yang, Shi-Yan; Zhang, Yan

    2012-12-01

    This paper aims to establish a new method of calibration and positioning in quantitative analysis of multicomponents by single marker (QAMS), using Shuanghuanglian oral liquid as the research object. Establishing relative correction factors with reference chlorogenic acid to other 11 active components (neochlorogenic acid, cryptochlorogenic acid, cafferic acid, forsythoside A, scutellarin, isochlorogenic acid B, isochlorogenic acid A, isochlorogenic acid C, baicalin and phillyrin wogonoside) in Shuanghuanglian oral liquid by 3 correction methods (multipoint correction, slope correction and quantitative factor correction). At the same time chromatographic peak was positioned by linear regression method. Only one standard uas used to determine the content of 12 components in Shuanghuanglian oral liquid, in stead of needing too many reference substance in quality control. The results showed that within the linear ranges, no significant differences were found in the quantitative results of 12 active constituents in 3 batches of Shuanghuanglian oral liquid determined by 3 correction methods and external standard method (ESM) or standard curve method (SCM). And this method is simpler and quicker than literature methods. The results were accurate and reliable, and had good reproducibility. While the positioning chromatographic peaks by linear regression method was more accurate than relative retention time in literature. The slope and the quantitative factor correction controlling the quality of Chinese traditional medicine is feasible and accurate.

  2. DETERMINATION OF FACTORS AFFECTING LENGTH OF STAY WITH MULTINOMIAL LOGISTIC REGRESSION IN TURKEY

    Directory of Open Access Journals (Sweden)

    Öğr. Gör. Rukiye NUMAN TEKİN

    2016-08-01

    Full Text Available Length of stay (LOS has important implications in various aspects of health services, can vary according to a wide range of factors. It is noticed that LOS has been neglected mostly in both theoratical studies and practice of health care management in Turkey. The main purpose of this study is to identify factors related to LOS in Turkey. A retrospective analysis of 2.255.836 patients hospitalized to private, university, foundation university and other (municipality, association and foreigners/minority hospitals hospitals which have an agreement with Social Security Institution (SSI in Turkey, from January 1, 2010, until the December 31, 2010, was examined. Patient’s data were taken from MEDULA (National Electronic Invoice System and SPSS 18.0 was used to perform statistical analysis. In this study t-test, one way anova and multinomial logistic regression are used to determine variables that may affect to LOS. The average LOS of patients was 3,93 days (SD = 5,882. LOS showed a statistically significant difference according to all independent variables used in the study (age, gender, disease class, type of hospitalization, presence of comorbidity, type and number of surgery, season of hospitalization, hospital ownership/bed capacity/ geographical region/residential area/type of service. According to the results of the multinomial lojistic regression analysis, LOS was negatively affected in terms of gender, presence of comorbidity, geographical region of hospital and was positively affected in terms of age, season of hospitalization, hospital bed capacity/ ownership/type of service/residential area.

  3. A regression approach for zircaloy-2 in-reactor creep constitutive equations

    International Nuclear Information System (INIS)

    Yung Liu, Y.; Bement, A.L.

    1977-01-01

    In this paper the methodology of multiple regressions as applied to zircaloy-2 in-reactor creep data analysis and construction of constitutive equation are illustrated. While the resulting constitutive equation can be used in creep analysis of in-reactor zircaloy structural components, the methodology itself is entirely general and can be applied to any creep data analysis. From data analysis and model development point of views, both the assumption of independence and prior committment to specific model forms are unacceptable. One would desire means which can not only estimate the required parameters directly from data but also provide basis for model selections, viz., one model against others. Basic understanding of the physics of deformation is important in choosing the forms of starting physical model equations, but the justifications must rely on their abilities in correlating the overall data. The promising aspects of multiple regression creep data analysis are briefly outlined as follows: (1) when there are more than one variable involved, there is no need to make the assumption that each variable affects the response independently. No separate normalizations are required either and the estimation of parameters is obtained by solving many simultaneous equations. The number of simultaneous equations is equal to the number of data sets, (2) regression statistics such as R 2 - and F-statistics provide measures of the significance of regression creep equation in correlating the overall data. The relative weights of each variable on the response can also be obtained. (3) Special regression techniques such as step-wise, ridge, and robust regressions and residual plots, etc., provide diagnostic tools for model selections

  4. Linear and evolutionary polynomial regression models to forecast coastal dynamics: Comparison and reliability assessment

    Science.gov (United States)

    Bruno, Delia Evelina; Barca, Emanuele; Goncalves, Rodrigo Mikosz; de Araujo Queiroz, Heithor Alexandre; Berardi, Luigi; Passarella, Giuseppe

    2018-01-01

    In this paper, the Evolutionary Polynomial Regression data modelling strategy has been applied to study small scale, short-term coastal morphodynamics, given its capability for treating a wide database of known information, non-linearly. Simple linear and multilinear regression models were also applied to achieve a balance between the computational load and reliability of estimations of the three models. In fact, even though it is easy to imagine that the more complex the model, the more the prediction improves, sometimes a "slight" worsening of estimations can be accepted in exchange for the time saved in data organization and computational load. The models' outcomes were validated through a detailed statistical, error analysis, which revealed a slightly better estimation of the polynomial model with respect to the multilinear model, as expected. On the other hand, even though the data organization was identical for the two models, the multilinear one required a simpler simulation setting and a faster run time. Finally, the most reliable evolutionary polynomial regression model was used in order to make some conjecture about the uncertainty increase with the extension of extrapolation time of the estimation. The overlapping rate between the confidence band of the mean of the known coast position and the prediction band of the estimated position can be a good index of the weakness in producing reliable estimations when the extrapolation time increases too much. The proposed models and tests have been applied to a coastal sector located nearby Torre Colimena in the Apulia region, south Italy.

  5. Cervix Regression and Motion During the Course of External Beam Chemoradiation for Cervical Cancer

    International Nuclear Information System (INIS)

    Beadle, Beth M.; Jhingran, Anuja; Salehpour, Mohammad; Sam, Marianne; Iyer, Revathy B.; Eifel, Patricia J.

    2009-01-01

    Purpose: To evaluate the magnitude of cervix regression and motion during external beam chemoradiation for cervical cancer. Methods and Materials: Sixteen patients with cervical cancer underwent computed tomography scanning before, weekly during, and after conventional chemoradiation. Cervix volumes were calculated to determine the extent of cervix regression. Changes in the center of mass and perimeter of the cervix between scans were used to determine the magnitude of cervix motion. Maximum cervix position changes were calculated for each patient, and mean maximum changes were calculated for the group. Results: Mean cervical volumes before and after 45 Gy of external beam irradiation were 97.0 and 31.9 cc, respectively; mean volume reduction was 62.3%. Mean maximum changes in the center of mass of the cervix were 2.1, 1.6, and 0.82 cm in the superior-inferior, anterior-posterior, and right-left lateral dimensions, respectively. Mean maximum changes in the perimeter of the cervix were 2.3 and 1.3 cm in the superior and inferior, 1.7 and 1.8 cm in the anterior and posterior, and 0.76 and 0.94 cm in the right and left lateral directions, respectively. Conclusions: Cervix regression and internal organ motion contribute to marked interfraction variations in the intrapelvic position of the cervical target in patients receiving chemoradiation for cervical cancer. Failure to take these variations into account during the application of highly conformal external beam radiation techniques poses a theoretical risk of underdosing the target or overdosing adjacent critical structures

  6. Estimating Loess Plateau Average Annual Precipitation with Multiple Linear Regression Kriging and Geographically Weighted Regression Kriging

    Directory of Open Access Journals (Sweden)

    Qiutong Jin

    2016-06-01

    Full Text Available Estimating the spatial distribution of precipitation is an important and challenging task in hydrology, climatology, ecology, and environmental science. In order to generate a highly accurate distribution map of average annual precipitation for the Loess Plateau in China, multiple linear regression Kriging (MLRK and geographically weighted regression Kriging (GWRK methods were employed using precipitation data from the period 1980–2010 from 435 meteorological stations. The predictors in regression Kriging were selected by stepwise regression analysis from many auxiliary environmental factors, such as elevation (DEM, normalized difference vegetation index (NDVI, solar radiation, slope, and aspect. All predictor distribution maps had a 500 m spatial resolution. Validation precipitation data from 130 hydrometeorological stations were used to assess the prediction accuracies of the MLRK and GWRK approaches. Results showed that both prediction maps with a 500 m spatial resolution interpolated by MLRK and GWRK had a high accuracy and captured detailed spatial distribution data; however, MLRK produced a lower prediction error and a higher variance explanation than GWRK, although the differences were small, in contrast to conclusions from similar studies.

  7. Identifying position, visibility, dimensions, and angulation of the ear.

    Science.gov (United States)

    Mohamed, Kasim; Christian, Jayanth; Jeyapalan, Karthigeyan; Natarajan, Shanmuganathan; Banu, Fathima; Veeravalli, Padmanabhan T

    2014-01-01

    We selected 254 subjects between the ages of 18 and 30 yr to assess the ear position, angulations of the ear in relation to the nose, visibility from the frontal view, and dimensions of the ear by using various anthropometric points of the face. Subjects were divided into four groups based on facial form. A reference plane indicator, facial topographical measurements, metal ruler, and digital photography were used. While considering the position of the ear, in all facial forms except square tapering, the most samples showed a tendency for the subaurale being in line with subnasale. Regression analysis showed a tendency to gnathion distance is the most dependent variable with length of the ear kept as a constant predictor, while both interalar distance and exocanthion to endocanthion distance correlate highly significantly to the width of the ear. In all subjects, the visibility of the ear when viewed from the front was an average of 1.5 mm. Regardless of facial form, ear angulation was generally less than nose angulation.

  8. Gibrat’s law and quantile regressions

    DEFF Research Database (Denmark)

    Distante, Roberta; Petrella, Ivan; Santoro, Emiliano

    2017-01-01

    The nexus between firm growth, size and age in U.S. manufacturing is examined through the lens of quantile regression models. This methodology allows us to overcome serious shortcomings entailed by linear regression models employed by much of the existing literature, unveiling a number of important...

  9. ON REGRESSION REPRESENTATIONS OF STOCHASTIC-PROCESSES

    NARCIS (Netherlands)

    RUSCHENDORF, L; DEVALK, [No Value

    We construct a.s. nonlinear regression representations of general stochastic processes (X(n))n is-an-element-of N. As a consequence we obtain in particular special regression representations of Markov chains and of certain m-dependent sequences. For m-dependent sequences we obtain a constructive

  10. Introduction to the use of regression models in epidemiology.

    Science.gov (United States)

    Bender, Ralf

    2009-01-01

    Regression modeling is one of the most important statistical techniques used in analytical epidemiology. By means of regression models the effect of one or several explanatory variables (e.g., exposures, subject characteristics, risk factors) on a response variable such as mortality or cancer can be investigated. From multiple regression models, adjusted effect estimates can be obtained that take the effect of potential confounders into account. Regression methods can be applied in all epidemiologic study designs so that they represent a universal tool for data analysis in epidemiology. Different kinds of regression models have been developed in dependence on the measurement scale of the response variable and the study design. The most important methods are linear regression for continuous outcomes, logistic regression for binary outcomes, Cox regression for time-to-event data, and Poisson regression for frequencies and rates. This chapter provides a nontechnical introduction to these regression models with illustrating examples from cancer research.

  11. From Rasch scores to regression

    DEFF Research Database (Denmark)

    Christensen, Karl Bang

    2006-01-01

    Rasch models provide a framework for measurement and modelling latent variables. Having measured a latent variable in a population a comparison of groups will often be of interest. For this purpose the use of observed raw scores will often be inadequate because these lack interval scale propertie....... This paper compares two approaches to group comparison: linear regression models using estimated person locations as outcome variables and latent regression models based on the distribution of the score....

  12. A consistent framework for Horton regression statistics that leads to a modified Hack's law

    Science.gov (United States)

    Furey, P.R.; Troutman, B.M.

    2008-01-01

    A statistical framework is introduced that resolves important problems with the interpretation and use of traditional Horton regression statistics. The framework is based on a univariate regression model that leads to an alternative expression for Horton ratio, connects Horton regression statistics to distributional simple scaling, and improves the accuracy in estimating Horton plot parameters. The model is used to examine data for drainage area A and mainstream length L from two groups of basins located in different physiographic settings. Results show that confidence intervals for the Horton plot regression statistics are quite wide. Nonetheless, an analysis of covariance shows that regression intercepts, but not regression slopes, can be used to distinguish between basin groups. The univariate model is generalized to include n > 1 dependent variables. For the case where the dependent variables represent ln A and ln L, the generalized model performs somewhat better at distinguishing between basin groups than two separate univariate models. The generalized model leads to a modification of Hack's law where L depends on both A and Strahler order ??. Data show that ?? plays a statistically significant role in the modified Hack's law expression. ?? 2008 Elsevier B.V.

  13. Producing The New Regressive Left

    DEFF Research Database (Denmark)

    Crone, Christine

    members, this thesis investigates a growing political trend and ideological discourse in the Arab world that I have called The New Regressive Left. On the premise that a media outlet can function as a forum for ideology production, the thesis argues that an analysis of this material can help to trace...... the contexture of The New Regressive Left. If the first part of the thesis lays out the theoretical approach and draws the contextual framework, through an exploration of the surrounding Arab media-and ideoscapes, the second part is an analytical investigation of the discourse that permeates the programmes aired...... becomes clear from the analytical chapters is the emergence of the new cross-ideological alliance of The New Regressive Left. This emerging coalition between Shia Muslims, religious minorities, parts of the Arab Left, secular cultural producers, and the remnants of the political,strategic resistance...

  14. How psychological and behavioral team states change during positive and negative momentum.

    Science.gov (United States)

    Den Hartigh, Ruud J R; Gernigon, Christophe; Van Yperen, Nico W; Marin, Ludovic; Van Geert, Paul L C

    2014-01-01

    In business and sports, teams often experience periods of positive and negative momentum while pursuing their goals. However, researchers have not yet been able to provide insights into how psychological and behavioral states actually change during positive and negative team momentum. In the current study we aimed to provide these insights by introducing an experimental dynamical research design. Rowing pairs had to compete against a virtual opponent on rowing ergometers, while a screen in front of the team broadcasted the ongoing race. The race was manipulated so that the team's rowing avatar gradually progressed (positive momentum) or regressed (negative momentum) in relation to the victory. The participants responded verbally to collective efficacy and task cohesion items appearing on the screen each minute. In addition, effort exertion and interpersonal coordination were continuously measured. Our results showed negative psychological changes (perceptions of collective efficacy and task cohesion) during negative team momentum, which were stronger than the positive changes during positive team momentum. Moreover, teams' exerted efforts rapidly decreased during negative momentum, whereas positive momentum accompanied a more variable and adaptive sequence of effort exertion. Finally, the interpersonal coordination was worse during negative momentum than during positive momentum. These results provide the first empirical insights into actual team momentum dynamics, and demonstrate how a dynamical research approach significantly contributes to current knowledge on psychological and behavioral processes.

  15. Differential item functioning analysis with ordinal logistic regression techniques. DIFdetect and difwithpar.

    Science.gov (United States)

    Crane, Paul K; Gibbons, Laura E; Jolley, Lance; van Belle, Gerald

    2006-11-01

    We present an ordinal logistic regression model for identification of items with differential item functioning (DIF) and apply this model to a Mini-Mental State Examination (MMSE) dataset. We employ item response theory ability estimation in our models. Three nested ordinal logistic regression models are applied to each item. Model testing begins with examination of the statistical significance of the interaction term between ability and the group indicator, consistent with nonuniform DIF. Then we turn our attention to the coefficient of the ability term in models with and without the group term. If including the group term has a marked effect on that coefficient, we declare that it has uniform DIF. We examined DIF related to language of test administration in addition to self-reported race, Hispanic ethnicity, age, years of education, and sex. We used PARSCALE for IRT analyses and STATA for ordinal logistic regression approaches. We used an iterative technique for adjusting IRT ability estimates on the basis of DIF findings. Five items were found to have DIF related to language. These same items also had DIF related to other covariates. The ordinal logistic regression approach to DIF detection, when combined with IRT ability estimates, provides a reasonable alternative for DIF detection. There appear to be several items with significant DIF related to language of test administration in the MMSE. More attention needs to be paid to the specific criteria used to determine whether an item has DIF, not just the technique used to identify DIF.

  16. Serum Potassium Is Positively Associated With Stroke and Mortality in the Large, Population-Based Malmö Preventive Project Cohort

    DEFF Research Database (Denmark)

    Johnson, Linda S; Mattsson, Nick; Sajadieh, Ahmad

    2017-01-01

    analyses. There were 2061 incident stroke events and 8709 deaths. Cox regression analyses adjusted for multiple stroke risk factors (age, sex, height, weight, systolic blood pressure, fasting blood glucose, serum sodium, current smoking, prevalent diabetes mellitus, prevalent coronary artery disease.......0001). This was significant in subjects both older and younger than the median age (46.5 years), and there was evidence of an interaction with serum sodium. The association was positive and significant for both ischemic stroke and intracerebral hemorrhage and in both hypertensive and normotensive subjects. CONCLUSIONS: Serum...

  17. Negative and Positive Peer Influence: Relations to Positive and Negative Behaviors for African American, European American, and Hispanic Adolescents

    Science.gov (United States)

    Padilla-Walker, Laura M.; Bean, Roy A.

    2009-01-01

    The purpose of the current study was to examine adolescents' perceptions of negative and positive peer influence (i.e., indirect peer association and direct peer pressure) as they related to adolescent behavior. Regression analyses were conducted using a sample of African American, European American, and Hispanic adolescents (N=1659, M age=16.06,…

  18. Mixture of Regression Models with Single-Index

    OpenAIRE

    Xiang, Sijia; Yao, Weixin

    2016-01-01

    In this article, we propose a class of semiparametric mixture regression models with single-index. We argue that many recently proposed semiparametric/nonparametric mixture regression models can be considered special cases of the proposed model. However, unlike existing semiparametric mixture regression models, the new pro- posed model can easily incorporate multivariate predictors into the nonparametric components. Backfitting estimates and the corresponding algorithms have been proposed for...

  19. Local bilinear multiple-output quantile/depth regression

    Czech Academy of Sciences Publication Activity Database

    Hallin, M.; Lu, Z.; Paindaveine, D.; Šiman, Miroslav

    2015-01-01

    Roč. 21, č. 3 (2015), s. 1435-1466 ISSN 1350-7265 R&D Projects: GA MŠk(CZ) 1M06047 Institutional support: RVO:67985556 Keywords : conditional depth * growth chart * halfspace depth * local bilinear regression * multivariate quantile * quantile regression * regression depth Subject RIV: BA - General Mathematics Impact factor: 1.372, year: 2015 http://library.utia.cas.cz/separaty/2015/SI/siman-0446857.pdf

  20. Optimism and positive and negative feelings in parents of young children with developmental delay.

    Science.gov (United States)

    Kurtz-Nelson, E; McIntyre, L L

    2017-07-01

    Parents' positive and negative feelings about their young children influence both parenting behaviour and child problem behaviour. Research has not previously examined factors that contribute to positive and negative feelings in parents of young children with developmental delay (DD). The present study sought to examine whether optimism, a known protective factor for parents of children with DD, was predictive of positive and negative feelings for these parents. Data were collected from 119 parents of preschool-aged children with developmental delay. Two separate hierarchical linear regression analyses were conducted to determine if optimism significantly predicted positive feelings and negative feelings and whether optimism moderated relations between parenting stress and parent feelings. Increased optimism was found to predict increased positive feelings and decreased negative feelings after controlling for child problem behaviour and parenting stress. In addition, optimism was found to moderate the relation between parenting stress and positive feelings. Results suggest that optimism may impact how parents perceive their children with DD. Future research should examine how positive and negative feelings impact positive parenting behaviour and the trajectory of problem behaviour specifically for children with DD. © 2017 MENCAP and International Association of the Scientific Study of Intellectual and Developmental Disabilities and John Wiley & Sons Ltd.

  1. The effect of positive and negative memory bias on anxiety and depression symptoms among adolescents.

    Science.gov (United States)

    Ho, Samuel M Y; Cheng, Joseph; Dai, Darren Wai Tong; Tam, Titian; Hui, Otilia

    2018-02-28

    To examine the interaction effect of anxiety and depression on the intentional forgetting of positive and negative valence words. One hundred fifty-five grade 7 to grade 10 students participated in the study. The item-method directed forgetting paradigm was used to examine the intentional forgetting of positive-valence, negative-valence, and neutral-valence words. Negative-valence words were recognized better than either positive-valence or neutral-valence words. The results revealed an anxiety main effect (p = .01, LLCI = -.09, and ULCI = -.01) and a depression main effect (p = .04, LLCI = .00, and ULCI = .24). The anxiety score was negative, whereas the depression score was positively related to the directed forgetting of negative-valence words. Regression-based moderation analysis revealed a significant anxiety × depression interaction effect on the directed forgetting of positive-valence words (p = .02, LLCI = .00, and ULCI = .01). Greater anxiety was associated with more directed forgetting of positive-valance words only among participants with high depression scores. With negative-valence words, the anxiety × depression interaction effect was not significant (p = .15, LLCI = - .00, and ULCI = .01). Therapeutic strategies to increase positive memory bias may reduce anxiety symptoms only among those with high depression scores. Interventions to reduce negative memory bias may reduce anxiety symptoms irrespective of levels of depression. © 2018 Wiley Periodicals, Inc.

  2. The MIDAS Touch: Mixed Data Sampling Regression Models

    OpenAIRE

    Ghysels, Eric; Santa-Clara, Pedro; Valkanov, Rossen

    2004-01-01

    We introduce Mixed Data Sampling (henceforth MIDAS) regression models. The regressions involve time series data sampled at different frequencies. Technically speaking MIDAS models specify conditional expectations as a distributed lag of regressors recorded at some higher sampling frequencies. We examine the asymptotic properties of MIDAS regression estimation and compare it with traditional distributed lag models. MIDAS regressions have wide applicability in macroeconomics and �nance.

  3. Suppression Situations in Multiple Linear Regression

    Science.gov (United States)

    Shieh, Gwowen

    2006-01-01

    This article proposes alternative expressions for the two most prevailing definitions of suppression without resorting to the standardized regression modeling. The formulation provides a simple basis for the examination of their relationship. For the two-predictor regression, the author demonstrates that the previous results in the literature are…

  4. Regression calibration with more surrogates than mismeasured variables

    KAUST Repository

    Kipnis, Victor

    2012-06-29

    In a recent paper (Weller EA, Milton DK, Eisen EA, Spiegelman D. Regression calibration for logistic regression with multiple surrogates for one exposure. Journal of Statistical Planning and Inference 2007; 137: 449-461), the authors discussed fitting logistic regression models when a scalar main explanatory variable is measured with error by several surrogates, that is, a situation with more surrogates than variables measured with error. They compared two methods of adjusting for measurement error using a regression calibration approximate model as if it were exact. One is the standard regression calibration approach consisting of substituting an estimated conditional expectation of the true covariate given observed data in the logistic regression. The other is a novel two-stage approach when the logistic regression is fitted to multiple surrogates, and then a linear combination of estimated slopes is formed as the estimate of interest. Applying estimated asymptotic variances for both methods in a single data set with some sensitivity analysis, the authors asserted superiority of their two-stage approach. We investigate this claim in some detail. A troubling aspect of the proposed two-stage method is that, unlike standard regression calibration and a natural form of maximum likelihood, the resulting estimates are not invariant to reparameterization of nuisance parameters in the model. We show, however, that, under the regression calibration approximation, the two-stage method is asymptotically equivalent to a maximum likelihood formulation, and is therefore in theory superior to standard regression calibration. However, our extensive finite-sample simulations in the practically important parameter space where the regression calibration model provides a good approximation failed to uncover such superiority of the two-stage method. We also discuss extensions to different data structures.

  5. Regression calibration with more surrogates than mismeasured variables

    KAUST Repository

    Kipnis, Victor; Midthune, Douglas; Freedman, Laurence S.; Carroll, Raymond J.

    2012-01-01

    In a recent paper (Weller EA, Milton DK, Eisen EA, Spiegelman D. Regression calibration for logistic regression with multiple surrogates for one exposure. Journal of Statistical Planning and Inference 2007; 137: 449-461), the authors discussed fitting logistic regression models when a scalar main explanatory variable is measured with error by several surrogates, that is, a situation with more surrogates than variables measured with error. They compared two methods of adjusting for measurement error using a regression calibration approximate model as if it were exact. One is the standard regression calibration approach consisting of substituting an estimated conditional expectation of the true covariate given observed data in the logistic regression. The other is a novel two-stage approach when the logistic regression is fitted to multiple surrogates, and then a linear combination of estimated slopes is formed as the estimate of interest. Applying estimated asymptotic variances for both methods in a single data set with some sensitivity analysis, the authors asserted superiority of their two-stage approach. We investigate this claim in some detail. A troubling aspect of the proposed two-stage method is that, unlike standard regression calibration and a natural form of maximum likelihood, the resulting estimates are not invariant to reparameterization of nuisance parameters in the model. We show, however, that, under the regression calibration approximation, the two-stage method is asymptotically equivalent to a maximum likelihood formulation, and is therefore in theory superior to standard regression calibration. However, our extensive finite-sample simulations in the practically important parameter space where the regression calibration model provides a good approximation failed to uncover such superiority of the two-stage method. We also discuss extensions to different data structures.

  6. Is Posidonia oceanica regression a general feature in the Mediterranean Sea?

    Directory of Open Access Journals (Sweden)

    M. BONACORSI

    2013-03-01

    Full Text Available Over the last few years, a widespread regression of Posidonia oceanica meadows has been noticed in the Mediterranean Sea. However, the magnitude of this decline is still debated. The objectives of this study are (i to assess the spatio-temporal evolution of Posidonia oceanica around Cap Corse (Corsica over time comparing available ancient maps (from 1960 with a new (2011 detailed map realized combining different techniques (aerial photographs, SSS, ROV, scuba diving; (ii evaluate the reliability of ancient maps; (iii discuss observed regression of the meadows in relation to human pressure along the 110 km of coast. Thus, the comparison with previous data shows that, apart from sites clearly identified with the actual evolution, there is a relative stability of the surfaces occupied by the seagrass Posidonia oceanica. The recorded differences seem more related to changes in mapping techniques. These results confirm that in areas characterized by a moderate anthropogenic impact, the Posidonia oceanica meadow has no significant regression and that the changes due to the evolution of mapping techniques are not negligible. However, others facts should be taken into account before extrapolating to the Mediterranean Sea (e.g. actually mapped surfaces and assessing the amplitude of the actual regression.

  7. Constrained statistical inference: sample-size tables for ANOVA and regression

    Directory of Open Access Journals (Sweden)

    Leonard eVanbrabant

    2015-01-01

    Full Text Available Researchers in the social and behavioral sciences often have clear expectations about the order/direction of the parameters in their statistical model. For example, a researcher might expect that regression coefficient beta1 is larger than beta2 and beta3. The corresponding hypothesis is H: beta1 > {beta2, beta3} and this is known as an (order constrained hypothesis. A major advantage of testing such a hypothesis is that power can be gained and inherently a smaller sample size is needed. This article discusses this gain in sample size reduction, when an increasing number of constraints is included into the hypothesis. The main goal is to present sample-size tables for constrained hypotheses. A sample-size table contains the necessary sample-size at a prespecified power (say, 0.80 for an increasing number of constraints. To obtain sample-size tables, two Monte Carlo simulations were performed, one for ANOVA and one for multiple regression. Three results are salient. First, in an ANOVA the needed sample-size decreases with 30% to 50% when complete ordering of the parameters is taken into account. Second, small deviations from the imposed order have only a minor impact on the power. Third, at the maximum number of constraints, the linear regression results are comparable with the ANOVA results. However, in the case of fewer constraints, ordering the parameters (e.g., beta1 > beta2 results in a higher power than assigning a positive or a negative sign to the parameters (e.g., beta1 > 0.

  8. Few crystal balls are crystal clear : eyeballing regression

    International Nuclear Information System (INIS)

    Wittebrood, R.T.

    1998-01-01

    The theory of regression and statistical analysis as it applies to reservoir analysis was discussed. It was argued that regression lines are not always the final truth. It was suggested that regression lines and eyeballed lines are often equally accurate. The many conditions that must be fulfilled to calculate a proper regression were discussed. Mentioned among these conditions were the distribution of the data, hidden variables, knowledge of how the data was obtained, the need for causal correlation of the variables, and knowledge of the manner in which the regression results are going to be used. 1 tab., 13 figs

  9. Situational motivation and perceived intensity: their interaction in predicting changes in positive affect from physical activity.

    Science.gov (United States)

    Guérin, Eva; Fortier, Michelle S

    2012-01-01

    There is evidence that affective experiences surrounding physical activity can contribute to the proper self-regulation of an active lifestyle. Motivation toward physical activity, as portrayed by self-determination theory, has been linked to positive affect, as has the intensity of physical activity, especially of a preferred nature. The purpose of this experimental study was to examine the interaction between situational motivation and intensity [i.e., ratings of perceived exertion (RPE)] in predicting changes in positive affect following an acute bout of preferred physical activity, namely, running. Fourty-one female runners engaged in a 30-minute self-paced treadmill run in a laboratory context. Situational motivation for running, pre- and post-running positive affect, and RPE were assessed via validated self-report questionnaires. Hierarchical regression analyses revealed a significant interaction effect between RPE and introjection (P positive affect was considerable, with higher RPE ratings being associated with greater increases in positive affect. The implications of the findings in light of SDT principles as well as the potential contingencies between the regulations and RPE in predicting positive affect among women are discussed.

  10. Self-management and quality of life in chronic obstructive pulmonary disease (COPD): The mediating effects of positive affect.

    Science.gov (United States)

    Benzo, Roberto P; Abascal-Bolado, Beatriz; Dulohery, Megan M

    2016-04-01

    This study aimed to increase our understanding of general self-management (SM) abilities in COPD by determining if SM can predict disease specific quality of life (QoL), by investigating whether specific SM domains are significant in COPD and by exploring the mediating effect of the positive/negative affect in the association between SM and QoL. Cross-sectional study based on 292 patients with COPD. Measures included demographics, lung function, gait speed, health care utilization, positive/negative affect, SM abilities, breathlessness and disease specific QoL. We performed, correlation, multiple regression models and mediation analysis (positive/negative affect being mediator between SM and QoL association). After controlling for breathlessness, living alone, marital status, hospitalization history, age and lung function, SM related to QoL (pnegative affect ratio completely mediates the relationship of SM with QoL. SM is independently associated with disease specific QoL in COPD after adjustment significant covariates but positive/negative affect ratio completely mediates the relationship of SM with QoL. Measuring positive/negative affect and addressing investment behavior and self-efficacy are important in implementing COPD-SM programs. Copyright © 2015 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

  11. Infantile myofibroma of the zygomatoco-maxillo-orbital complex: Case report with spontaneous regression

    Directory of Open Access Journals (Sweden)

    K. Arab

    2016-12-01

    Conclusion: Radiologically aggressive infantile myofibroma has been previously treated by surgical intervention. In this case report there was a significant spontaneous regression. Conservative treatment and follow-up may be an appropriate alternative.

  12. Regression methods for medical research

    CERN Document Server

    Tai, Bee Choo

    2013-01-01

    Regression Methods for Medical Research provides medical researchers with the skills they need to critically read and interpret research using more advanced statistical methods. The statistical requirements of interpreting and publishing in medical journals, together with rapid changes in science and technology, increasingly demands an understanding of more complex and sophisticated analytic procedures.The text explains the application of statistical models to a wide variety of practical medical investigative studies and clinical trials. Regression methods are used to appropriately answer the

  13. Evaluation of logistic regression models and effect of covariates for case-control study in RNA-Seq analysis.

    Science.gov (United States)

    Choi, Seung Hoan; Labadorf, Adam T; Myers, Richard H; Lunetta, Kathryn L; Dupuis, Josée; DeStefano, Anita L

    2017-02-06

    Next generation sequencing provides a count of RNA molecules in the form of short reads, yielding discrete, often highly non-normally distributed gene expression measurements. Although Negative Binomial (NB) regression has been generally accepted in the analysis of RNA sequencing (RNA-Seq) data, its appropriateness has not been exhaustively evaluated. We explore logistic regression as an alternative method for RNA-Seq studies designed to compare cases and controls, where disease status is modeled as a function of RNA-Seq reads using simulated and Huntington disease data. We evaluate the effect of adjusting for covariates that have an unknown relationship with gene expression. Finally, we incorporate the data adaptive method in order to compare false positive rates. When the sample size is small or the expression levels of a gene are highly dispersed, the NB regression shows inflated Type-I error rates but the Classical logistic and Bayes logistic (BL) regressions are conservative. Firth's logistic (FL) regression performs well or is slightly conservative. Large sample size and low dispersion generally make Type-I error rates of all methods close to nominal alpha levels of 0.05 and 0.01. However, Type-I error rates are controlled after applying the data adaptive method. The NB, BL, and FL regressions gain increased power with large sample size, large log2 fold-change, and low dispersion. The FL regression has comparable power to NB regression. We conclude that implementing the data adaptive method appropriately controls Type-I error rates in RNA-Seq analysis. Firth's logistic regression provides a concise statistical inference process and reduces spurious associations from inaccurately estimated dispersion parameters in the negative binomial framework.

  14. Should metacognition be measured by logistic regression?

    Science.gov (United States)

    Rausch, Manuel; Zehetleitner, Michael

    2017-03-01

    Are logistic regression slopes suitable to quantify metacognitive sensitivity, i.e. the efficiency with which subjective reports differentiate between correct and incorrect task responses? We analytically show that logistic regression slopes are independent from rating criteria in one specific model of metacognition, which assumes (i) that rating decisions are based on sensory evidence generated independently of the sensory evidence used for primary task responses and (ii) that the distributions of evidence are logistic. Given a hierarchical model of metacognition, logistic regression slopes depend on rating criteria. According to all considered models, regression slopes depend on the primary task criterion. A reanalysis of previous data revealed that massive numbers of trials are required to distinguish between hierarchical and independent models with tolerable accuracy. It is argued that researchers who wish to use logistic regression as measure of metacognitive sensitivity need to control the primary task criterion and rating criteria. Copyright © 2017 Elsevier Inc. All rights reserved.

  15. Neoclassical versus Frontier Production Models ? Testing for the Skewness of Regression Residuals

    DEFF Research Database (Denmark)

    Kuosmanen, T; Fosgerau, Mogens

    2009-01-01

    The empirical literature on production and cost functions is divided into two strands. The neoclassical approach concentrates on model parameters, while the frontier approach decomposes the disturbance term to a symmetric noise term and a positively skewed inefficiency term. We propose a theoreti......The empirical literature on production and cost functions is divided into two strands. The neoclassical approach concentrates on model parameters, while the frontier approach decomposes the disturbance term to a symmetric noise term and a positively skewed inefficiency term. We propose...... a theoretical justification for the skewness of the inefficiency term, arguing that this skewness is the key testable hypothesis of the frontier approach. We propose to test the regression residuals for skewness in order to distinguish the two competing approaches. Our test builds directly upon the asymmetry...

  16. Noninvasive scoring system for significant inflammation related to chronic hepatitis B

    Science.gov (United States)

    Hong, Mei-Zhu; Ye, Linglong; Jin, Li-Xin; Ren, Yan-Dan; Yu, Xiao-Fang; Liu, Xiao-Bin; Zhang, Ru-Mian; Fang, Kuangnan; Pan, Jin-Shui

    2017-03-01

    Although a liver stiffness measurement-based model can precisely predict significant intrahepatic inflammation, transient elastography is not commonly available in a primary care center. Additionally, high body mass index and bilirubinemia have notable effects on the accuracy of transient elastography. The present study aimed to create a noninvasive scoring system for the prediction of intrahepatic inflammatory activity related to chronic hepatitis B, without the aid of transient elastography. A total of 396 patients with chronic hepatitis B were enrolled in the present study. Liver biopsies were performed, liver histology was scored using the Scheuer scoring system, and serum markers and liver function were investigated. Inflammatory activity scoring models were constructed for both hepatitis B envelope antigen (+) and hepatitis B envelope antigen (-) patients. The sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve were 86.00%, 84.80%, 62.32%, 95.39%, and 0.9219, respectively, in the hepatitis B envelope antigen (+) group and 91.89%, 89.86%, 70.83%, 97.64%, and 0.9691, respectively, in the hepatitis B envelope antigen (-) group. Significant inflammation related to chronic hepatitis B can be predicted with satisfactory accuracy by using our logistic regression-based scoring system.

  17. High serum coenzyme Q10, positively correlated with age, selenium and cholesterol in Inuit of Greenland

    DEFF Research Database (Denmark)

    Pedersen, Henning Sloth; Mortensen, S.A.; Rhode, M.

    1999-01-01

    impact. From a health survey we chose the subpopulation from the most remote area, where the traditional Greenlandic diet with high intake of sea mammals and fish predominates. The mean (SD) of S-CoQ10 in males was 1.495 (0.529) nmol/ml and 1.421 (0.629) nmol/ml in females, significantly higher (p ....001) compared to a Danish population. In a linear multiple regression model the S-CoQ10 level is significantly positively associated with age and S-selenium in males, and S-total cholesterol in females. The high level of CoQ10 in Greenlanders probably reflects diet, since no bioaccumulation takes place...

  18. BOX-COX REGRESSION METHOD IN TIME SCALING

    Directory of Open Access Journals (Sweden)

    ATİLLA GÖKTAŞ

    2013-06-01

    Full Text Available Box-Cox regression method with λj, for j = 1, 2, ..., k, power transformation can be used when dependent variable and error term of the linear regression model do not satisfy the continuity and normality assumptions. The situation obtaining the smallest mean square error  when optimum power λj, transformation for j = 1, 2, ..., k, of Y has been discussed. Box-Cox regression method is especially appropriate to adjust existence skewness or heteroscedasticity of error terms for a nonlinear functional relationship between dependent and explanatory variables. In this study, the advantage and disadvantage use of Box-Cox regression method have been discussed in differentiation and differantial analysis of time scale concept.

  19. Substitution elasticities between GHG-polluting and nonpolluting inputs in agricultural production: A meta-regression

    International Nuclear Information System (INIS)

    Liu, Boying; Richard Shumway, C.

    2016-01-01

    This paper reports meta-regressions of substitution elasticities between greenhouse gas (GHG) polluting and nonpolluting inputs in agricultural production, which is the main feedstock source for biofuel in the U.S. We treat energy, fertilizer, and manure collectively as the “polluting input” and labor, land, and capital as nonpolluting inputs. We estimate meta-regressions for samples of Morishima substitution elasticities for labor, land, and capital vs. the polluting input. Much of the heterogeneity of Morishima elasticities can be explained by type of primal or dual function, functional form, type and observational level of data, input categories, number of outputs, type of output, time period, and country categories. Each estimated long-run elasticity for the reference case, which is most relevant for assessing GHG emissions through life-cycle analysis, is greater than 1.0 and significantly different from zero. Most predicted long-run elasticities remain significantly different from zero at the data means. These findings imply that life-cycle analysis based on fixed proportion production functions could provide grossly inaccurate measures of GHG of biofuel. - Highlights: • This paper reports meta-regressions of substitution elasticities between greenhouse-gas (GHG) polluting and nonpolluting inputs in agricultural production, which is the main feedstock source for biofuel in the U.S. • We estimate meta-regressions for samples of Morishima substitution elasticities for labor, land, and capital vs. the polluting input based on 65 primary studies. • We found that each estimated long-run elasticity for the reference case, which is most relevant for assessing GHG emissions through life-cycle analysis, is greater than 1.0 and significantly different from zero. Most predicted long-run elasticities remain significantly different from zero at the data means. • These findings imply that life-cycle analysis based on fixed proportion production functions could

  20. Gaussian Process Regression Model in Spatial Logistic Regression

    Science.gov (United States)

    Sofro, A.; Oktaviarina, A.

    2018-01-01

    Spatial analysis has developed very quickly in the last decade. One of the favorite approaches is based on the neighbourhood of the region. Unfortunately, there are some limitations such as difficulty in prediction. Therefore, we offer Gaussian process regression (GPR) to accommodate the issue. In this paper, we will focus on spatial modeling with GPR for binomial data with logit link function. The performance of the model will be investigated. We will discuss the inference of how to estimate the parameters and hyper-parameters and to predict as well. Furthermore, simulation studies will be explained in the last section.

  1. Serum BDNF Is Positively Associated With Negative Symptoms in Older Adults With Schizophrenia.

    Science.gov (United States)

    Binford, Sasha S; Hubbard, Erin M; Flowers, Elena; Miller, Bruce L; Leutwyler, Heather

    2018-01-01

    Older adults with chronic schizophrenia are at greater risk for functional disability and poorer health outcomes than those without serious mental illness. These individuals comprise 1-2% of the elderly population in the United States and are projected to number approximately 15 million by 2030. The symptoms of schizophrenia can be disabling for individuals, significantly reducing quality of life. Often, the negative symptoms (NS) are the most resistant to treatment and are considered a marker of illness severity, though they are challenging to measure objectively. Biomarkers can serve as objective indicators of health status. Brain-derived neurotrophic factor (BDNF) is a potential biomarker for schizophrenia and may serve as an important indicator of illness severity. A cross-sectional study with 30 older adults with chronic schizophrenia. Participants were assessed on serum levels of BDNF and psychiatric symptoms (Positive and Negative Syndrome Scale). Pearson's bivariate correlations (two-tailed) and linear regression models were used. A significant positive association ( p schizophrenia. It is possible that higher serum levels of BDNF reflect compensatory neuronal mechanisms resulting from neurodevelopmental dysfunction.

  2. DNA-Cytometry of Progressive and Regressive Cervical Intraepithelial Neoplasia

    Directory of Open Access Journals (Sweden)

    Antonius G. J. M. Hanselaar

    1998-01-01

    Full Text Available A retrospective analysis was performed on archival cervical smears from a group of 56 women with cervical intraepithelial neoplasia (CIN, who had received follow‐up by cytology only. Automated image cytometry of Feulgen‐stained DNA was used to determine the differences between progressive and regressive lesions. The first group of 30 smears was from women who had developed cancer after initial smears with dysplastic changes (progressive group. The second group of 26 smears with dysplastic changes had shown regression to normal (regressive group. The goal of the study was to determine if differences in cytometric features existed between the progressive and regressive groups. CIN categories I, II and III were represented in both groups, and measurements were pooled across diagnostic categories. Images of up to 700 intermediate cells were obtained from each slide, and cells were scanned exhaustively for the detection of diagnostic cells. Discriminant function analysis was performed for both intermediate and diagnostic cells. The most significant differences between the groups were found for diagnostic cells, with a cell classification accuracy of 82%. Intermediate cells could be classified with 60% accuracy. Cytometric features which afforded the best discrimination were characteristic of the chromatin organization in diagnostic cells (nuclear texture. Slide classification was performed by thresholding the number of cells which exhibited progression associated changes (PAC in chromatin configuration, with an accuracy of 93 and 73% for diagnostic and intermediate cells, respectively. These results indicate that regardless of the extent of nuclear atypia as reflected in the CIN category, features of chromatin organization can potentially be used to predict the malignant or progressive potential of CIN lesions.

  3. Regression Analysis and the Sociological Imagination

    Science.gov (United States)

    De Maio, Fernando

    2014-01-01

    Regression analysis is an important aspect of most introductory statistics courses in sociology but is often presented in contexts divorced from the central concerns that bring students into the discipline. Consequently, we present five lesson ideas that emerge from a regression analysis of income inequality and mortality in the USA and Canada.

  4. Incremental validity of positive and negative valence in predicting personality disorder.

    Science.gov (United States)

    Simms, Leonard J; Yufik, Tom; Gros, Daniel F

    2010-04-01

    The Big Seven model of personality includes five dimensions similar to the Big Five model as well as two evaluative dimensions—Positive Valence (PV) and Negative Valence (NV)—which reflect extremely positive and negative person descriptors, respectively. Recent theory and research have suggested that PV and NV predict significant variance in personality disorder (PD) above that predicted by the Big Five, but firm conclusions have not been possible because previous studies have been limited to only single measures of PV, NV, and the Big Five traits. In the present study, we replicated and extended previous findings using three markers of all key constructs—including PV, NV, and the Big Five—in a diverse sample of 338 undergraduates. Results of hierarchical multiple regression analyses revealed that PV incrementally predicted Narcissistic and Histrionic PDs above the Big Five and that NV nonspecifically incremented the prediction of most PDs. Implications for dimensional models of personality pathology are discussed. PsycINFO Database Record (c) 2010 APA, all rights reserved.

  5. Addendum to the article: Misuse of null hypothesis significance testing: Would estimation of positive and negative predictive values improve certainty of chemical risk assessment?

    Science.gov (United States)

    Bundschuh, Mirco; Newman, Michael C; Zubrod, Jochen P; Seitz, Frank; Rosenfeldt, Ricki R; Schulz, Ralf

    2015-03-01

    We argued recently that the positive predictive value (PPV) and the negative predictive value (NPV) are valuable metrics to include during null hypothesis significance testing: They inform the researcher about the probability of statistically significant and non-significant test outcomes actually being true. Although commonly misunderstood, a reported p value estimates only the probability of obtaining the results or more extreme results if the null hypothesis of no effect was true. Calculations of the more informative PPV and NPV require a priori estimate of the probability (R). The present document discusses challenges of estimating R.

  6. Predicting Factors of INSURE Failure in Low Birth Weight Neonates with RDS; A Logistic Regression Model

    Directory of Open Access Journals (Sweden)

    Bita Najafian

    2015-02-01

    Full Text Available Background:Respiratory Distress syndrome is the most common respiratory disease in premature neonate and the most important cause of death among them. We aimed to investigate factors to predict successful or failure of INSURE method as a therapeutic method of RDS.Methods:In a cohort study,45 neonates with diagnosed RDS and birth weight lower than 1500g were included and they underwent INSURE followed by NCPAP(Nasal Continuous Positive Airway Pressure. The patients were divided into failure or successful groups and factors which can predict success of INSURE were investigated by logistic regression in SPSS 16th version.Results:29 and16 neonates were observed in successful and failure groups, respectively. Birth weight was the only variable with significant difference between two groups (P=0.002. Finally logistic regression test showed that birth weight is only predicting factor for success (P: 0.001, EXP[β]: 0.009, CI [95%]: 1.003-0.014 and mortality (P: 0.029, EXP[β]: 0.993, CI [95%]: 0.987-0.999 of neonates treated with INSURE method.Conclusion:Predicting factors which affect on success rate of INSURE can be useful for treating and reducing charge of neonate with RDS and the birth weight is one of the effective factor on INSURE Success in this study.

  7. Predicting Factors of INSURE Failure in Low Birth Weight Neonates with RDS; A Logistic Regression Model

    Directory of Open Access Journals (Sweden)

    Bita Najafian

    2015-02-01

    Full Text Available Background:Respiratory Distress syndrome is the most common respiratory disease in premature neonate and the most important cause of death among them. We aimed to investigate factors to predict successful or failure of INSURE method as a therapeutic method of RDS. Methods:In a cohort study,45 neonates with diagnosed RDS and birth weight lower than 1500g were included and they underwent INSURE followed by NCPAP(Nasal Continuous Positive Airway Pressure. The patients were divided into failure or successful groups and factors which can predict success of INSURE were investigated by logistic regression in SPSS 16th version. Results:29 and16 neonates were observed in successful and failure groups, respectively. Birth weight was the only variable with significant difference between two groups (P=0.002. Finally logistic regression test showed that birth weight is only predicting factor for success (P: 0.001, EXP[β]: 0.009, CI [95%]: 1.003-0.014 and mortality (P: 0.029, EXP[β]: 0.993, CI [95%]: 0.987-0.999 of neonates treated with INSURE method. Conclusion:Predicting factors which affect on success rate of INSURE can be useful for treating and reducing charge of neonate with RDS and the birth weight is one of the effective factor on INSURE Success in this study.

  8. An Additive-Multiplicative Cox-Aalen Regression Model

    DEFF Research Database (Denmark)

    Scheike, Thomas H.; Zhang, Mei-Jie

    2002-01-01

    Aalen model; additive risk model; counting processes; Cox regression; survival analysis; time-varying effects......Aalen model; additive risk model; counting processes; Cox regression; survival analysis; time-varying effects...

  9. The significance of lower jaw position in relation to postural stability. Comparison of a premanufactured occlusal splint with the Dental Power Splint.

    Science.gov (United States)

    Ohlendorf, D; Riegel, M; Lin Chung, T; Kopp, S

    2013-01-01

    The purpose of this study was to investigate the effects on postural stability of two different lower jaw positions held in place by splints with eyes open and eyes closed. The postural stability in 21 healthy adult volunteers was investigated using two different sets of occlusal conditions with the lower jaw being at rest either with the eyes opened or closed. Two occlusal splints (standard splint and DPS splint) were used in order to maintain this lower jaw position. The balance behaviour was recorded using a balance platform. In a comparison of the habitual occlusion with the two occlusal splints, the balance posturographic values with the eyes opened fell between 7-9% and those for weight distribution with the eyes closed between 22-26% (with greater improvement being achieved with DPS) with the result that the variability in the range of fluctuations was reduced. The level of positioning accuracy deteriorated with the wearing of a splint between 13% with the DPS splint and 30% with the standard splint. Gender-specific differences of minor importance in relation to the positioning accuracy were recorded, with there being significant differences in the female participants (P≤0.00). An occlusal change in the stomatognathic system impacts on postural stability. Balance deficits seem to correlate with deteriorated body sway, which, according to the results, can be improved by a myocentric bite position using a DPS splint. This is more the case with the eyes closed than with the eyes opened.

  10. Clinical evaluation of a novel population-based regression analysis for detecting glaucomatous visual field progression.

    Science.gov (United States)

    Kovalska, M P; Bürki, E; Schoetzau, A; Orguel, S F; Orguel, S; Grieshaber, M C

    2011-04-01

    The distinction of real progression from test variability in visual field (VF) series may be based on clinical judgment, on trend analysis based on follow-up of test parameters over time, or on identification of a significant change related to the mean of baseline exams (event analysis). The aim of this study was to compare a new population-based method (Octopus field analysis, OFA) with classic regression analyses and clinical judgment for detecting glaucomatous VF changes. 240 VF series of 240 patients with at least 9 consecutive examinations available were included into this study. They were independently classified by two experienced investigators. The results of such a classification served as a reference for comparison for the following statistical tests: (a) t-test global, (b) r-test global, (c) regression analysis of 10 VF clusters and (d) point-wise linear regression analysis. 32.5 % of the VF series were classified as progressive by the investigators. The sensitivity and specificity were 89.7 % and 92.0 % for r-test, and 73.1 % and 93.8 % for the t-test, respectively. In the point-wise linear regression analysis, the specificity was comparable (89.5 % versus 92 %), but the sensitivity was clearly lower than in the r-test (22.4 % versus 89.7 %) at a significance level of p = 0.01. A regression analysis for the 10 VF clusters showed a markedly higher sensitivity for the r-test (37.7 %) than the t-test (14.1 %) at a similar specificity (88.3 % versus 93.8 %) for a significant trend (p = 0.005). In regard to the cluster distribution, the paracentral clusters and the superior nasal hemifield progressed most frequently. The population-based regression analysis seems to be superior to the trend analysis in detecting VF progression in glaucoma, and may eliminate the drawbacks of the event analysis. Further, it may assist the clinician in the evaluation of VF series and may allow better visualization of the correlation between function and structure owing to VF

  11. Dimensional changes in plaster cast models due to the position of the impression tray during setting

    Directory of Open Access Journals (Sweden)

    Betina Grehs Porto

    2014-01-01

    Full Text Available Introduction: The objective of this study was to assess whether the positioning of the impression tray could cause distortion to plaster casts during gypsum setting time.Materials and Methods: Fifteen pairs of master models were cast with alginate impression material and immediately filled with gypsum. Impressions were allowed to set with the tray in the noninverted position (Group A or in the inverted position (Group B. The plaster models were digitized using a laser scanner (3Shape R-700, 3Shape A/S, Copenhagen, Denmark. Measurements of tooth size and distance were obtained using O3d software (Widialabs, Brazil measurement tools. Data were analyzed by paired t-test and linear regression with 5% significance.Results and Conclusion: Most of the measurements from both groups were similar, except forthe lower intermolar distance. It was not possible to corroborate the presence of distortions due to the position of the impression tray during gypsum setting time.

  12. A Bayesian goodness of fit test and semiparametric generalization of logistic regression with measurement data.

    Science.gov (United States)

    Schörgendorfer, Angela; Branscum, Adam J; Hanson, Timothy E

    2013-06-01

    Logistic regression is a popular tool for risk analysis in medical and population health science. With continuous response data, it is common to create a dichotomous outcome for logistic regression analysis by specifying a threshold for positivity. Fitting a linear regression to the nondichotomized response variable assuming a logistic sampling model for the data has been empirically shown to yield more efficient estimates of odds ratios than ordinary logistic regression of the dichotomized endpoint. We illustrate that risk inference is not robust to departures from the parametric logistic distribution. Moreover, the model assumption of proportional odds is generally not satisfied when the condition of a logistic distribution for the data is violated, leading to biased inference from a parametric logistic analysis. We develop novel Bayesian semiparametric methodology for testing goodness of fit of parametric logistic regression with continuous measurement data. The testing procedures hold for any cutoff threshold and our approach simultaneously provides the ability to perform semiparametric risk estimation. Bayes factors are calculated using the Savage-Dickey ratio for testing the null hypothesis of logistic regression versus a semiparametric generalization. We propose a fully Bayesian and a computationally efficient empirical Bayesian approach to testing, and we present methods for semiparametric estimation of risks, relative risks, and odds ratios when parametric logistic regression fails. Theoretical results establish the consistency of the empirical Bayes test. Results from simulated data show that the proposed approach provides accurate inference irrespective of whether parametric assumptions hold or not. Evaluation of risk factors for obesity shows that different inferences are derived from an analysis of a real data set when deviations from a logistic distribution are permissible in a flexible semiparametric framework. © 2013, The International Biometric

  13. Diagnosis of cranial hemangioma: Comparison between logistic regression analysis and neuronal network

    International Nuclear Information System (INIS)

    Arana, E.; Marti-Bonmati, L.; Bautista, D.; Paredes, R.

    1998-01-01

    To study the utility of logistic regression and the neuronal network in the diagnosis of cranial hemangiomas. Fifteen patients presenting hemangiomas were selected form a total of 167 patients with cranial lesions. All were evaluated by plain radiography and computed tomography (CT). Nineteen variables in their medical records were reviewed. Logistic regression and neuronal network models were constructed and validated by the jackknife (leave-one-out) approach. The yields of the two models were compared by means of ROC curves, using the area under the curve as parameter. Seven men and 8 women presented hemangiomas. The mean age of these patients was 38.4 (15.4 years (mea ± standard deviation). Logistic regression identified as significant variables the shape, soft tissue mass and periosteal reaction. The neuronal network lent more importance to the existence of ossified matrix, ruptured cortical vein and the mixed calcified-blastic (trabeculated) pattern. The neuronal network showed a greater yield than logistic regression (Az, 0.9409) (0.004 versus 0.7211± 0.075; p<0.001). The neuronal network discloses hidden interactions among the variables, providing a higher yield in the characterization of cranial hemangiomas and constituting a medical diagnostic acid. (Author)29 refs

  14. Predictors of positive health in disability pensioners: a population-based questionnaire study using Positive Odds Ratio

    Directory of Open Access Journals (Sweden)

    Edén Lena

    2002-09-01

    Full Text Available Abstract Background Determinants of ill-health have been studied far more than determinants of good and improving health. Health promotion measures are important even among individuals with chronic diseases. The aim of this study was to find predictors of positive subjective health among disability pensioners (DPs with musculoskeletal disorders. Methods Two questionnaire surveys were performed among 352 DPs with musculoskeletal disorders. Two groups were defined: DPs with positive health and negative health, respectively. In consequence with the health perspective in this study the conception Positive Odds Ratio was defined and used in the logistic regression analyses instead of the commonly used odds ratio. Results Positive health was associated with age ≥ 55 years, not being an immigrant, not having fibromyalgia as the main diagnosis for granting an early retirement, no regular use of analgesics, a high ADL capacity, a positive subjective health preceding the study period, and good quality of life. Conclusion Positive odds ratio is a concept well adapted to theories of health promotion. It can be used in relation to positive outcomes instead of risks. Suggested health promotion and secondary prevention efforts among individuals with musculoskeletal disorders are 1 to avoid a disability pension for individuals

  15. Effects of Fatty Acids at Different Positions in the Triglycerides on Cholesterol Levels

    International Nuclear Information System (INIS)

    Teh, S.S.; Voon, P.T.; Ng, Y.T.; Ong, S.H.; Augustine, S.H.O.; Choo, Y.M.

    2016-01-01

    Previous studies established a series of regression equations for predicting the risk factor effects from serum cholesterol concentrations. However, the degree of saturation was solely based on total fatty acid composition in triglycerides. Our article is focused on the relationships between the published human nutrition studies and predicted values of serum cholesterol levels based on total fatty acid compositions and at sn-2 position in triglycerides. Twenty-two published human nutrition studies were chosen to assess the effects of palm olein, olive oil, cocoa butter, sunflower seed oil, corn oil, soyabean oil, grape seed oil, groundnut oil and rice bran oil diets on serum cholesterol levels. There were no statistically significant differences between the predicted values of serum cholesterol levels based on fatty acids at sn-2 position and the published human nutrition studies as proven by the statistical analyses with p values more than 0.05. In contrast, there were statistically significant differences between the predicted values of serum cholesterol levels based on total fatty acids and the published human nutritional studies with p values less than 0.05. Fatty acids at sn-2 position appear to influence the cholesterol levels rather than total fatty acids of the triglyceride. (author)

  16. Prognostic significance of interleukin-8 and CD163-positive cell-infiltration in tumor tissues in patients with oral squamous cell carcinoma.

    Directory of Open Access Journals (Sweden)

    Yohei Fujita

    Full Text Available PURPOSE: We investigated whether serum interleukin (IL-8 reflects the tumor microenvironment and has prognostic value in patients with oral squamous cell carcinoma (OSCC. EXPERIMENTAL DESIGN: Fifty OSCC patients who received radical resection of their tumor(s were enrolled. Preoperative sera were measured for IL-8 by ELISA. Expression of IL-8 and the infiltration of immune cells in tumor tissues were analyzed by an immunohistochemical staining of surgical specimens. RESULTS: We found that disease-free survival (DFS was significantly longer in the Stage I/II OSCC patients with low serum IL-8 levels compared to those with high levels (p = 0.001. The tumor expression of IL-8, i.e., IL-8(T and the density of CD163-positive cells in the tumor invasive front, i.e., CD163(IF were correlated with the serum IL-8 level (p = 0.033 and p = 0.038, respectively, and they were associated with poor clinical outcome (p = 0.007 and p = 0.002, respectively, in DFS in all patients. A multivariate analysis revealed that N status, IL-8(T and CD163(IF significantly affected the DFS of the patients. Further analysis suggested that combination of N status with serum IL-8, IL-8(T or CD163(IF may be a new criterion for discriminating between OSCC patients at high and low risk for tumor relapse. Interestingly, the in vitro experiments demonstrated that IL-8 enhanced generation of CD163-positive M2 macrophages from peripheral blood monocytes, and that the cells produced IL-10. CONCLUSIONS: These findings indicate that IL-8 may be involved in poor clinical outcomes via generation of CD163-positive M2 macrophages, and that these factors in addition to N status may have prognostic value in patients with resectable OSCSS.

  17. Model-based Quantile Regression for Discrete Data

    KAUST Repository

    Padellini, Tullia

    2018-04-10

    Quantile regression is a class of methods voted to the modelling of conditional quantiles. In a Bayesian framework quantile regression has typically been carried out exploiting the Asymmetric Laplace Distribution as a working likelihood. Despite the fact that this leads to a proper posterior for the regression coefficients, the resulting posterior variance is however affected by an unidentifiable parameter, hence any inferential procedure beside point estimation is unreliable. We propose a model-based approach for quantile regression that considers quantiles of the generating distribution directly, and thus allows for a proper uncertainty quantification. We then create a link between quantile regression and generalised linear models by mapping the quantiles to the parameter of the response variable, and we exploit it to fit the model with R-INLA. We extend it also in the case of discrete responses, where there is no 1-to-1 relationship between quantiles and distribution\\'s parameter, by introducing continuous generalisations of the most common discrete variables (Poisson, Binomial and Negative Binomial) to be exploited in the fitting.

  18. riskRegression

    DEFF Research Database (Denmark)

    Ozenne, Brice; Sørensen, Anne Lyngholm; Scheike, Thomas

    2017-01-01

    In the presence of competing risks a prediction of the time-dynamic absolute risk of an event can be based on cause-specific Cox regression models for the event and the competing risks (Benichou and Gail, 1990). We present computationally fast and memory optimized C++ functions with an R interface......-product we obtain fast access to the baseline hazards (compared to survival::basehaz()) and predictions of survival probabilities, their confidence intervals and confidence bands. Confidence intervals and confidence bands are based on point-wise asymptotic expansions of the corresponding statistical...

  19. Social networks of HIV-positive women and their association with social support and depression symptoms.

    Science.gov (United States)

    Cederbaum, Julie A; Rice, Eric; Craddock, Jaih; Pimentel, Veronica; Beaver, Patty

    2017-02-01

    Social support is important to the mental health and well-being of HIV-positive women. Limited information exists about the specific structure and composition of HIV-positive women's support networks or associations of these network properties with mental health outcomes. In this pilot study, the authors examine whether support network characteristics were associated with depressive symptoms. Survey and network data were collected from HIV-positive women (N = 46) via a web-based survey and an iPad application in August 2012. Data were analyzed using multivariate linear regression models in SAS. Depressive symptoms were positively associated with a greater number of doctors in a woman's network; having more HIV-positive network members was associated with less symptom reporting. Women who reported more individuals who could care for them had more family support. Those who reported feeling loved were less likely to report disclosure stigma. This work highlighted that detailed social network data can increase our understanding of social support so as to identify interventions to support the mental health of HIV-positive women. Most significant is the ongoing need for support from peers.

  20. YKL-40/c-Met expression in rectal cancer biopsies predicts tumor regression following neoadjuvant chemoradiotherapy: a multi-institutional study.

    Science.gov (United States)

    Senetta, Rebecca; Duregon, Eleonora; Sonetto, Cristina; Spadi, Rossella; Mistrangelo, Massimiliano; Racca, Patrizia; Chiusa, Luigi; Munoz, Fernando H; Ricardi, Umberto; Arezzo, Alberto; Cassenti, Adele; Castellano, Isabella; Papotti, Mauro; Morino, Mario; Risio, Mauro; Cassoni, Paola

    2015-01-01

    Neoadjuvant chemo-radiotherapy (CRT) followed by surgical resection is the standard treatment for locally advanced rectal cancer, although complete tumor pathological regression is achieved in only up to 30% of cases. A clinicopathological and molecular predictive stratification of patients with advanced rectal cancer is still lacking. Here, c-Met and YKL-40 have been studied as putative predictors of CRT response in rectal cancer, due to their reported involvement in chemoradioresistance in various solid tumors. A multicentric study was designed to assess the role of c-Met and YKL-40 expression in predicting chemoradioresistance and to correlate clinical and pathological features with CRT response. Immunohistochemistry and fluorescent in situ hybridization for c-Met were performed on 81 rectal cancer biopsies from patients with locally advanced rectal adenocarcinoma. All patients underwent standard (50.4 gy in 28 fractions + concurrent capecitabine 825 mg/m2) neoadjuvant CRT or the XELOXART protocol. CRT response was documented on surgical resection specimens and recorded as tumor regression grade (TRG) according to the Mandard criteria. A significant correlation between c-Met and YKL-40 expression was observed (R = 0.43). The expressions of c-Met and YKL-40 were both significantly associated with a lack of complete response (86% and 87% of c-Met and YKL-40 positive cases, prectal cancer. Targeted therapy protocols could take advantage of prior evaluations of c-MET and YKL-40 expression levels to increase therapeutic efficacy.

  1. The determination of contribution of emotional intelligence and parenting styles components to predicts positive psychological components

    Directory of Open Access Journals (Sweden)

    hosein Ebrahimi moghadam

    2015-05-01

    Full Text Available Background: Since the essential of positive psychological components, as compliment of deficiency oriented approaches, has begun in recent days,we decided to take into account this new branch of psychology which scientifically considers studying forces of human, as well as because of the importance of this branch of psychology, we also tried to search the contribution of emotional intelligence and parenting styles components to predict positive psychological components. Materials and Methods:In this cross sectional study 200 psychological students of Azad university (Rudehen branch selected using cluster sampling method. Then they were estimated by Bradbery and Grivers emotional intelligence questionnaire , Bamrind parenting styles and Rajayi et al positive psychological components questionnaire. Research data was analyzed using descriptive statistics (mean and standard deviation, inferential statistics (multiple regression and Pierson correlation coefficient and SPSS software. Results:Among the components of emotional intelligence, the component of emotional self consciousness (β=0.464 had the greatest predictable , and reaction leadership showed no predictability in this research between parenting styles , authority parenting styles had positive significance relationship with positive psychological components. And no significant relationship was found between despot parenting styles and positive psychological components. Conclusion: Regarding the results of this research and importance of positive psychological components, it is suggested to treat the emotional intelligence from childhood and to learn it to parents and remind them the parenting way to decrease the satisfaction of individuals which leads to promotion of society mental health.

  2. When to perform urine cultures in respiratory syncytial virus-positive febrile older infants?

    Science.gov (United States)

    Kaluarachchi, Dinushan; Kaldas, Virginia; Erickson, Evelyn; Nunez, Randolph; Mendez, Magda

    2014-09-01

    Respiratory syncytial virus (RSV) infections are associated with clinically significant rate of urinary tract infections (UTIs) in young infants. Previous research investigating RSV infections and UTIs has been performed mainly in infants younger than 2 to 3 months and has not focused on the risk of UTI in infants 3 to 12 months. This study aimed to assess the rate of UTIs in febrile RSV-positive older infants admitted as inpatients and identify predictors of UTI in febrile RSV-positive older infants. This is a retrospective comparative study of febrile RSV-positive infants 0 to 12 months of age admitted to the inpatient pediatric unit of Lincoln Medical and Mental Health Center, Bronx, from September through April 2006 to 2012. Infants 3 to 12 months were considered the cases, and infants 0 to 3 months were the comparative group. The rate of UTIs between the 2 groups was compared. Univariate tests and multiple logistic regression were used to identify demographic/clinical factors associated with UTI in febrile RSV-positive older infants. A total of 414 RSV-positive febrile infants were enrolled including 297 infants 3 to 12 months of age. The rate of UTI in older infants was 6.1% compared with 6.8% in infants younger than 3 months. Positive urinalysis finding was an independent predictor of UTI (P = 0.003) in older infants. All 11 boys with UTI were uncircumcised, and none of the 51 circumcised boys had UTI. Demographic (race, sex, and age) and clinical factors (temperature, white blood cell count, and absolute neutrophil count) were not associated with UTI. Febrile older infants who are RSV positive have a clinically significant rate of UTIs. It seems prudent to examine the urine of these older infants. Positive urinalysis finding was a predictive factor of UTI. Circumcised boys are at a decreased risk of UTI, compared with uncircumcised boys.

  3. Computing multiple-output regression quantile regions

    Czech Academy of Sciences Publication Activity Database

    Paindaveine, D.; Šiman, Miroslav

    2012-01-01

    Roč. 56, č. 4 (2012), s. 840-853 ISSN 0167-9473 R&D Projects: GA MŠk(CZ) 1M06047 Institutional research plan: CEZ:AV0Z10750506 Keywords : halfspace depth * multiple-output regression * parametric linear programming * quantile regression Subject RIV: BA - General Mathematics Impact factor: 1.304, year: 2012 http://library.utia.cas.cz/separaty/2012/SI/siman-0376413.pdf

  4. Preface to Berk's "Regression Analysis: A Constructive Critique"

    OpenAIRE

    de Leeuw, Jan

    2003-01-01

    It is pleasure to write a preface for the book ”Regression Analysis” of my fellow series editor Dick Berk. And it is a pleasure in particular because the book is about regression analysis, the most popular and the most fundamental technique in applied statistics. And because it is critical of the way regression analysis is used in the sciences, in particular in the social and behavioral sciences. Although the book can be read as an introduction to regression analysis, it can also be read as a...

  5. Five cases of caudal regression with an aberrant abdominal umbilical artery: Further support for a caudal regression-sirenomelia spectrum.

    Science.gov (United States)

    Duesterhoeft, Sara M; Ernst, Linda M; Siebert, Joseph R; Kapur, Raj P

    2007-12-15

    Sirenomelia and caudal regression have sparked centuries of interest and recent debate regarding their classification and pathogenetic relationship. Specific anomalies are common to both conditions, but aside from fusion of the lower extremities, an aberrant abdominal umbilical artery ("persistent vitelline artery") has been invoked as the chief anatomic finding that distinguishes sirenomelia from caudal regression. This observation is important from a pathogenetic viewpoint, in that diversion of blood away from the caudal portion of the embryo through the abdominal umbilical artery ("vascular steal") has been proposed as the primary mechanism leading to sirenomelia. In contrast, caudal regression is hypothesized to arise from primary deficiency of caudal mesoderm. We present five cases of caudal regression that exhibit an aberrant abdominal umbilical artery similar to that typically associated with sirenomelia. Review of the literature identified four similar cases. Collectively, the series lends support for a caudal regression-sirenomelia spectrum with a common pathogenetic basis and suggests that abnormal umbilical arterial anatomy may be the consequence, rather than the cause, of deficient caudal mesoderm. (c) 2007 Wiley-Liss, Inc.

  6. Model-based Quantile Regression for Discrete Data

    KAUST Repository

    Padellini, Tullia; Rue, Haavard

    2018-01-01

    Quantile regression is a class of methods voted to the modelling of conditional quantiles. In a Bayesian framework quantile regression has typically been carried out exploiting the Asymmetric Laplace Distribution as a working likelihood. Despite

  7. Linear Regression Analysis

    CERN Document Server

    Seber, George A F

    2012-01-01

    Concise, mathematically clear, and comprehensive treatment of the subject.* Expanded coverage of diagnostics and methods of model fitting.* Requires no specialized knowledge beyond a good grasp of matrix algebra and some acquaintance with straight-line regression and simple analysis of variance models.* More than 200 problems throughout the book plus outline solutions for the exercises.* This revision has been extensively class-tested.

  8. Regression analysis and transfer function in estimating the parameters of central pulse waves from brachial pulse wave.

    Science.gov (United States)

    Chai Rui; Li Si-Man; Xu Li-Sheng; Yao Yang; Hao Li-Ling

    2017-07-01

    This study mainly analyzed the parameters such as ascending branch slope (A_slope), dicrotic notch height (Hn), diastolic area (Ad) and systolic area (As) diastolic blood pressure (DBP), systolic blood pressure (SBP), pulse pressure (PP), subendocardial viability ratio (SEVR), waveform parameter (k), stroke volume (SV), cardiac output (CO) and peripheral resistance (RS) of central pulse wave invasively and non-invasively measured. These parameters extracted from the central pulse wave invasively measured were compared with the parameters measured from the brachial pulse waves by a regression model and a transfer function model. The accuracy of the parameters which were estimated by the regression model and the transfer function model was compared too. Our findings showed that in addition to the k value, the above parameters of the central pulse wave and the brachial pulse wave invasively measured had positive correlation. Both the regression model parameters including A_slope, DBP, SEVR and the transfer function model parameters had good consistency with the parameters invasively measured, and they had the same effect of consistency. The regression equations of the three parameters were expressed by Y'=a+bx. The SBP, PP, SV, CO of central pulse wave could be calculated through the regression model, but their accuracies were worse than that of transfer function model.

  9. Ridge regression for predicting elastic moduli and hardness of calcium aluminosilicate glasses

    Science.gov (United States)

    Deng, Yifan; Zeng, Huidan; Jiang, Yejia; Chen, Guorong; Chen, Jianding; Sun, Luyi

    2018-03-01

    It is of great significance to design glasses with satisfactory mechanical properties predictively through modeling. Among various modeling methods, data-driven modeling is such a reliable approach that can dramatically shorten research duration, cut research cost and accelerate the development of glass materials. In this work, the ridge regression (RR) analysis was used to construct regression models for predicting the compositional dependence of CaO-Al2O3-SiO2 glass elastic moduli (Shear, Bulk, and Young’s moduli) and hardness based on the ternary diagram of the compositions. The property prediction over a large glass composition space was accomplished with known experimental data of various compositions in the literature, and the simulated results are in good agreement with the measured ones. This regression model can serve as a facile and effective tool for studying the relationship between the compositions and the property, enabling high-efficient design of glasses to meet the requirements for specific elasticity and hardness.

  10. Research on the multiple linear regression in non-invasive blood glucose measurement.

    Science.gov (United States)

    Zhu, Jianming; Chen, Zhencheng

    2015-01-01

    A non-invasive blood glucose measurement sensor and the data process algorithm based on the metabolic energy conservation (MEC) method are presented in this paper. The physiological parameters of human fingertip can be measured by various sensing modalities, and blood glucose value can be evaluated with the physiological parameters by the multiple linear regression analysis. Five methods such as enter, remove, forward, backward and stepwise in multiple linear regression were compared, and the backward method had the best performance. The best correlation coefficient was 0.876 with the standard error of the estimate 0.534, and the significance was 0.012 (sig. regression equation was valid. The Clarke error grid analysis was performed to compare the MEC method with the hexokinase method, using 200 data points. The correlation coefficient R was 0.867 and all of the points were located in Zone A and Zone B, which shows the MEC method provides a feasible and valid way for non-invasive blood glucose measurement.

  11. SU-E-T-377: Inaccurate Positioning Might Introduce Significant MapCheck Calibration Error in Flatten Filter Free Beams

    International Nuclear Information System (INIS)

    Wang, S; Chao, C; Chang, J

    2014-01-01

    Purpose: This study investigates the calibration error of detector sensitivity for MapCheck due to inaccurate positioning of the device, which is not taken into account by the current commercial iterative calibration algorithm. We hypothesize the calibration is more vulnerable to the positioning error for the flatten filter free (FFF) beams than the conventional flatten filter flattened beams. Methods: MapCheck2 was calibrated with 10MV conventional and FFF beams, with careful alignment and with 1cm positioning error during calibration, respectively. Open fields of 37cmx37cm were delivered to gauge the impact of resultant calibration errors. The local calibration error was modeled as a detector independent multiplication factor, with which propagation error was estimated with positioning error from 1mm to 1cm. The calibrated sensitivities, without positioning error, were compared between the conventional and FFF beams to evaluate the dependence on the beam type. Results: The 1cm positioning error leads to 0.39% and 5.24% local calibration error in the conventional and FFF beams respectively. After propagating to the edges of MapCheck, the calibration errors become 6.5% and 57.7%, respectively. The propagation error increases almost linearly with respect to the positioning error. The difference of sensitivities between the conventional and FFF beams was small (0.11 ± 0.49%). Conclusion: The results demonstrate that the positioning error is not handled by the current commercial calibration algorithm of MapCheck. Particularly, the calibration errors for the FFF beams are ~9 times greater than those for the conventional beams with identical positioning error, and a small 1mm positioning error might lead to up to 8% calibration error. Since the sensitivities are only slightly dependent of the beam type and the conventional beam is less affected by the positioning error, it is advisable to cross-check the sensitivities between the conventional and FFF beams to detect

  12. Visual grading characteristics and ordinal regression analysis during optimisation of CT head examinations.

    Science.gov (United States)

    Zarb, Francis; McEntee, Mark F; Rainford, Louise

    2015-06-01

    To evaluate visual grading characteristics (VGC) and ordinal regression analysis during head CT optimisation as a potential alternative to visual grading assessment (VGA), traditionally employed to score anatomical visualisation. Patient images (n = 66) were obtained using current and optimised imaging protocols from two CT suites: a 16-slice scanner at the national Maltese centre for trauma and a 64-slice scanner in a private centre. Local resident radiologists (n = 6) performed VGA followed by VGC and ordinal regression analysis. VGC alone indicated that optimised protocols had similar image quality as current protocols. Ordinal logistic regression analysis provided an in-depth evaluation, criterion by criterion allowing the selective implementation of the protocols. The local radiology review panel supported the implementation of optimised protocols for brain CT examinations (including trauma) in one centre, achieving radiation dose reductions ranging from 24 % to 36 %. In the second centre a 29 % reduction in radiation dose was achieved for follow-up cases. The combined use of VGC and ordinal logistic regression analysis led to clinical decisions being taken on the implementation of the optimised protocols. This improved method of image quality analysis provided the evidence to support imaging protocol optimisation, resulting in significant radiation dose savings. • There is need for scientifically based image quality evaluation during CT optimisation. • VGC and ordinal regression analysis in combination led to better informed clinical decisions. • VGC and ordinal regression analysis led to dose reductions without compromising diagnostic efficacy.

  13. Computing group cardinality constraint solutions for logistic regression problems.

    Science.gov (United States)

    Zhang, Yong; Kwon, Dongjin; Pohl, Kilian M

    2017-01-01

    We derive an algorithm to directly solve logistic regression based on cardinality constraint, group sparsity and use it to classify intra-subject MRI sequences (e.g. cine MRIs) of healthy from diseased subjects. Group cardinality constraint models are often applied to medical images in order to avoid overfitting of the classifier to the training data. Solutions within these models are generally determined by relaxing the cardinality constraint to a weighted feature selection scheme. However, these solutions relate to the original sparse problem only under specific assumptions, which generally do not hold for medical image applications. In addition, inferring clinical meaning from features weighted by a classifier is an ongoing topic of discussion. Avoiding weighing features, we propose to directly solve the group cardinality constraint logistic regression problem by generalizing the Penalty Decomposition method. To do so, we assume that an intra-subject series of images represents repeated samples of the same disease patterns. We model this assumption by combining series of measurements created by a feature across time into a single group. Our algorithm then derives a solution within that model by decoupling the minimization of the logistic regression function from enforcing the group sparsity constraint. The minimum to the smooth and convex logistic regression problem is determined via gradient descent while we derive a closed form solution for finding a sparse approximation of that minimum. We apply our method to cine MRI of 38 healthy controls and 44 adult patients that received reconstructive surgery of Tetralogy of Fallot (TOF) during infancy. Our method correctly identifies regions impacted by TOF and generally obtains statistically significant higher classification accuracy than alternative solutions to this model, i.e., ones relaxing group cardinality constraints. Copyright © 2016 Elsevier B.V. All rights reserved.

  14. A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression.

    Science.gov (United States)

    Yu, Xu; Lin, Jun-Yu; Jiang, Feng; Du, Jun-Wei; Han, Ji-Zhong

    2018-01-01

    Cross-domain collaborative filtering (CDCF) solves the sparsity problem by transferring rating knowledge from auxiliary domains. Obviously, different auxiliary domains have different importance to the target domain. However, previous works cannot evaluate effectively the significance of different auxiliary domains. To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR). We first construct features in different domains and use these features to represent different auxiliary domains. Thus the weight computation across different domains can be converted as the weight computation across different features. Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem. Finally, we employ a Locally Weighted Linear Regression (LWLR) model to solve the regression problem. As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods. We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods.

  15. How significant is the ‘significant other’? Associations between significant others’ health behaviors and attitudes and young adults’ health outcomes

    Directory of Open Access Journals (Sweden)

    Berge Jerica M

    2012-04-01

    Full Text Available Abstract Background Having a significant other has been shown to be protective against physical and psychological health conditions for adults. Less is known about the period of emerging young adulthood and associations between significant others’ weight and weight-related health behaviors (e.g. healthy dietary intake, the frequency of physical activity, weight status. This study examined the association between significant others’ health attitudes and behaviors regarding eating and physical activity and young adults’ weight status, dietary intake, and physical activity. Methods This study uses data from Project EAT-III, a population-based cohort study with emerging young adults from diverse ethnic and socioeconomic backgrounds (n = 1212. Logistic regression models examining cross-sectional associations, adjusted for sociodemographics and health behaviors five years earlier, were used to estimate predicted probabilities and calculate prevalence differences. Results Young adult women whose significant others had health promoting attitudes/behaviors were significantly less likely to be overweight/obese and were more likely to eat ≥ 5 fruits/vegetables per day and engage in ≥ 3.5 hours/week of physical activity, compared to women whose significant others did not have health promoting behaviors/attitudes. Young adult men whose significant other had health promoting behaviors/attitudes were more likely to engage in ≥ 3.5 hours/week of physical activity compared to men whose significant others did not have health promoting behaviors/attitudes. Conclusions Findings suggest the protective nature of the significant other with regard to weight-related health behaviors of young adults, particularly for young adult women. Obesity prevention efforts should consider the importance of including the significant other in intervention efforts with young adult women and potentially men.

  16. Independent contrasts and PGLS regression estimators are equivalent.

    Science.gov (United States)

    Blomberg, Simon P; Lefevre, James G; Wells, Jessie A; Waterhouse, Mary

    2012-05-01

    We prove that the slope parameter of the ordinary least squares regression of phylogenetically independent contrasts (PICs) conducted through the origin is identical to the slope parameter of the method of generalized least squares (GLSs) regression under a Brownian motion model of evolution. This equivalence has several implications: 1. Understanding the structure of the linear model for GLS regression provides insight into when and why phylogeny is important in comparative studies. 2. The limitations of the PIC regression analysis are the same as the limitations of the GLS model. In particular, phylogenetic covariance applies only to the response variable in the regression and the explanatory variable should be regarded as fixed. Calculation of PICs for explanatory variables should be treated as a mathematical idiosyncrasy of the PIC regression algorithm. 3. Since the GLS estimator is the best linear unbiased estimator (BLUE), the slope parameter estimated using PICs is also BLUE. 4. If the slope is estimated using different branch lengths for the explanatory and response variables in the PIC algorithm, the estimator is no longer the BLUE, so this is not recommended. Finally, we discuss whether or not and how to accommodate phylogenetic covariance in regression analyses, particularly in relation to the problem of phylogenetic uncertainty. This discussion is from both frequentist and Bayesian perspectives.

  17. Assessing the human cardiovascular response to moderate exercise: feature extraction by support vector regression

    International Nuclear Information System (INIS)

    Wang, Lu; Su, Steven W; Celler, Branko G; Chan, Gregory S H; Cheng, Teddy M; Savkin, Andrey V

    2009-01-01

    This study aims to quantitatively describe the steady-state relationships among percentage changes in key central cardiovascular variables (i.e. stroke volume, heart rate (HR), total peripheral resistance and cardiac output), measured using non-invasive means, in response to moderate exercise, and the oxygen uptake rate, using a new nonlinear regression approach—support vector regression. Ten untrained normal males exercised in an upright position on an electronically braked cycle ergometer with constant workloads ranging from 25 W to 125 W. Throughout the experiment, .VO 2 was determined breath by breath and the HR was monitored beat by beat. During the last minute of each exercise session, the cardiac output was measured beat by beat using a novel non-invasive ultrasound-based device and blood pressure was measured using a tonometric measurement device. Based on the analysis of experimental data, nonlinear steady-state relationships between key central cardiovascular variables and .VO 2 were qualitatively observed except for the HR which increased linearly as a function of increasing .VO 2 . Quantitative descriptions of these complex nonlinear behaviour were provided by nonparametric models which were obtained by using support vector regression

  18. Methods for identifying SNP interactions: a review on variations of Logic Regression, Random Forest and Bayesian logistic regression.

    Science.gov (United States)

    Chen, Carla Chia-Ming; Schwender, Holger; Keith, Jonathan; Nunkesser, Robin; Mengersen, Kerrie; Macrossan, Paula

    2011-01-01

    Due to advancements in computational ability, enhanced technology and a reduction in the price of genotyping, more data are being generated for understanding genetic associations with diseases and disorders. However, with the availability of large data sets comes the inherent challenges of new methods of statistical analysis and modeling. Considering a complex phenotype may be the effect of a combination of multiple loci, various statistical methods have been developed for identifying genetic epistasis effects. Among these methods, logic regression (LR) is an intriguing approach incorporating tree-like structures. Various methods have built on the original LR to improve different aspects of the model. In this study, we review four variations of LR, namely Logic Feature Selection, Monte Carlo Logic Regression, Genetic Programming for Association Studies, and Modified Logic Regression-Gene Expression Programming, and investigate the performance of each method using simulated and real genotype data. We contrast these with another tree-like approach, namely Random Forests, and a Bayesian logistic regression with stochastic search variable selection.

  19. Brightness-normalized Partial Least Squares Regression for hyperspectral data

    International Nuclear Information System (INIS)

    Feilhauer, Hannes; Asner, Gregory P.; Martin, Roberta E.; Schmidtlein, Sebastian

    2010-01-01

    Developed in the field of chemometrics, Partial Least Squares Regression (PLSR) has become an established technique in vegetation remote sensing. PLSR was primarily designed for laboratory analysis of prepared material samples. Under field conditions in vegetation remote sensing, the performance of the technique may be negatively affected by differences in brightness due to amount and orientation of plant tissues in canopies or the observing conditions. To minimize these effects, we introduced brightness normalization to the PLSR approach and tested whether this modification improves the performance under changing canopy and observing conditions. This test was carried out using high-fidelity spectral data (400-2510 nm) to model observed leaf chemistry. The spectral data was combined with a canopy radiative transfer model to simulate effects of varying canopy structure and viewing geometry. Brightness normalization enhanced the performance of PLSR by dampening the effects of canopy shade, thus providing a significant improvement in predictions of leaf chemistry (up to 3.6% additional explained variance in validation) compared to conventional PLSR. Little improvement was made on effects due to variable leaf area index, while minor improvement (mostly not significant) was observed for effects of variable viewing geometry. In general, brightness normalization increased the stability of model fits and regression coefficients for all canopy scenarios. Brightness-normalized PLSR is thus a promising approach for application on airborne and space-based imaging spectrometer data.

  20. Demonstration of a Fiber Optic Regression Probe

    Science.gov (United States)

    Korman, Valentin; Polzin, Kurt A.

    2010-01-01

    The capability to provide localized, real-time monitoring of material regression rates in various applications has the potential to provide a new stream of data for development testing of various components and systems, as well as serving as a monitoring tool in flight applications. These applications include, but are not limited to, the regression of a combusting solid fuel surface, the ablation of the throat in a chemical rocket or the heat shield of an aeroshell, and the monitoring of erosion in long-life plasma thrusters. The rate of regression in the first application is very fast, while the second and third are increasingly slower. A recent fundamental sensor development effort has led to a novel regression, erosion, and ablation sensor technology (REAST). The REAST sensor allows for measurement of real-time surface erosion rates at a discrete surface location. The sensor is optical, using two different, co-located fiber-optics to perform the regression measurement. The disparate optical transmission properties of the two fiber-optics makes it possible to measure the regression rate by monitoring the relative light attenuation through the fibers. As the fibers regress along with the parent material in which they are embedded, the relative light intensities through the two fibers changes, providing a measure of the regression rate. The optical nature of the system makes it relatively easy to use in a variety of harsh, high temperature environments, and it is also unaffected by the presence of electric and magnetic fields. In addition, the sensor could be used to perform optical spectroscopy on the light emitted by a process and collected by fibers, giving localized measurements of various properties. The capability to perform an in-situ measurement of material regression rates is useful in addressing a variety of physical issues in various applications. An in-situ measurement allows for real-time data regarding the erosion rates, providing a quick method for

  1. Caudal regression syndrome : a case report

    International Nuclear Information System (INIS)

    Lee, Eun Joo; Kim, Hi Hye; Kim, Hyung Sik; Park, So Young; Han, Hye Young; Lee, Kwang Hun

    1998-01-01

    Caudal regression syndrome is a rare congenital anomaly, which results from a developmental failure of the caudal mesoderm during the fetal period. We present a case of caudal regression syndrome composed of a spectrum of anomalies including sirenomelia, dysplasia of the lower lumbar vertebrae, sacrum, coccyx and pelvic bones,genitourinary and anorectal anomalies, and dysplasia of the lung, as seen during infantography and MR imaging

  2. Caudal regression syndrome : a case report

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Eun Joo; Kim, Hi Hye; Kim, Hyung Sik; Park, So Young; Han, Hye Young; Lee, Kwang Hun [Chungang Gil Hospital, Incheon (Korea, Republic of)

    1998-07-01

    Caudal regression syndrome is a rare congenital anomaly, which results from a developmental failure of the caudal mesoderm during the fetal period. We present a case of caudal regression syndrome composed of a spectrum of anomalies including sirenomelia, dysplasia of the lower lumbar vertebrae, sacrum, coccyx and pelvic bones,genitourinary and anorectal anomalies, and dysplasia of the lung, as seen during infantography and MR imaging.

  3. Modelling subject-specific childhood growth using linear mixed-effect models with cubic regression splines.

    Science.gov (United States)

    Grajeda, Laura M; Ivanescu, Andrada; Saito, Mayuko; Crainiceanu, Ciprian; Jaganath, Devan; Gilman, Robert H; Crabtree, Jean E; Kelleher, Dermott; Cabrera, Lilia; Cama, Vitaliano; Checkley, William

    2016-01-01

    Childhood growth is a cornerstone of pediatric research. Statistical models need to consider individual trajectories to adequately describe growth outcomes. Specifically, well-defined longitudinal models are essential to characterize both population and subject-specific growth. Linear mixed-effect models with cubic regression splines can account for the nonlinearity of growth curves and provide reasonable estimators of population and subject-specific growth, velocity and acceleration. We provide a stepwise approach that builds from simple to complex models, and account for the intrinsic complexity of the data. We start with standard cubic splines regression models and build up to a model that includes subject-specific random intercepts and slopes and residual autocorrelation. We then compared cubic regression splines vis-à-vis linear piecewise splines, and with varying number of knots and positions. Statistical code is provided to ensure reproducibility and improve dissemination of methods. Models are applied to longitudinal height measurements in a cohort of 215 Peruvian children followed from birth until their fourth year of life. Unexplained variability, as measured by the variance of the regression model, was reduced from 7.34 when using ordinary least squares to 0.81 (p linear mixed-effect models with random slopes and a first order continuous autoregressive error term. There was substantial heterogeneity in both the intercept (p modeled with a first order continuous autoregressive error term as evidenced by the variogram of the residuals and by a lack of association among residuals. The final model provides a parametric linear regression equation for both estimation and prediction of population- and individual-level growth in height. We show that cubic regression splines are superior to linear regression splines for the case of a small number of knots in both estimation and prediction with the full linear mixed effect model (AIC 19,352 vs. 19

  4. Expression and prognostic significance of lysozyme in male breast cancer

    International Nuclear Information System (INIS)

    Serra, Carlos; Baltasar, Aniceto; Medrano, Justo; Vizoso, Francisco; Alonso, Lorena; Rodríguez, Juan C; González, Luis O; Fernández, María; Lamelas, María L; Sánchez, Luis M; García-Muñiz, José L

    2002-01-01

    Lysozyme, one of the major protein components of human milk that is also synthesized by a significant percentage of breast carcinomas, is associated with lesions that have a favorable outcome in female breast cancer. Here we evaluate the expression and prognostic value of lysozyme in male breast cancer (MBC). Lysozyme expression was examined by immunohistochemical methods in a series of 60 MBC tissue sections and in 15 patients with gynecomastia. Staining was quantified using the HSCORE (histological score) system, which considers both the intensity and the percentage of cells staining at each intensity. Prognostic value of lysozyme was retrospectively evaluated by multivariate analysis taking into account conventional prognostic factors. Lysozyme immunostaining was negative in all cases of gynecomastia. A total of 27 of 60 MBC sections (45%) stained positively for this protein, but there were clear differences among them with regard to the intensity and percentage of stained cells. Statistical analysis showed that lysozyme HSCORE values in relation to age, tumor size, nodal status, histological grade, estrogen receptor status, metastasis and histological type did not increase the statistical significance. Univariate analysis confirmed that both nodal involvement and lysozyme values were significant predictors of short-term relapse-free survival. Multivariate analysis, according to Cox's regression model, also showed that nodal status and lysozyme levels were significant independent indicators of short-term relapse-free survival. Tumor expression of lysozyme is associated with lesions that have an unfavorable outcome in male breast cancer. This milk protein may be a new prognostic factor in patients with breast cancer

  5. Correlation and simple linear regression.

    Science.gov (United States)

    Zou, Kelly H; Tuncali, Kemal; Silverman, Stuart G

    2003-06-01

    In this tutorial article, the concepts of correlation and regression are reviewed and demonstrated. The authors review and compare two correlation coefficients, the Pearson correlation coefficient and the Spearman rho, for measuring linear and nonlinear relationships between two continuous variables. In the case of measuring the linear relationship between a predictor and an outcome variable, simple linear regression analysis is conducted. These statistical concepts are illustrated by using a data set from published literature to assess a computed tomography-guided interventional technique. These statistical methods are important for exploring the relationships between variables and can be applied to many radiologic studies.

  6. Funding source, conflict of interest and positive conclusions in neuro-oncology clinical trials.

    Science.gov (United States)

    Moraes, Fabio Y; Mendez, Lucas C; Taunk, Neil K; Raman, Srinivas; Suh, John H; Souhami, Luis; Slotman, Ben; Weltman, Eduardo; Spratt, Daniel E; Berlin, Alejandro; Marta, Gustavo N

    2018-02-01

    We aimed to test any association between authors' conclusions and self-reported COI or funding sources in central nervous system (CNS) studies. A review was performed for CNS malignancy clinical trials published in the last 5 years. Two investigators independently classified study conclusions according to authors' endorsement of the experimental therapy. Statistical models were used to test for associations between positive conclusions and trials characteristics. From February 2010 to February 2015, 1256 articles were retrieved; 319 were considered eligible trials. Positive conclusions were reported in 56.8% of trials with industry-only, 55.6% with academia-only, 44.1% with academia and industry, 77.8% with none, and 76.4% with not described funding source (p = 0.011). Positive conclusions were reported in 60.4% of trials with unrelated COI, 60% with related COI, and 60% with no COI reported (p = 0.997). Factors that were significantly associated with the presence of positive conclusion included trials design (phase 1) [OR 11.64 (95 CI 4.66-29.09), p source [OR 2.45 (95 CI 1.22-5.22), p = 0.011]. In a multivariable regression model, all these factors remained significantly associated with trial's positive conclusion. Funding source and self-reported COI did not appear to influence the CNS trials conclusion. Funding source information and COI disclosure were under-reported in 14.1 and 17.2% of the CNS trials. Continued efforts are needed to increase rates of both COI and funding source reporting.

  7. bayesQR: A Bayesian Approach to Quantile Regression

    Directory of Open Access Journals (Sweden)

    Dries F. Benoit

    2017-01-01

    Full Text Available After its introduction by Koenker and Basset (1978, quantile regression has become an important and popular tool to investigate the conditional response distribution in regression. The R package bayesQR contains a number of routines to estimate quantile regression parameters using a Bayesian approach based on the asymmetric Laplace distribution. The package contains functions for the typical quantile regression with continuous dependent variable, but also supports quantile regression for binary dependent variables. For both types of dependent variables, an approach to variable selection using the adaptive lasso approach is provided. For the binary quantile regression model, the package also contains a routine that calculates the fitted probabilities for each vector of predictors. In addition, functions for summarizing the results, creating traceplots, posterior histograms and drawing quantile plots are included. This paper starts with a brief overview of the theoretical background of the models used in the bayesQR package. The main part of this paper discusses the computational problems that arise in the implementation of the procedure and illustrates the usefulness of the package through selected examples.

  8. Relationship between the curve of Spee and craniofacial variables: A regression analysis.

    Science.gov (United States)

    Halimi, Abdelali; Benyahia, Hicham; Azeroual, Mohamed-Faouzi; Bahije, Loubna; Zaoui, Fatima

    2018-06-01

    The aim of this regression analysis was to identify the determining factors, which impact the curve of Spee during its genesis, its therapeutic reconstruction, and its stability, within a continuously evolving craniofacial morphology throughout life. We selected a total of 107 patients, according to the inclusion criteria. A morphological and functional clinical examination was performed for each patient: plaster models, tracing of the curve of Spee, crowding, Angle's classification, overjet and overbite were thus recorded. Then, we made a cephalometric analysis based on the standardized lateral cephalograms. In the sagittal dimension, we measured the values of angles ANB, SNA, SNB, SND, I/i; and the following distances: AoBo, I/NA, i/NB, SE and SL. In the vertical dimension, we measured the values of angles FMA, GoGn/SN, the occlusal plane, and the following distances: SAr, ArD, Ar/Con, Con/Gn, GoPo, HFP, HFA and IF. The statistical analysis was performed using the SPSS software with a significance level of 0.05. Our sample including 107 subjects was composed of 77 female patients (71.3%) and 30 male patients (27.8%) 7 hypodivergent patients (6.5%), 56 hyperdivergent patients (52.3%) and 44 normodivergent patients (41.1%). Patients' mean age was 19.35±5.95 years. The hypodivergent patients presented more pronounced curves of Spee compared to the normodivergent and the hyperdivergent populations; patients in skeletal Class I presented less pronounced curves of Spee compared to patients in skeletal Class II and Class III. These differences were non significant (P>0.05). The curve of Spee was positively and moderately correlated with Angle's classification, overjet, overbite, sellion-articulare distance, and breathing type (P0.05). Seventy five percent (75%) of the hyperdivergent patients with an oral breathing presented an overbite of 3mm, which is quite excessive given the characteristics often admitted for this typology; this parameter could explain the overbite

  9. Comparison of Regression Analysis and Transfer Function in Estimating the Parameters of Central Pulse Waves from Brachial Pulse Wave.

    Science.gov (United States)

    Chai, Rui; Xu, Li-Sheng; Yao, Yang; Hao, Li-Ling; Qi, Lin

    2017-01-01

    This study analyzed ascending branch slope (A_slope), dicrotic notch height (Hn), diastolic area (Ad) and systolic area (As) diastolic blood pressure (DBP), systolic blood pressure (SBP), pulse pressure (PP), subendocardial viability ratio (SEVR), waveform parameter (k), stroke volume (SV), cardiac output (CO), and peripheral resistance (RS) of central pulse wave invasively and non-invasively measured. Invasively measured parameters were compared with parameters measured from brachial pulse waves by regression model and transfer function model. Accuracy of parameters estimated by regression and transfer function model, was compared too. Findings showed that k value, central pulse wave and brachial pulse wave parameters invasively measured, correlated positively. Regression model parameters including A_slope, DBP, SEVR, and transfer function model parameters had good consistency with parameters invasively measured. They had same effect of consistency. SBP, PP, SV, and CO could be calculated through the regression model, but their accuracies were worse than that of transfer function model.

  10. Positive and negative associations of individual social capital factors with health among community-dwelling older people.

    Science.gov (United States)

    Kabayama, Mai; Watanabe, Chie; Ryuno, Hirochika; Kamide, Kei

    2017-12-01

    Previous literature has found positive correlations between social capital and health in older adults, fewer studies have investigated the subdimension's effects of social capital on health. We aimed to determine the individual social capital subfactors in community-dwelling older adults in Japan, and to analyze the associations of these factors with physical and mental health. We sent a self-administered questionnaire assessing their perception of social group activity as the individual social capital, and mental and physical health (measured by the Medical Outcomes Study Short Form-36) to 4320 randomly selected older people. There were 1836 valid responses. We clarified that people who participated in any social activity group were in significantly better physical and mental health compared with the people who did not. By the factor analysis of the perception for the social group activity, we identified three components of the individual social capital aspect that we termed harmonious, hierarchic and diversity. Using multiple linear regression, we found the hierarchic aspect was significantly negatively associated with mental health, whereas the harmonious aspect was significantly positively associated with mental and physical health, and diversity was significantly positively associated with mental health. As the previous research literature on social capital has mainly emphasized its positive health consequences, the present findings provide a novel demonstration that some aspects of individual social capital can have negative associations with health outcomes in community-dwelling older people. For the practical application of promoting a healthier society, it is important to consider both the positive and negative sides of social capital. Geriatr Gerontol Int 2017; 17: 2427-2434. © 2017 Japan Geriatrics Society.

  11. Quasi-Poisson versus negative binomial regression models in identifying factors affecting initial CD4 cell count change due to antiretroviral therapy administered to HIV-positive adults in North-West Ethiopia (Amhara region).

    Science.gov (United States)

    Seyoum, Awoke; Ndlovu, Principal; Zewotir, Temesgen

    2016-01-01

    CD4 cells are a type of white blood cells that plays a significant role in protecting humans from infectious diseases. Lack of information on associated factors on CD4 cell count reduction is an obstacle for improvement of cells in HIV positive adults. Therefore, the main objective of this study was to investigate baseline factors that could affect initial CD4 cell count change after highly active antiretroviral therapy had been given to adult patients in North West Ethiopia. A retrospective cross-sectional study was conducted among 792 HIV positive adult patients who already started antiretroviral therapy for 1 month of therapy. A Chi square test of association was used to assess of predictor covariates on the variable of interest. Data was secondary source and modeled using generalized linear models, especially Quasi-Poisson regression. The patients' CD4 cell count changed within a month ranged from 0 to 109 cells/mm 3 with a mean of 15.9 cells/mm 3 and standard deviation 18.44 cells/mm 3 . The first month CD4 cell count change was significantly affected by poor adherence to highly active antiretroviral therapy (aRR = 0.506, P value = 2e -16 ), fair adherence (aRR = 0.592, P value = 0.0120), initial CD4 cell count (aRR = 1.0212, P value = 1.54e -15 ), low household income (aRR = 0.63, P value = 0.671e -14 ), middle income (aRR = 0.74, P value = 0.629e -12 ), patients without cell phone (aRR = 0.67, P value = 0.615e -16 ), WHO stage 2 (aRR = 0.91, P value = 0.0078), WHO stage 3 (aRR = 0.91, P value = 0.0058), WHO stage 4 (0876, P value = 0.0214), age (aRR = 0.987, P value = 0.000) and weight (aRR = 1.0216, P value = 3.98e -14 ). Adherence to antiretroviral therapy, initial CD4 cell count, household income, WHO stages, age, weight and owner of cell phone played a major role for the variation of CD4 cell count in our data. Hence, we recommend a close follow-up of patients to adhere the prescribed medication for

  12. The Regression Analysis of Individual Financial Performance: Evidence from Croatia

    OpenAIRE

    Bahovec, Vlasta; Barbić, Dajana; Palić, Irena

    2017-01-01

    Background: A large body of empirical literature indicates that gender and financial literacy are significant determinants of individual financial performance. Objectives: The purpose of this paper is to recognize the impact of the variable financial literacy and the variable gender on the variation of the financial performance using the regression analysis. Methods/Approach: The survey was conducted using the systematically chosen random sample of Croatian financial consumers. The cross sect...

  13. Multivariate Linear Regression and CART Regression Analysis of TBM Performance at Abu Hamour Phase-I Tunnel

    Science.gov (United States)

    Jakubowski, J.; Stypulkowski, J. B.; Bernardeau, F. G.

    2017-12-01

    The first phase of the Abu Hamour drainage and storm tunnel was completed in early 2017. The 9.5 km long, 3.7 m diameter tunnel was excavated with two Earth Pressure Balance (EPB) Tunnel Boring Machines from Herrenknecht. TBM operation processes were monitored and recorded by Data Acquisition and Evaluation System. The authors coupled collected TBM drive data with available information on rock mass properties, cleansed, completed with secondary variables and aggregated by weeks and shifts. Correlations and descriptive statistics charts were examined. Multivariate Linear Regression and CART regression tree models linking TBM penetration rate (PR), penetration per revolution (PPR) and field penetration index (FPI) with TBM operational and geotechnical characteristics were performed for the conditions of the weak/soft rock of Doha. Both regression methods are interpretable and the data were screened with different computational approaches allowing enriched insight. The primary goal of the analysis was to investigate empirical relations between multiple explanatory and responding variables, to search for best subsets of explanatory variables and to evaluate the strength of linear and non-linear relations. For each of the penetration indices, a predictive model coupling both regression methods was built and validated. The resultant models appeared to be stronger than constituent ones and indicated an opportunity for more accurate and robust TBM performance predictions.

  14. Incremental validity of positive orientation: predictive efficiency beyond the five-factor model

    Directory of Open Access Journals (Sweden)

    Łukasz Roland Miciuk

    2016-05-01

    Full Text Available Background The relation of positive orientation (a basic predisposition to think positively of oneself, one’s life and one’s future and personality traits is still disputable. The purpose of the described research was to verify the hypothesis that positive orientation has predictive efficiency beyond the five-factor model. Participants and procedure One hundred and thirty participants (at the mean age M = 24.84 completed the following questionnaires: the Self-Esteem Scale (SES, the Satisfaction with Life Scale (SWLS, the Life Orientation Test-Revised (LOT-R, the Positivity Scale (P-SCALE, the NEO Five Factor Inventory (NEO-FFI, the Self-Concept Clarity Scale (SCC, the Generalized Self-Efficacy Scale (GSES and the Life Engagement Test (LET. Results The introduction of positive orientation as an additional predictor in the second step of regression analyses led to better prediction of the following variables: purpose in life, self-concept clarity and generalized self-efficacy. This effect was the strongest for predicting purpose in life (i.e. 14% increment of the explained variance. Conclusions The results confirmed our hypothesis that positive orientation can be characterized by incremental validity – its inclusion in the regression model (in addition to the five main factors of personality increases the amount of explained variance. These findings may provide further evidence for the legitimacy of measuring positive orientation and personality traits separately.

  15. Background stratified Poisson regression analysis of cohort data.

    Science.gov (United States)

    Richardson, David B; Langholz, Bryan

    2012-03-01

    Background stratified Poisson regression is an approach that has been used in the analysis of data derived from a variety of epidemiologically important studies of radiation-exposed populations, including uranium miners, nuclear industry workers, and atomic bomb survivors. We describe a novel approach to fit Poisson regression models that adjust for a set of covariates through background stratification while directly estimating the radiation-disease association of primary interest. The approach makes use of an expression for the Poisson likelihood that treats the coefficients for stratum-specific indicator variables as 'nuisance' variables and avoids the need to explicitly estimate the coefficients for these stratum-specific parameters. Log-linear models, as well as other general relative rate models, are accommodated. This approach is illustrated using data from the Life Span Study of Japanese atomic bomb survivors and data from a study of underground uranium miners. The point estimate and confidence interval obtained from this 'conditional' regression approach are identical to the values obtained using unconditional Poisson regression with model terms for each background stratum. Moreover, it is shown that the proposed approach allows estimation of background stratified Poisson regression models of non-standard form, such as models that parameterize latency effects, as well as regression models in which the number of strata is large, thereby overcoming the limitations of previously available statistical software for fitting background stratified Poisson regression models.

  16. Variable importance in latent variable regression models

    NARCIS (Netherlands)

    Kvalheim, O.M.; Arneberg, R.; Bleie, O.; Rajalahti, T.; Smilde, A.K.; Westerhuis, J.A.

    2014-01-01

    The quality and practical usefulness of a regression model are a function of both interpretability and prediction performance. This work presents some new graphical tools for improved interpretation of latent variable regression models that can also assist in improved algorithms for variable

  17. Prediction of Mind-Wandering with Electroencephalogram and Non-linear Regression Modeling.

    Science.gov (United States)

    Kawashima, Issaku; Kumano, Hiroaki

    2017-01-01

    Mind-wandering (MW), task-unrelated thought, has been examined by researchers in an increasing number of articles using models to predict whether subjects are in MW, using numerous physiological variables. However, these models are not applicable in general situations. Moreover, they output only binary classification. The current study suggests that the combination of electroencephalogram (EEG) variables and non-linear regression modeling can be a good indicator of MW intensity. We recorded EEGs of 50 subjects during the performance of a Sustained Attention to Response Task, including a thought sampling probe that inquired the focus of attention. We calculated the power and coherence value and prepared 35 patterns of variable combinations and applied Support Vector machine Regression (SVR) to them. Finally, we chose four SVR models: two of them non-linear models and the others linear models; two of the four models are composed of a limited number of electrodes to satisfy model usefulness. Examination using the held-out data indicated that all models had robust predictive precision and provided significantly better estimations than a linear regression model using single electrode EEG variables. Furthermore, in limited electrode condition, non-linear SVR model showed significantly better precision than linear SVR model. The method proposed in this study helps investigations into MW in various little-examined situations. Further, by measuring MW with a high temporal resolution EEG, unclear aspects of MW, such as time series variation, are expected to be revealed. Furthermore, our suggestion that a few electrodes can also predict MW contributes to the development of neuro-feedback studies.

  18. Prediction of Mind-Wandering with Electroencephalogram and Non-linear Regression Modeling

    Directory of Open Access Journals (Sweden)

    Issaku Kawashima

    2017-07-01

    Full Text Available Mind-wandering (MW, task-unrelated thought, has been examined by researchers in an increasing number of articles using models to predict whether subjects are in MW, using numerous physiological variables. However, these models are not applicable in general situations. Moreover, they output only binary classification. The current study suggests that the combination of electroencephalogram (EEG variables and non-linear regression modeling can be a good indicator of MW intensity. We recorded EEGs of 50 subjects during the performance of a Sustained Attention to Response Task, including a thought sampling probe that inquired the focus of attention. We calculated the power and coherence value and prepared 35 patterns of variable combinations and applied Support Vector machine Regression (SVR to them. Finally, we chose four SVR models: two of them non-linear models and the others linear models; two of the four models are composed of a limited number of electrodes to satisfy model usefulness. Examination using the held-out data indicated that all models had robust predictive precision and provided significantly better estimations than a linear regression model using single electrode EEG variables. Furthermore, in limited electrode condition, non-linear SVR model showed significantly better precision than linear SVR model. The method proposed in this study helps investigations into MW in various little-examined situations. Further, by measuring MW with a high temporal resolution EEG, unclear aspects of MW, such as time series variation, are expected to be revealed. Furthermore, our suggestion that a few electrodes can also predict MW contributes to the development of neuro-feedback studies.

  19. The Spatial Association Between Federally Qualified Health Centers and County-Level Reported Sexually Transmitted Infections: A Spatial Regression Approach.

    Science.gov (United States)

    Owusu-Edusei, Kwame; Gift, Thomas L; Leichliter, Jami S; Romaguera, Raul A

    2018-02-01

    The number of categorical sexually transmitted disease (STD) clinics is declining in the United States. Federally qualified health centers (FQHCs) have the potential to supplement the needed sexually transmitted infection (STI) services. In this study, we describe the spatial distribution of FQHC sites and determine if reported county-level nonviral STI morbidity were associated with having FQHC(s) using spatial regression techniques. We extracted map data from the Health Resources and Services Administration data warehouse on FQHCs (ie, geocoded health care service delivery [HCSD] sites) and extracted county-level data on the reported rates of chlamydia, gonorrhea and, primary and secondary (P&S) syphilis (2008-2012) from surveillance data. A 3-equation seemingly unrelated regression estimation procedure (with a spatial regression specification that controlled for county-level multiyear (2008-2012) demographic and socioeconomic factors) was used to determine the association between reported county-level STI morbidity and HCSD sites. Counties with HCSD sites had higher STI, poverty, unemployment, and violent crime rates than counties with no HCSD sites (P < 0.05). The number of HCSD sites was associated (P < 0.01) with increases in the temporally smoothed rates of chlamydia, gonorrhea, and P&S syphilis, but there was no significant association between the number of HCSD per 100,000 population and reported STI rates. There is a positive association between STI morbidity and the number of HCSD sites; however, this association does not exist when adjusting by population size. Further work may determine the extent to which HCSD sites can meet unmet needs for safety net STI services.

  20. Multiple regression and beyond an introduction to multiple regression and structural equation modeling

    CERN Document Server

    Keith, Timothy Z

    2014-01-01

    Multiple Regression and Beyond offers a conceptually oriented introduction to multiple regression (MR) analysis and structural equation modeling (SEM), along with analyses that flow naturally from those methods. By focusing on the concepts and purposes of MR and related methods, rather than the derivation and calculation of formulae, this book introduces material to students more clearly, and in a less threatening way. In addition to illuminating content necessary for coursework, the accessibility of this approach means students are more likely to be able to conduct research using MR or SEM--and more likely to use the methods wisely. Covers both MR and SEM, while explaining their relevance to one another Also includes path analysis, confirmatory factor analysis, and latent growth modeling Figures and tables throughout provide examples and illustrate key concepts and techniques For additional resources, please visit: http://tzkeith.com/.

  1. Optimism predicts positive health in repatriated prisoners of war.

    Science.gov (United States)

    Segovia, Francine; Moore, Jeffrey L; Linnville, Steven E; Hoyt, Robert E

    2015-05-01

    "Positive health," defined as a state beyond the mere absence of disease, was used as a model to examine factors for enhancing health despite extreme trauma. The study examined the United States' longest detained American prisoners of war, those held in Vietnam in the 1960s through early 1970s. Positive health was measured using a physical and a psychological composite score for each individual, based on 9 physical and 9 psychological variables. Physical and psychological health was correlated with optimism obtained postrepatriation (circa 1973). Linear regressions were employed to determine which variables contributed most to health ratings. Optimism was the strongest predictor of physical health (β = -.33, t = -2.73, p = .008), followed by fewer sleep complaints (β = -.29, t = -2.52, p = .01). This model accounted for 25% of the variance. Optimism was also the strongest predictor of psychological health (β = -.41, t = -2.87, p = .006), followed by Minnesota Multiphasic Personality Inventory-Psychopathic Deviate (MMPI-PD; McKinley & Hathaway, 1944) scores (β = -.23, t = -1.88, p = .07). This model strongly suggests that optimism is a significant predictor of positive physical and psychological health, and optimism also provides long-term protective benefits. These findings and the utility of this model suggest a promising area for future research and intervention. (c) 2015 APA, all rights reserved).

  2. Predicting company growth using logistic regression and neural networks

    Directory of Open Access Journals (Sweden)

    Marijana Zekić-Sušac

    2016-12-01

    Full Text Available The paper aims to establish an efficient model for predicting company growth by leveraging the strengths of logistic regression and neural networks. A real dataset of Croatian companies was used which described the relevant industry sector, financial ratios, income, and assets in the input space, with a dependent binomial variable indicating whether a company had high-growth if it had annualized growth in assets by more than 20% a year over a three-year period. Due to a large number of input variables, factor analysis was performed in the pre -processing stage in order to extract the most important input components. Building an efficient model with a high classification rate and explanatory ability required application of two data mining methods: logistic regression as a parametric and neural networks as a non -parametric method. The methods were tested on the models with and without variable reduction. The classification accuracy of the models was compared using statistical tests and ROC curves. The results showed that neural networks produce a significantly higher classification accuracy in the model when incorporating all available variables. The paper further discusses the advantages and disadvantages of both approaches, i.e. logistic regression and neural networks in modelling company growth. The suggested model is potentially of benefit to investors and economic policy makers as it provides support for recognizing companies with growth potential, especially during times of economic downturn.

  3. Quasi-experimental evidence on tobacco tax regressivity.

    Science.gov (United States)

    Koch, Steven F

    2018-01-01

    Tobacco taxes are known to reduce tobacco consumption and to be regressive, such that tobacco control policy may have the perverse effect of further harming the poor. However, if tobacco consumption falls faster amongst the poor than the rich, tobacco control policy can actually be progressive. We take advantage of persistent and committed tobacco control activities in South Africa to examine the household tobacco expenditure burden. For the analysis, we make use of two South African Income and Expenditure Surveys (2005/06 and 2010/11) that span a series of such tax increases and have been matched across the years, yielding 7806 matched pairs of tobacco consuming households and 4909 matched pairs of cigarette consuming households. By matching households across the surveys, we are able to examine both the regressivity of the household tobacco burden, and any change in that regressivity, and since tobacco taxes have been a consistent component of tobacco prices, our results also relate to the regressivity of tobacco taxes. Like previous research into cigarette and tobacco expenditures, we find that the tobacco burden is regressive; thus, so are tobacco taxes. However, we find that over the five-year period considered, the tobacco burden has decreased, and, most importantly, falls less heavily on the poor. Thus, the tobacco burden and the tobacco tax is less regressive in 2010/11 than in 2005/06. Thus, increased tobacco taxes can, in at least some circumstances, reduce the financial burden that tobacco places on households. Copyright © 2017 Elsevier Ltd. All rights reserved.

  4. Neck-focused panic attacks among Cambodian refugees; a logistic and linear regression analysis.

    Science.gov (United States)

    Hinton, Devon E; Chhean, Dara; Pich, Vuth; Um, Khin; Fama, Jeanne M; Pollack, Mark H

    2006-01-01

    Consecutive Cambodian refugees attending a psychiatric clinic were assessed for the presence and severity of current--i.e., at least one episode in the last month--neck-focused panic. Among the whole sample (N=130), in a logistic regression analysis, the Anxiety Sensitivity Index (ASI; odds ratio=3.70) and the Clinician-Administered PTSD Scale (CAPS; odds ratio=2.61) significantly predicted the presence of current neck panic (NP). Among the neck panic patients (N=60), in the linear regression analysis, NP severity was significantly predicted by NP-associated flashbacks (beta=.42), NP-associated catastrophic cognitions (beta=.22), and CAPS score (beta=.28). Further analysis revealed the effect of the CAPS score to be significantly mediated (Sobel test [Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173-1182]) by both NP-associated flashbacks and catastrophic cognitions. In the care of traumatized Cambodian refugees, NP severity, as well as NP-associated flashbacks and catastrophic cognitions, should be specifically assessed and treated.

  5. Polylinear regression analysis in radiochemistry

    International Nuclear Information System (INIS)

    Kopyrin, A.A.; Terent'eva, T.N.; Khramov, N.N.

    1995-01-01

    A number of radiochemical problems have been formulated in the framework of polylinear regression analysis, which permits the use of conventional mathematical methods for their solution. The authors have considered features of the use of polylinear regression analysis for estimating the contributions of various sources to the atmospheric pollution, for studying irradiated nuclear fuel, for estimating concentrations from spectral data, for measuring neutron fields of a nuclear reactor, for estimating crystal lattice parameters from X-ray diffraction patterns, for interpreting data of X-ray fluorescence analysis, for estimating complex formation constants, and for analyzing results of radiometric measurements. The problem of estimating the target parameters can be incorrect at certain properties of the system under study. The authors showed the possibility of regularization by adding a fictitious set of data open-quotes obtainedclose quotes from the orthogonal design. To estimate only a part of the parameters under consideration, the authors used incomplete rank models. In this case, it is necessary to take into account the possibility of confounding estimates. An algorithm for evaluating the degree of confounding is presented which is realized using standard software or regression analysis

  6. The impact of global signal regression on resting state correlations: are anti-correlated networks introduced?

    Science.gov (United States)

    Murphy, Kevin; Birn, Rasmus M; Handwerker, Daniel A; Jones, Tyler B; Bandettini, Peter A

    2009-02-01

    Low-frequency fluctuations in fMRI signal have been used to map several consistent resting state networks in the brain. Using the posterior cingulate cortex as a seed region, functional connectivity analyses have found not only positive correlations in the default mode network but negative correlations in another resting state network related to attentional processes. The interpretation is that the human brain is intrinsically organized into dynamic, anti-correlated functional networks. Global variations of the BOLD signal are often considered nuisance effects and are commonly removed using a general linear model (GLM) technique. This global signal regression method has been shown to introduce negative activation measures in standard fMRI analyses. The topic of this paper is whether such a correction technique could be the cause of anti-correlated resting state networks in functional connectivity analyses. Here we show that, after global signal regression, correlation values to a seed voxel must sum to a negative value. Simulations also show that small phase differences between regions can lead to spurious negative correlation values. A combination breath holding and visual task demonstrates that the relative phase of global and local signals can affect connectivity measures and that, experimentally, global signal regression leads to bell-shaped correlation value distributions, centred on zero. Finally, analyses of negatively correlated networks in resting state data show that global signal regression is most likely the cause of anti-correlations. These results call into question the interpretation of negatively correlated regions in the brain when using global signal regression as an initial processing step.

  7. Functional regression method for whole genome eQTL epistasis analysis with sequencing data.

    Science.gov (United States)

    Xu, Kelin; Jin, Li; Xiong, Momiao

    2017-05-18

    Epistasis plays an essential rule in understanding the regulation mechanisms and is an essential component of the genetic architecture of the gene expressions. However, interaction analysis of gene expressions remains fundamentally unexplored due to great computational challenges and data availability. Due to variation in splicing, transcription start sites, polyadenylation sites, post-transcriptional RNA editing across the entire gene, and transcription rates of the cells, RNA-seq measurements generate large expression variability and collectively create the observed position level read count curves. A single number for measuring gene expression which is widely used for microarray measured gene expression analysis is highly unlikely to sufficiently account for large expression variation across the gene. Simultaneously analyzing epistatic architecture using the RNA-seq and whole genome sequencing (WGS) data poses enormous challenges. We develop a nonlinear functional regression model (FRGM) with functional responses where the position-level read counts within a gene are taken as a function of genomic position, and functional predictors where genotype profiles are viewed as a function of genomic position, for epistasis analysis with RNA-seq data. Instead of testing the interaction of all possible pair-wises SNPs, the FRGM takes a gene as a basic unit for epistasis analysis, which tests for the interaction of all possible pairs of genes and use all the information that can be accessed to collectively test interaction between all possible pairs of SNPs within two genome regions. By large-scale simulations, we demonstrate that the proposed FRGM for epistasis analysis can achieve the correct type 1 error and has higher power to detect the interactions between genes than the existing methods. The proposed methods are applied to the RNA-seq and WGS data from the 1000 Genome Project. The numbers of pairs of significantly interacting genes after Bonferroni correction

  8. Influence diagnostics in meta-regression model.

    Science.gov (United States)

    Shi, Lei; Zuo, ShanShan; Yu, Dalei; Zhou, Xiaohua

    2017-09-01

    This paper studies the influence diagnostics in meta-regression model including case deletion diagnostic and local influence analysis. We derive the subset deletion formulae for the estimation of regression coefficient and heterogeneity variance and obtain the corresponding influence measures. The DerSimonian and Laird estimation and maximum likelihood estimation methods in meta-regression are considered, respectively, to derive the results. Internal and external residual and leverage measure are defined. The local influence analysis based on case-weights perturbation scheme, responses perturbation scheme, covariate perturbation scheme, and within-variance perturbation scheme are explored. We introduce a method by simultaneous perturbing responses, covariate, and within-variance to obtain the local influence measure, which has an advantage of capable to compare the influence magnitude of influential studies from different perturbations. An example is used to illustrate the proposed methodology. Copyright © 2017 John Wiley & Sons, Ltd.

  9. The more total cognitive load is reduced by cues, the better retention and transfer of multimedia learning: A meta-analysis and two meta-regression analyses.

    Science.gov (United States)

    Xie, Heping; Wang, Fuxing; Hao, Yanbin; Chen, Jiaxue; An, Jing; Wang, Yuxin; Liu, Huashan

    2017-01-01

    Cueing facilitates retention and transfer of multimedia learning. From the perspective of cognitive load theory (CLT), cueing has a positive effect on learning outcomes because of the reduction in total cognitive load and avoidance of cognitive overload. However, this has not been systematically evaluated. Moreover, what remains ambiguous is the direct relationship between the cue-related cognitive load and learning outcomes. A meta-analysis and two subsequent meta-regression analyses were conducted to explore these issues. Subjective total cognitive load (SCL) and scores on a retention test and transfer test were selected as dependent variables. Through a systematic literature search, 32 eligible articles encompassing 3,597 participants were included in the SCL-related meta-analysis. Among them, 25 articles containing 2,910 participants were included in the retention-related meta-analysis and the following retention-related meta-regression, while there were 29 articles containing 3,204 participants included in the transfer-related meta-analysis and the transfer-related meta-regression. The meta-analysis revealed a statistically significant cueing effect on subjective ratings of cognitive load (d = -0.11, 95% CI = [-0.19, -0.02], p < 0.05), retention performance (d = 0.27, 95% CI = [0.08, 0.46], p < 0.01), and transfer performance (d = 0.34, 95% CI = [0.12, 0.56], p < 0.01). The subsequent meta-regression analyses showed that dSCL for cueing significantly predicted dretention for cueing (β = -0.70, 95% CI = [-1.02, -0.38], p < 0.001), as well as dtransfer for cueing (β = -0.60, 95% CI = [-0.92, -0.28], p < 0.001). Thus in line with CLT, adding cues in multimedia materials can indeed reduce SCL and promote learning outcomes, and the more SCL is reduced by cues, the better retention and transfer of multimedia learning.

  10. Variable and subset selection in PLS regression

    DEFF Research Database (Denmark)

    Høskuldsson, Agnar

    2001-01-01

    The purpose of this paper is to present some useful methods for introductory analysis of variables and subsets in relation to PLS regression. We present here methods that are efficient in finding the appropriate variables or subset to use in the PLS regression. The general conclusion...... is that variable selection is important for successful analysis of chemometric data. An important aspect of the results presented is that lack of variable selection can spoil the PLS regression, and that cross-validation measures using a test set can show larger variation, when we use different subsets of X, than...

  11. Regression filter for signal resolution

    International Nuclear Information System (INIS)

    Matthes, W.

    1975-01-01

    The problem considered is that of resolving a measured pulse height spectrum of a material mixture, e.g. gamma ray spectrum, Raman spectrum, into a weighed sum of the spectra of the individual constituents. The model on which the analytical formulation is based is described. The problem reduces to that of a multiple linear regression. A stepwise linear regression procedure was constructed. The efficiency of this method was then tested by transforming the procedure in a computer programme which was used to unfold test spectra obtained by mixing some spectra, from a library of arbitrary chosen spectra, and adding a noise component. (U.K.)

  12. The Role of Perceived Social Support in Explanation of Positive and Negative Syndrome in Patients with Schizophrenia

    Directory of Open Access Journals (Sweden)

    akbar Atadokht

    2014-12-01

    Full Text Available Background & objectives: Chronic psychiatric patients not only become inactive members of community but also the heavy costs of their maintenance and rehabilitation burden on society and their family. According to importance of subject, this study aimed to investigate the role of percieved social support in predicting positive and negative syndrome in patients with schizophrenia.   Methods: In this descriptive-correlational study, 124 patients have been selected among patients with schizophrenia hospitalized in Issar Psychiatric Hospital and Rehabilitation Centers in first 3 mounths of 2014 in Ardabil, Iran and completed Positive and Negative Syndrome Scale (PANSS, Multidimensional Scale of Perceived Social Support (MSPSS and a researcher made demographic checklist. Data were analyzed by Pearson correlation coefficient and multivariate regression analysis on SPSS-16 software and P values less than 0.05 were considered statistically significant.   Results: The mean age of participants was 36.34±9.09 and their education level was mostly (58% primary or illiterate. Results showed that there is a negative relationship between patients positive symptoms index and their family, some others and total social support (p<0.01 and also between negative symptoms index and patients friends, some others and total social support (p<0.05. Results of multivariate regression analysis showed that 11% of positive and negative symptom syndrome are explained by percieved social support in patients with schizophrenia (p<0.01.   Conclusion: Percieved social support has relationship with positive and negative syndrome of patients with schizophrenia and measures to increase resources of social support and promotion of patients percieved social support can be used as an effective intervention by clinicians, patients and their family.

  13. Direction of Effects in Multiple Linear Regression Models.

    Science.gov (United States)

    Wiedermann, Wolfgang; von Eye, Alexander

    2015-01-01

    Previous studies analyzed asymmetric properties of the Pearson correlation coefficient using higher than second order moments. These asymmetric properties can be used to determine the direction of dependence in a linear regression setting (i.e., establish which of two variables is more likely to be on the outcome side) within the framework of cross-sectional observational data. Extant approaches are restricted to the bivariate regression case. The present contribution extends the direction of dependence methodology to a multiple linear regression setting by analyzing distributional properties of residuals of competing multiple regression models. It is shown that, under certain conditions, the third central moments of estimated regression residuals can be used to decide upon direction of effects. In addition, three different approaches for statistical inference are discussed: a combined D'Agostino normality test, a skewness difference test, and a bootstrap difference test. Type I error and power of the procedures are assessed using Monte Carlo simulations, and an empirical example is provided for illustrative purposes. In the discussion, issues concerning the quality of psychological data, possible extensions of the proposed methods to the fourth central moment of regression residuals, and potential applications are addressed.

  14. Robust mislabel logistic regression without modeling mislabel probabilities.

    Science.gov (United States)

    Hung, Hung; Jou, Zhi-Yu; Huang, Su-Yun

    2018-03-01

    Logistic regression is among the most widely used statistical methods for linear discriminant analysis. In many applications, we only observe possibly mislabeled responses. Fitting a conventional logistic regression can then lead to biased estimation. One common resolution is to fit a mislabel logistic regression model, which takes into consideration of mislabeled responses. Another common method is to adopt a robust M-estimation by down-weighting suspected instances. In this work, we propose a new robust mislabel logistic regression based on γ-divergence. Our proposal possesses two advantageous features: (1) It does not need to model the mislabel probabilities. (2) The minimum γ-divergence estimation leads to a weighted estimating equation without the need to include any bias correction term, that is, it is automatically bias-corrected. These features make the proposed γ-logistic regression more robust in model fitting and more intuitive for model interpretation through a simple weighting scheme. Our method is also easy to implement, and two types of algorithms are included. Simulation studies and the Pima data application are presented to demonstrate the performance of γ-logistic regression. © 2017, The International Biometric Society.

  15. Ordinal regression models to describe tourist satisfaction with Sintra's world heritage

    Science.gov (United States)

    Mouriño, Helena

    2013-10-01

    In Tourism Research, ordinal regression models are becoming a very powerful tool in modelling the relationship between an ordinal response variable and a set of explanatory variables. In August and September 2010, we conducted a pioneering Tourist Survey in Sintra, Portugal. The data were obtained by face-to-face interviews at the entrances of the Palaces and Parks of Sintra. The work developed in this paper focus on two main points: tourists' perception of the entrance fees; overall level of satisfaction with this heritage site. For attaining these goals, ordinal regression models were developed. We concluded that tourist's nationality was the only significant variable to describe the perception of the admission fees. Also, Sintra's image among tourists depends not only on their nationality, but also on previous knowledge about Sintra's World Heritage status.

  16. The role of strategic position in brand promise: Evidence from LG Company

    Directory of Open Access Journals (Sweden)

    A. Eilaghi Karvandi

    2016-08-01

    Full Text Available This paper presents an empirical investigation to study the effects of different strategies including attribute, advantage, application, consumer, competitive advantage, pricing/quality and category on brand promise for products of LG Company in city of Tehran, Iran. The study designs two questionnaires, one for strategic positioning and the other for brand promise in Likert scale. Cronbach alphas for brand promise and strategic positioning are 0.81 and 0.79, respectively. The questionnaires are distributed among 385 randomly selected regular users of LG products and using Spearman correlation as well as Stepwise regression techniques, the effects of various strategies on brand promise are examined. The results of the implementation of Spearman correlation have indicated that there were positive and meaningful relationships between different strategies and brand promise. In addition, the results of Stepwise regression have indicated that three strategies of price/quality, consumer and application were the most important predictors of brand promise.

  17. Finance-growth nexus: Insights from an application of threshold regression model to Malaysia's dual financial system

    Directory of Open Access Journals (Sweden)

    Alaa Alaabed

    2016-06-01

    Full Text Available The purpose of this paper is to test the growing converging views regarding the destabilizing and growth-halting impact of interest-based debt financial system. The views are as advocated by the followers of Keynes and Hyman Minsky and those of Islam. Islam discourages interest rate based debt financing as it considers it not very conducive to productive activities and human solidarity. Likewise, since the onset of the crisis of 2007/2008, calls by skeptics of mainstream capitalism have been renewed. The paper applies a threshold regression model to Malaysian data and finds that the relationship between growth and financial development is non-linear. A threshold is estimated, after which credit expansion negatively impacts GDP growth. While the post-threshold negative relationship is found to be statistically significant, the estimated positive relationship at lower levels of financial development is insignificant. The findings provide support to the above views and are hoped to guide monetary authorities to better growth-promoting policy-making.

  18. The effects of intraoperative positioning on patients undergoing early definitive care for femoral shaft fractures.

    Science.gov (United States)

    Apostle, K L; Lefaivre, K A; Guy, P; Broekhuyse, H M; Blachut, P A; O'Brien, P J; Meek, R N

    2009-10-01

    To determine if there is a difference in morbidity and mortality in orthopaedic trauma patients with femoral shaft fractures undergoing early definitive care with intramedullary (IM) nails in the supine versus the lateral position. Retrospective cohort study, single centered. One level 1 trauma center. Nine hundred eighty-eight patients representing 1027 femoral shaft fractures treated with IM nails were identified through a prospectively gathered database between 1987 and 2006. Antegrade IM nail insertion with reaming of the femoral canal in either the supine or lateral position. Mortality was the primary outcome. Admission to intensive care unit (ICU) was the secondary outcome measure and a surrogate measure of morbidity. Literature review was performed to identify factors shown to contribute to morbidity and mortality in orthopaedic trauma patients. Intraoperative position in either the supine or lateral position was added to this list. Logistic regression analysis was performed to determine the magnitude and effect of the independent variables on each of the study end points. To determine if a more significant trend toward less favorable outcomes was observed with increasing severity of injury, particularly injuries of the chest and thorax, subgroup analysis was performed for all those with a femur fracture and an Injury Severity Score > or =18 and all those with a femur fracture and an Abbreviated Injury Score chest > or =3. Intraoperative position in either the supine or lateral position was not a significant predictor of mortality or ICU admission for the original cohort or the subgroup of Injury Severity Score > or =18. However, for the subgroup of Abbreviated Injury Score chest > or =3, intraoperative positioning in the lateral position had a statistically significant protective effect against ICU admission (P = 0.044). For polytrauma patients with femoral shaft fractures, surgical stabilization using IM nails inserted with reaming of the femoral canal in

  19. Cluster regression model and level fluctuation features of Van Lake, Turkey

    Directory of Open Access Journals (Sweden)

    Z. Şen

    1999-02-01

    Full Text Available Lake water levels change under the influences of natural and/or anthropogenic environmental conditions. Among these influences are the climate change, greenhouse effects and ozone layer depletions which are reflected in the hydrological cycle features over the lake drainage basins. Lake levels are among the most significant hydrological variables that are influenced by different atmospheric and environmental conditions. Consequently, lake level time series in many parts of the world include nonstationarity components such as shifts in the mean value, apparent or hidden periodicities. On the other hand, many lake level modeling techniques have a stationarity assumption. The main purpose of this work is to develop a cluster regression model for dealing with nonstationarity especially in the form of shifting means. The basis of this model is the combination of transition probability and classical regression technique. Both parts of the model are applied to monthly level fluctuations of Lake Van in eastern Turkey. It is observed that the cluster regression procedure does preserve the statistical properties and the transitional probabilities that are indistinguishable from the original data.Key words. Hydrology (hydrologic budget; stochastic processes · Meteorology and atmospheric dynamics (ocean-atmosphere interactions

  20. Cluster regression model and level fluctuation features of Van Lake, Turkey

    Directory of Open Access Journals (Sweden)

    Z. Şen

    Full Text Available Lake water levels change under the influences of natural and/or anthropogenic environmental conditions. Among these influences are the climate change, greenhouse effects and ozone layer depletions which are reflected in the hydrological cycle features over the lake drainage basins. Lake levels are among the most significant hydrological variables that are influenced by different atmospheric and environmental conditions. Consequently, lake level time series in many parts of the world include nonstationarity components such as shifts in the mean value, apparent or hidden periodicities. On the other hand, many lake level modeling techniques have a stationarity assumption. The main purpose of this work is to develop a cluster regression model for dealing with nonstationarity especially in the form of shifting means. The basis of this model is the combination of transition probability and classical regression technique. Both parts of the model are applied to monthly level fluctuations of Lake Van in eastern Turkey. It is observed that the cluster regression procedure does preserve the statistical properties and the transitional probabilities that are indistinguishable from the original data.

    Key words. Hydrology (hydrologic budget; stochastic processes · Meteorology and atmospheric dynamics (ocean-atmosphere interactions