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

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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.

Exponential Decay Nonlinear Regression Analysis of Patient Survival Curves: Preliminary Assessment in Non-Small Cell Lung Cancer

Science.gov (United States)

Stewart, David J.; Behrens, Carmen; Roth, Jack; Wistuba, Ignacio I.

2010-01-01

Background For processes that follow first order kinetics, exponential decay nonlinear regression analysis (EDNRA) may delineate curve characteristics and suggest processes affecting curve shape. We conducted a preliminary feasibility assessment of EDNRA of patient survival curves. Methods EDNRA was performed on Kaplan-Meier overall survival (OS) and time-to-relapse (TTR) curves for 323 patients with resected NSCLC and on OS and progression-free survival (PFS) curves from selected publications. Results and Conclusions In our resected patients, TTR curves were triphasic with a “cured” fraction of 60.7% (half-life [t1/2] >100,000 months), a rapidly-relapsing group (7.4%, t1/2=5.9 months) and a slowly-relapsing group (31.9%, t1/2=23.6 months). OS was uniphasic (t1/2=74.3 months), suggesting an impact of co-morbidities; hence, tumor molecular characteristics would more likely predict TTR than OS. Of 172 published curves analyzed, 72 (42%) were uniphasic, 92 (53%) were biphasic, 8 (5%) were triphasic. With first-line chemotherapy in advanced NSCLC, 87.5% of curves from 2-3 drug regimens were uniphasic vs only 20% of those with best supportive care or 1 drug (p<0.001). 54% of curves from 2-3 drug regimens had convex rapid-decay phases vs 0% with fewer agents (p<0.001). Curve convexities suggest that discontinuing chemotherapy after 3-6 cycles “synchronizes” patient progression and death. With postoperative adjuvant chemotherapy, the PFS rapid-decay phase accounted for a smaller proportion of the population than in controls (p=0.02) with no significant difference in rapid-decay t1/2, suggesting adjuvant chemotherapy may move a subpopulation of patients with sensitive tumors from the relapsing group to the cured group, with minimal impact on time to relapse for a larger group of patients with resistant tumors. In untreated patients, the proportion of patients in the rapid-decay phase increased (p=0.04) while rapid-decay t1/2 decreased (p=0.0004) with increasing

[A SAS marco program for batch processing of univariate Cox regression analysis for great database].

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Yang, Rendong; Xiong, Jie; Peng, Yangqin; Peng, Xiaoning; Zeng, Xiaomin

2015-02-01

To realize batch processing of univariate Cox regression analysis for great database by SAS marco program. We wrote a SAS macro program, which can filter, integrate, and export P values to Excel by SAS9.2. The program was used for screening survival correlated RNA molecules of ovarian cancer. A SAS marco program could finish the batch processing of univariate Cox regression analysis, the selection and export of the results. The SAS macro program has potential applications in reducing the workload of statistical analysis and providing a basis for batch processing of univariate Cox regression analysis.

Survival analysis using S analysis of time-to-event data

CERN Document Server

Tableman, Mara

2003-01-01

Survival Analysis Using S: Analysis of Time-to-Event Data is designed as a text for a one-semester or one-quarter course in survival analysis for upper-level or graduate students in statistics, biostatistics, and epidemiology. Prerequisites are a standard pre-calculus first course in probability and statistics, and a course in applied linear regression models. No prior knowledge of S or R is assumed. A wide choice of exercises is included, some intended for more advanced students with a first course in mathematical statistics. The authors emphasize parametric log-linear models, while also detailing nonparametric procedures along with model building and data diagnostics. Medical and public health researchers will find the discussion of cut point analysis with bootstrap validation, competing risks and the cumulative incidence estimator, and the analysis of left-truncated and right-censored data invaluable. The bootstrap procedure checks robustness of cut point analysis and determines cut point(s). In a chapter ...

Survival analysis in hematologic malignancies: recommendations for clinicians

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Delgado, Julio; Pereira, Arturo; Villamor, Neus; López-Guillermo, Armando; Rozman, Ciril

2014-01-01

The widespread availability of statistical packages has undoubtedly helped hematologists worldwide in the analysis of their data, but has also led to the inappropriate use of statistical methods. In this article, we review some basic concepts of survival analysis and also make recommendations about how and when to perform each particular test using SPSS, Stata and R. In particular, we describe a simple way of defining cut-off points for continuous variables and the appropriate and inappropriate uses of the Kaplan-Meier method and Cox proportional hazard regression models. We also provide practical advice on how to check the proportional hazards assumption and briefly review the role of relative survival and multiple imputation. PMID:25176982

Advanced Online Survival Analysis Tool for Predictive Modelling in Clinical Data Science.

Science.gov (United States)

Montes-Torres, Julio; Subirats, José Luis; Ribelles, Nuria; Urda, Daniel; Franco, Leonardo; Alba, Emilio; Jerez, José Manuel

2016-01-01

One of the prevailing applications of machine learning is the use of predictive modelling in clinical survival analysis. In this work, we present our view of the current situation of computer tools for survival analysis, stressing the need of transferring the latest results in the field of machine learning to biomedical researchers. We propose a web based software for survival analysis called OSA (Online Survival Analysis), which has been developed as an open access and user friendly option to obtain discrete time, predictive survival models at individual level using machine learning techniques, and to perform standard survival analysis. OSA employs an Artificial Neural Network (ANN) based method to produce the predictive survival models. Additionally, the software can easily generate survival and hazard curves with multiple options to personalise the plots, obtain contingency tables from the uploaded data to perform different tests, and fit a Cox regression model from a number of predictor variables. In the Materials and Methods section, we depict the general architecture of the application and introduce the mathematical background of each of the implemented methods. The study concludes with examples of use showing the results obtained with public datasets.

Survival Prediction and Feature Selection in Patients with Breast Cancer Using Support Vector Regression

Directory of Open Access Journals (Sweden)

Shahrbanoo Goli

2016-01-01

Full Text Available The Support Vector Regression (SVR model has been broadly used for response prediction. However, few researchers have used SVR for survival analysis. In this study, a new SVR model is proposed and SVR with different kernels and the traditional Cox model are trained. The models are compared based on different performance measures. We also select the best subset of features using three feature selection methods: combination of SVR and statistical tests, univariate feature selection based on concordance index, and recursive feature elimination. The evaluations are performed using available medical datasets and also a Breast Cancer (BC dataset consisting of 573 patients who visited the Oncology Clinic of Hamadan province in Iran. Results show that, for the BC dataset, survival time can be predicted more accurately by linear SVR than nonlinear SVR. Based on the three feature selection methods, metastasis status, progesterone receptor status, and human epidermal growth factor receptor 2 status are the best features associated to survival. Also, according to the obtained results, performance of linear and nonlinear kernels is comparable. The proposed SVR model performs similar to or slightly better than other models. Also, SVR performs similar to or better than Cox when all features are included in model.

Enhanced left ventricular mass regression after aortic valve replacement in patients with aortic stenosis is associated with improved long-term survival.

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Ali, Ayyaz; Patel, Amit; Ali, Ziad; Abu-Omar, Yasir; Saeed, Amber; Athanasiou, Thanos; Pepper, John

2011-08-01

Aortic valve replacement in patients with aortic stenosis is usually followed by regression of left ventricular hypertrophy. More complete resolution of left ventricular hypertrophy is suggested to be associated with superior clinical outcomes; however, its translational impact on long-term survival after aortic valve replacement has not been investigated. Demographic, operative, and clinical data were obtained retrospectively through case note review. Transthoracic echocardiography was used to measure left ventricular mass preoperatively and at annual follow-up visits. Patients were classified according to their reduction in left ventricular mass at 1 year after the operation: group 1, less than 25 g; group 2, 25 to 150 g; and group 3, more than 150 g. Kaplan-Meier and multivariable Cox regression were used. A total of 147 patients were discharged from the hospital after aortic valve replacement for aortic stenosis between 1991 and 2001. Preoperative left ventricular mass was 279 ± 98 g in group 1 (n = 47), 347 ± 104 g in group 2 (n = 62), and 491 ± 183 g in group 3 (n = 38) (P regression such as ischemic heart disease or hypertension, valve type, or valve size used. Ten-year actuarial survival was not statistically different in patients with enhanced left ventricular mass regression when compared with the log-rank test (group 1, 51% ± 9%; group 2, 54% ± 8%; and group 3, 72% ± 10%) (P = .26). After adjustment, left ventricular mass reduction of more than 150 g was demonstrated as an independent predictor of improved long-term survival on multivariate analysis (P = .02). Our study is the first to suggest that enhanced postoperative left ventricular mass regression, specifically in patients undergoing aortic valve replacement for aortic stenosis, may be associated with improved long-term survival. In view of these findings, strategies purported to be associated with superior left ventricular mass regression should be considered when undertaking

Association between obesity with disease-free survival and overall survival in triple-negative breast cancer: A meta-analysis.

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Mei, Lin; He, Lin; Song, Yuhua; Lv, Yang; Zhang, Lijiu; Hao, Fengxi; Xu, Mengmeng

2018-05-01

To investigate the relationship between obesity and disease-free survival (DFS) and overall survival (OS) of triple-negative breast cancer. Citations were searched in PubMed, Cochrane Library, and Web of Science. Random effect model meta-analysis was conducted by using Revman software version 5.0, and publication bias was evaluated by creating Egger regression with STATA software version 12. Nine studies (4412 patients) were included for DFS meta-analysis, 8 studies (4392 patients) include for OS meta-analysis. There were no statistical significances between obesity with DFS (P = .60) and OS (P = .71) in triple-negative breast cancer (TNBC) patients. Obesity has no impact on DFS and OS in patients with TNBC.

Survival analysis

International Nuclear Information System (INIS)

Badwe, R.A.

1999-01-01

The primary endpoint in the majority of the studies has been either disease recurrence or death. This kind of analysis requires a special method since all patients in the study experience the endpoint. The standard method for estimating such survival distribution is Kaplan Meier method. The survival function is defined as the proportion of individuals who survive beyond certain time. Multi-variate comparison for survival has been carried out with Cox's proportional hazard model

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.

Survival analysis of dialysis patients in selected hospitals of lahore city

International Nuclear Information System (INIS)

Ahmad, Z.; Shahzad, I.

2015-01-01

There are several reasons which are directly or indirectly relate to affect the survival time of End Stage Renal Disease (ESRD) patients. This study was done to analyse the survival rate of ESRD patients in Lahore city, and to evaluate the influence of various risk factors and prognostic factors on survival of these patients. Methods: A sample of 40 patients was taken from the Jinnah Hospital Lahore and Lahore General Hospital by using the convenience sampling technique. The Log Rank Test was used to determine the significant difference between the categories of qualitative variables of ESRD patients. Multivariate Cox Regression Analysis was used to analyse the effect of different clinical and socio-economic variables on the survival time of these patients. Results: Different qualitative variables like: age, marital status, BMI, comorbid factors, diabetes type, gender, income level, place, risk factor like diabetes, ischemic heart disease, hypertension and Hepatitis status were analysed on the basis of Log Rank Test. While age and comorbid factors were found to be statistically significant which showed that the distribution of age and comorbid factors were different. By using the Cox Regression analysis the coefficient of Mass, serum albumin and family history of diabetes were found to be significant. Conclusions: There were some of the factors which had been taken for the analysis came out less or more significant in patients of ESRD. So it was concluded that mostly clinical factors which were Mass of the Patient, Serum Albumin and Family History of Diabetes made significant contribution towards the survival status of patients. (author)

Survival analysis models and applications

CERN Document Server

Liu, Xian

2012-01-01

Survival analysis concerns sequential occurrences of events governed by probabilistic laws.  Recent decades have witnessed many applications of survival analysis in various disciplines. This book introduces both classic survival models and theories along with newly developed techniques. Readers will learn how to perform analysis of survival data by following numerous empirical illustrations in SAS. Survival Analysis: Models and Applications: Presents basic techniques before leading onto some of the most advanced topics in survival analysis.Assumes only a minimal knowledge of SAS whilst enablin

Prognostic and survival analysis of 837 Chinese colorectal cancer patients.

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Yuan, Ying; Li, Mo-Dan; Hu, Han-Guang; Dong, Cai-Xia; Chen, Jia-Qi; Li, Xiao-Fen; Li, Jing-Jing; Shen, Hong

2013-05-07

To develop a prognostic model to predict survival of patients with colorectal cancer (CRC). Survival data of 837 CRC patients undergoing surgery between 1996 and 2006 were collected and analyzed by univariate analysis and Cox proportional hazard regression model to reveal the prognostic factors for CRC. All data were recorded using a standard data form and analyzed using SPSS version 18.0 (SPSS, Chicago, IL, United States). Survival curves were calculated by the Kaplan-Meier method. The log rank test was used to assess differences in survival. Univariate hazard ratios and significant and independent predictors of disease-specific survival and were identified by Cox proportional hazard analysis. The stepwise procedure was set to a threshold of 0.05. Statistical significance was defined as P analysis suggested age, preoperative obstruction, serum carcinoembryonic antigen level at diagnosis, status of resection, tumor size, histological grade, pathological type, lymphovascular invasion, invasion of adjacent organs, and tumor node metastasis (TNM) staging were positive prognostic factors (P analysis showed a significant statistical difference in 3-year survival among these groups: LNR1, 73%; LNR2, 55%; and LNR3, 42% (P analysis results showed that histological grade, depth of bowel wall invasion, and number of metastatic lymph nodes were the most important prognostic factors for CRC if we did not consider the interaction of the TNM staging system (P < 0.05). When the TNM staging was taken into account, histological grade lost its statistical significance, while the specific TNM staging system showed a statistically significant difference (P < 0.0001). The overall survival of CRC patients has improved between 1996 and 2006. LNR is a powerful factor for estimating the survival of stage III CRC patients.

Prediction of survival to discharge following cardiopulmonary resuscitation using classification and regression trees.

Science.gov (United States)

Ebell, Mark H; Afonso, Anna M; Geocadin, Romergryko G

2013-12-01

To predict the likelihood that an inpatient who experiences cardiopulmonary arrest and undergoes cardiopulmonary resuscitation survives to discharge with good neurologic function or with mild deficits (Cerebral Performance Category score = 1). Classification and Regression Trees were used to develop branching algorithms that optimize the ability of a series of tests to correctly classify patients into two or more groups. Data from 2007 to 2008 (n = 38,092) were used to develop candidate Classification and Regression Trees models to predict the outcome of inpatient cardiopulmonary resuscitation episodes and data from 2009 (n = 14,435) to evaluate the accuracy of the models and judge the degree of over fitting. Both supervised and unsupervised approaches to model development were used. 366 hospitals participating in the Get With the Guidelines-Resuscitation registry. Adult inpatients experiencing an index episode of cardiopulmonary arrest and undergoing cardiopulmonary resuscitation in the hospital. The five candidate models had between 8 and 21 nodes and an area under the receiver operating characteristic curve from 0.718 to 0.766 in the derivation group and from 0.683 to 0.746 in the validation group. One of the supervised models had 14 nodes and classified 27.9% of patients as very unlikely to survive neurologically intact or with mild deficits (Tree models that predict survival to discharge with good neurologic function or with mild deficits following in-hospital cardiopulmonary arrest. Models like this can assist physicians and patients who are considering do-not-resuscitate orders.

Survival analysis of postoperative nausea and vomiting in patients receiving patient-controlled epidural analgesia

Directory of Open Access Journals (Sweden)

Shang-Yi Lee

2014-11-01

Conclusion: Survival analysis using Cox regression showed that the average consumption of opioids played an important role in postoperative nausea and vomiting, a result not found by logistic regression. Therefore, the incidence of postoperative nausea and vomiting in patients cannot be reliably determined on the basis of a single visit at one point in time.

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)

Survival Analysis of Patients with End Stage Renal Disease

Science.gov (United States)

Urrutia, J. D.; Gayo, W. S.; Bautista, L. A.; Baccay, E. B.

2015-06-01

This paper provides a survival analysis of End Stage Renal Disease (ESRD) under Kaplan-Meier Estimates and Weibull Distribution. The data were obtained from the records of V. L. MakabaliMemorial Hospital with respect to time t (patient's age), covariates such as developed secondary disease (Pulmonary Congestion and Cardiovascular Disease), gender, and the event of interest: the death of ESRD patients. Survival and hazard rates were estimated using NCSS for Weibull Distribution and SPSS for Kaplan-Meier Estimates. These lead to the same conclusion that hazard rate increases and survival rate decreases of ESRD patient diagnosed with Pulmonary Congestion, Cardiovascular Disease and both diseases with respect to time. It also shows that female patients have a greater risk of death compared to males. The probability risk was given the equation R = 1 — e-H(t) where e-H(t) is the survival function, H(t) the cumulative hazard function which was created using Cox-Regression.

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...

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…

Regression analysis with categorized regression calibrated exposure: some interesting findings

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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

Replica analysis of overfitting in regression models for time-to-event data

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Coolen, A. C. C.; Barrett, J. E.; Paga, P.; Perez-Vicente, C. J.

2017-09-01

Overfitting, which happens when the number of parameters in a model is too large compared to the number of data points available for determining these parameters, is a serious and growing problem in survival analysis. While modern medicine presents us with data of unprecedented dimensionality, these data cannot yet be used effectively for clinical outcome prediction. Standard error measures in maximum likelihood regression, such as p-values and z-scores, are blind to overfitting, and even for Cox’s proportional hazards model (the main tool of medical statisticians), one finds in literature only rules of thumb on the number of samples required to avoid overfitting. In this paper we present a mathematical theory of overfitting in regression models for time-to-event data, which aims to increase our quantitative understanding of the problem and provide practical tools with which to correct regression outcomes for the impact of overfitting. It is based on the replica method, a statistical mechanical technique for the analysis of heterogeneous many-variable systems that has been used successfully for several decades in physics, biology, and computer science, but not yet in medical statistics. We develop the theory initially for arbitrary regression models for time-to-event data, and verify its predictions in detail for the popular Cox model.

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

Instrumental variable estimation in a survival context

DEFF Research Database (Denmark)

Tchetgen Tchetgen, Eric J; Walter, Stefan; Vansteelandt, Stijn

2015-01-01

for regression analysis in a survival context, primarily under an additive hazards model, for which we describe 2 simple methods for estimating causal effects. The first method is a straightforward 2-stage regression approach analogous to 2-stage least squares commonly used for IV analysis in linear regression....... The IV approach is very well developed in the context of linear regression and also for certain generalized linear models with a nonlinear link function. However, IV methods are not as well developed for regression analysis with a censored survival outcome. In this article, we develop the IV approach....... In this approach, the fitted value from a first-stage regression of the exposure on the IV is entered in place of the exposure in the second-stage hazard model to recover a valid estimate of the treatment effect of interest. The second method is a so-called control function approach, which entails adding...

Survival analysis of heart failure patients: A case study.

Science.gov (United States)

Ahmad, Tanvir; Munir, Assia; Bhatti, Sajjad Haider; Aftab, Muhammad; Raza, Muhammad Ali

2017-01-01

This study was focused on survival analysis of heart failure patients who were admitted to Institute of Cardiology and Allied hospital Faisalabad-Pakistan during April-December (2015). All the patients were aged 40 years or above, having left ventricular systolic dysfunction, belonging to NYHA class III and IV. Cox regression was used to model mortality considering age, ejection fraction, serum creatinine, serum sodium, anemia, platelets, creatinine phosphokinase, blood pressure, gender, diabetes and smoking status as potentially contributing for mortality. Kaplan Meier plot was used to study the general pattern of survival which showed high intensity of mortality in the initial days and then a gradual increase up to the end of study. Martingale residuals were used to assess functional form of variables. Results were validated computing calibration slope and discrimination ability of model via bootstrapping. For graphical prediction of survival probability, a nomogram was constructed. Age, renal dysfunction, blood pressure, ejection fraction and anemia were found as significant risk factors for mortality among heart failure patients.

ASURV: Astronomical SURVival Statistics

Science.gov (United States)

Feigelson, E. D.; Nelson, P. I.; Isobe, T.; LaValley, M.

2014-06-01

ASURV (Astronomical SURVival Statistics) provides astronomy survival analysis for right- and left-censored data including the maximum-likelihood Kaplan-Meier estimator and several univariate two-sample tests, bivariate correlation measures, and linear regressions. ASURV is written in FORTRAN 77, and is stand-alone and does not call any specialized libraries.

Impact of anastomotic leak on recurrence and survival after colorectal cancer surgery: a BioGrid Australia analysis.

Science.gov (United States)

Sammour, Tarik; Hayes, Ian P; Jones, Ian T; Steel, Malcolm C; Faragher, Ian; Gibbs, Peter

2018-01-01

There is conflicting evidence regarding the oncological impact of anastomotic leak following colorectal cancer surgery. This study aims to test the hypothesis that anastomotic leak is independently associated with local recurrence and overall and cancer-specific survival. Analysis of prospectively collected data from multiple centres in Victoria between 1988 and 2015 including all patients who underwent colon or rectal resection for cancer with anastomosis was presented. Overall and cancer-specific survival rates and rates of local recurrence were compared using Cox regression analysis. A total of 4892 patients were included, of which 2856 had completed 5-year follow-up. The overall anastomotic leak rate was 4.0%. Cox regression analysis accounting for differences in age, sex, body mass index, American Society of Anesthesiologists score and tumour stage demonstrated that anastomotic leak was associated with significantly worse 5-year overall survival (χ 2 = 6.459, P = 0.011) for colon cancer, but only if early deaths were included. There was no difference in 5-year colon cancer-specific survival (χ 2 = 0.582, P = 0.446) or local recurrence (χ 2 = 0.735, P = 0.391). For rectal cancer, there was no difference in 5-year overall survival (χ 2 = 0.266, P = 0.606), cancer-specific survival (χ 2 = 0.008, P = 0.928) or local recurrence (χ 2 = 2.192, P = 0.139). Anastomotic leak may reduce 5-year overall survival in colon cancer patients but does not appear to influence the 5-year overall survival in rectal cancer patients. There was no effect on local recurrence or cancer-specific survival. © 2016 Royal Australasian College of Surgeons.

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)

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.

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

Survival analysis of heart failure patients: A case study.

Directory of Open Access Journals (Sweden)

Tanvir Ahmad

Full Text Available This study was focused on survival analysis of heart failure patients who were admitted to Institute of Cardiology and Allied hospital Faisalabad-Pakistan during April-December (2015. All the patients were aged 40 years or above, having left ventricular systolic dysfunction, belonging to NYHA class III and IV. Cox regression was used to model mortality considering age, ejection fraction, serum creatinine, serum sodium, anemia, platelets, creatinine phosphokinase, blood pressure, gender, diabetes and smoking status as potentially contributing for mortality. Kaplan Meier plot was used to study the general pattern of survival which showed high intensity of mortality in the initial days and then a gradual increase up to the end of study. Martingale residuals were used to assess functional form of variables. Results were validated computing calibration slope and discrimination ability of model via bootstrapping. For graphical prediction of survival probability, a nomogram was constructed. Age, renal dysfunction, blood pressure, ejection fraction and anemia were found as significant risk factors for mortality among heart failure patients.

Survival Analysis of Factors Influencing Cyclic Fatigue of Nickel-Titanium Endodontic Instruments

Directory of Open Access Journals (Sweden)

Eva Fišerová

2015-01-01

Full Text Available Objective. The aim of this study was to validate a survival analysis assessing the effect of type of rotary system, canal curvature, and instrument size on cyclic resistance. Materials and Methods. Cyclic fatigue testing was carried out in stainless steel artificial canals with radii of curvature of 3 or 5 mm and the angle of curvature of 60 degrees. All the instruments were new and 25 mm in working length, and ISO colour coding indicated the instrument size (yellow for size 20; red for size 25. Wizard Navigator instruments, Mtwo instruments, ProTaper instruments, and Revo-S instruments were passively rotated at 250 rotations per minute, and the time fracture was being recorded. Subsequently, fractographic analysis of broken tips was performed by scanning electron microscope. The data were then analysed by the Kaplan-Meier estimator of the survival function, the Cox proportional hazards model, the Wald test for regression covariates, and the Wald test for significance of regression model. Conclusion. The lifespan registered for the tested instruments was Mtwo > Wizard Navigator > Revo-S > ProTaper; 5 mm radius > 3 mm radius; and yellow > red in ISO colour coding system.

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.

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...

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.

Survival Analysis

CERN Document Server

Miller, Rupert G

2011-01-01

A concise summary of the statistical methods used in the analysis of survival data with censoring. Emphasizes recently developed nonparametric techniques. Outlines methods in detail and illustrates them with actual data. Discusses the theory behind each method. Includes numerous worked problems and numerical exercises.

Hierarchical regression analysis in structural Equation Modeling

NARCIS (Netherlands)

de Jong, P.F.

1999-01-01

In a hierarchical or fixed-order regression analysis, the independent variables are entered into the regression equation in a prespecified order. Such an analysis is often performed when the extra amount of variance accounted for in a dependent variable by a specific independent variable is the main

Multivariate Regression Analysis and Slaughter Livestock,

Science.gov (United States)

AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY

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.

Bayesian logistic regression analysis

NARCIS (Netherlands)

Van Erp, H.R.N.; Van Gelder, P.H.A.J.M.

2012-01-01

In this paper we present a Bayesian logistic regression analysis. It is found that if one wishes to derive the posterior distribution of the probability of some event, then, together with the traditional Bayes Theorem and the integrating out of nuissance parameters, the Jacobian transformation is an

Multivariate survival analysis and competing risks

CERN Document Server

Crowder, Martin J

2012-01-01

Multivariate Survival Analysis and Competing Risks introduces univariate survival analysis and extends it to the multivariate case. It covers competing risks and counting processes and provides many real-world examples, exercises, and R code. The text discusses survival data, survival distributions, frailty models, parametric methods, multivariate data and distributions, copulas, continuous failure, parametric likelihood inference, and non- and semi-parametric methods. There are many books covering survival analysis, but very few that cover the multivariate case in any depth. Written for a graduate-level audience in statistics/biostatistics, this book includes practical exercises and R code for the examples. The author is renowned for his clear writing style, and this book continues that trend. It is an excellent reference for graduate students and researchers looking for grounding in this burgeoning field of research.

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.

A generalized additive regression model for survival times

DEFF Research Database (Denmark)

Scheike, Thomas H.

2001-01-01

Additive Aalen model; counting process; disability model; illness-death model; generalized additive models; multiple time-scales; non-parametric estimation; survival data; varying-coefficient models......Additive Aalen model; counting process; disability model; illness-death model; generalized additive models; multiple time-scales; non-parametric estimation; survival data; varying-coefficient models...

RAWS II: A MULTIPLE REGRESSION ANALYSIS PROGRAM,

Science.gov (United States)

This memorandum gives instructions for the use and operation of a revised version of RAWS, a multiple regression analysis program. The program...of preprocessed data, the directed retention of variable, listing of the matrix of the normal equations and its inverse, and the bypassing of the regression analysis to provide the input variable statistics only. (Author)

Survival Prediction in Pancreatic Ductal Adenocarcinoma by Quantitative Computed Tomography Image Analysis.

Science.gov (United States)

Attiyeh, Marc A; Chakraborty, Jayasree; Doussot, Alexandre; Langdon-Embry, Liana; Mainarich, Shiana; Gönen, Mithat; Balachandran, Vinod P; D'Angelica, Michael I; DeMatteo, Ronald P; Jarnagin, William R; Kingham, T Peter; Allen, Peter J; Simpson, Amber L; Do, Richard K

2018-04-01

Pancreatic cancer is a highly lethal cancer with no established a priori markers of survival. Existing nomograms rely mainly on post-resection data and are of limited utility in directing surgical management. This study investigated the use of quantitative computed tomography (CT) features to preoperatively assess survival for pancreatic ductal adenocarcinoma (PDAC) patients. A prospectively maintained database identified consecutive chemotherapy-naive patients with CT angiography and resected PDAC between 2009 and 2012. Variation in CT enhancement patterns was extracted from the tumor region using texture analysis, a quantitative image analysis tool previously described in the literature. Two continuous survival models were constructed, with 70% of the data (training set) using Cox regression, first based only on preoperative serum cancer antigen (CA) 19-9 levels and image features (model A), and then on CA19-9, image features, and the Brennan score (composite pathology score; model B). The remaining 30% of the data (test set) were reserved for independent validation. A total of 161 patients were included in the analysis. Training and test sets contained 113 and 48 patients, respectively. Quantitative image features combined with CA19-9 achieved a c-index of 0.69 [integrated Brier score (IBS) 0.224] on the test data, while combining CA19-9, imaging, and the Brennan score achieved a c-index of 0.74 (IBS 0.200) on the test data. We present two continuous survival prediction models for resected PDAC patients. Quantitative analysis of CT texture features is associated with overall survival. Further work includes applying the model to an external dataset to increase the sample size for training and to determine its applicability.

Outcome of cardiopulmonary resuscitation - predictors of survival

International Nuclear Information System (INIS)

Ishtiaq, O.; Iqbal, M.; Zubair, M.; Qayyum, R.; Adil, M.

2008-01-01

To assess the outcomes of patients undergoing cardiopulmonary resuscitation (CPR). Data were collected retrospectively of all adult patients who underwent CPR. Clinical outcomes of interest were survival at the end of CPR and survival at discharge from hospital. Factors associated with survival were evaluated using logistic regression analysis. Of the 159 patients included, 55 (35%) were alive at the end of CPR and 17 (11%) were discharged alive from the hospital. At the end of CPR, univariate logistic regression analysis found the following factors associated with survival: cardiac arrest within hospital as compared to outside the hospital (odds ratio = 2.8, 95% CI = 1.27-6.20, p-value = 0.01), both cardiac and pulmonary arrest as compared to either cardiac or pulmonary arrest (odds ratio = 0.37, 95% CI = 0.19- 0.73, p-value = 0.004), asystole as cardiac rhythm at presentation (odds ratio = 0.47, 95% CI = 0.24-0.93, p-value = 0.03), and total atropine dose given during CPR (odds ratio = 0.78, 95% CI = 0.62-0.97, p-value = 0.02). In multivariate logistic regression, cardiac arrest within hospital (odds ratio = 2.52, 95% CI = 1.06-5.99, p-value = 0.04) and both cardiac and pulmonary arrest as compared to cardiac or pulmonary arrest (odds ratio = 0.44, 95% CI = 0.21-0.91, p-value = 0.03) were associated with survival at the end of CPR. At the time of discharge from hospital, univariate logistic regression analysis found following factors that were associated with survival: cardiac arrest within hospital (odds ratio = 8.4, 95% CI = 1.09-65.64, p-value = 0.04), duration of CPR (odds ratio = 0.91, 95% CI = 0.85-0.96, p-value = 0.001), and total atropine dose given during CPR (odds ratio = 0.68, 95% CI = 0.47-0.99, p-value = 0.05). In multivariate logistic regression analysis cardiac arrest within hospital (odds ratio 8.69, 95% CI = 1.01-74.6, p-value = 0.05) and duration of CPR (odds ratio 0.92, 95% CI = 0.87-0.98, p-value = 0.01) were associated with survival at

Outcome predictors in the management of intramedullary classic ependymoma: An integrative survival analysis.

Science.gov (United States)

Wang, Yinqing; Cai, Ranze; Wang, Rui; Wang, Chunhua; Chen, Chunmei

2018-06-01

This is a retrospective study.The aim of this study was to illustrate the survival outcomes of patients with classic ependymoma (CE) and identify potential prognostic factors.CE is the most common category of spinal ependymomas, but few published studies have discussed predictors of the survival outcome.A Boolean search of the PubMed, Embase, and OVID databases was conducted by 2 investigators independently. The objects were intramedullary grade II ependymoma according to 2007 WHO classification. Univariate Kaplan-Meier analysis and Log-Rank tests were performed to identify variables associated with progression-free survival (PFS) or overall survival (OS). Multivariate Cox regression was performed to assess hazard ratios (HRs) with 95% confidence intervals (95% CIs). Statistical analysis was performed by SPSS version 23.0 (IBM Corp.) with statistical significance defined as P analysis showed that patients who had undergone total resection (TR) had better PFS and OS than those with subtotal resection (STR) and biopsy (P = .002, P = .004, respectively). Within either univariate or multivariate analysis (P = .000, P = .07, respectively), histological type was an independent prognostic factor for PFS of CE [papillary type: HR 0.002, 95% CI (0.000-0.073), P = .001, tanycytic type: HR 0.010, 95% CI (0.000-0.218), P = .003].It was the first integrative analysis of CE to elucidate the correlation between kinds of factors and prognostic outcomes. Definite histological type and safely TR were foundation of CE's management. 4.

Classification and Regression Tree Analysis of Clinical Patterns that Predict Survival in 127 Chinese Patients with Advanced Non-small Cell Lung Cancer Treated by Gefitinib Who Failed to Previous Chemotherapy

Directory of Open Access Journals (Sweden)

Ziping WANG

2011-09-01

Full Text Available Background and objective It has been proven that gefitinib produces only 10%-20% tumor regression in heavily pretreated, unselected non-small cell lung cancer (NSCLC patients as the second- and third-line setting. Asian, female, nonsmokers and adenocarcinoma are favorable factors; however, it is difficult to find a patient satisfying all the above clinical characteristics. The aim of this study is to identify novel predicting factors, and to explore the interactions between clinical variables and their impact on the survival of Chinese patients with advanced NSCLC who were heavily treated with gefitinib in the second- or third-line setting. Methods The clinical and follow-up data of 127 advanced NSCLC patients referred to the Cancer Hospital & Institute, Chinese Academy of Medical Sciences from March 2005 to March 2010 were analyzed. Multivariate analysis of progression-free survival (PFS was performed using recursive partitioning, which is referred to as the classification and regression tree (CART analysis. Results The median PFS of 127 eligible consecutive advanced NSCLC patients was 8.0 months (95%CI: 5.8-10.2. CART was performed with an initial split on first-line chemotherapy outcomes and a second split on patients’ age. Three terminal subgroups were formed. The median PFS of the three subsets ranged from 1.0 month (95%CI: 0.8-1.2 for those with progressive disease outcome after the first-line chemotherapy subgroup, 10 months (95%CI: 7.0-13.0 in patients with a partial response or stable disease in first-line chemotherapy and age <70, and 22.0 months for patients obtaining a partial response or stable disease in first-line chemotherapy at age 70-81 (95%CI: 3.8-40.1. Conclusion Partial response, stable disease in first-line chemotherapy and age ≥ 70 are closely correlated with long-term survival treated by gefitinib as a second- or third-line setting in advanced NSCLC. CART can be used to identify previously unappreciated patient

Surrogacy of progression-free survival (PFS) for overall survival (OS) in esophageal cancer trials with preoperative therapy: Literature-based meta-analysis.

Science.gov (United States)

Kataoka, K; Nakamura, K; Mizusawa, J; Kato, K; Eba, J; Katayama, H; Shibata, T; Fukuda, H

2017-10-01

There have been no reports evaluating progression-free survival (PFS) as a surrogate endpoint in resectable esophageal cancer. This study was conducted to evaluate the trial level correlations between PFS and overall survival (OS) in resectable esophageal cancer with preoperative therapy and to explore the potential benefit of PFS as a surrogate endpoint for OS. A systematic literature search of randomized trials with preoperative chemotherapy or preoperative chemoradiotherapy for esophageal cancer reported from January 1990 to September 2014 was conducted using PubMed and the Cochrane Library. Weighted linear regression using sample size of each trial as a weight was used to estimate coefficient of determination (R 2 ) within PFS and OS. The primary analysis included trials in which the HR for both PFS and OS was reported. The sensitivity analysis included trials in which either HR or median survival time of PFS and OS was reported. In the sensitivity analysis, HR was estimated from the median survival time of PFS and OS, assuming exponential distribution. Of 614 articles, 10 trials were selected for the primary analysis and 15 for the sensitivity analysis. The primary analysis did not show a correlation between treatment effects on PFS and OS (R 2 0.283, 95% CI [0.00-0.90]). The sensitivity analysis did not show an association between PFS and OS (R 2 0.084, 95% CI [0.00-0.70]). Although the number of randomized controlled trials evaluating preoperative therapy for esophageal cancer is limited at the moment, PFS is not suitable for primary endpoint as a surrogate endpoint for OS. Copyright © 2017 Elsevier Ltd, BASO ~ The Association for Cancer Surgery, and the European Society of Surgical Oncology. All rights reserved.

Survival Analysis and its Associated Factors of Beta Thalassemia Major in Hamadan Province

Directory of Open Access Journals (Sweden)

Reza Zamani

2015-05-01

Full Text Available Background: There currently is a lack of knowledge about the long-term survival of patients with beta thalassemia (BT, particularly in regions with low incidence of the disease. The aim of the present study was to determine the survival rate of the patients with BT major and the factors associated with the survival time. Methods: This retrospective cohort study was performed in Hamadan province, located in the west of Iran. The study included patients that referred to the provincial hospitals during 16 year period from 1997 to 2013. The follow up of each subject was calculated from the date of birth to the date of death. Demographic and clinical data were extracted from patients’ medical records using a checklist. Statistical analysis included the Kaplan-Meier method to analyze survivals, log-rank to compare curves between groups, and Cox regression for multivariate prognostic analysis. Results: A total of 133 patients with BT major were enrolled, 54.9% of whom were male and 66.2% were urban. The 10-, 20- and 30-year survival rate for all patients were 98.3%, 88.4% and 80.5%, respectively. Based on hazard ratio (HR, we found that accompanied diseases (P=0.01, blood type (P=0.03 and residency status (P=0.01 were significant predictors for the survival time of patients. Conclusion: The survival rate of BT patients has improved. Future researches such as prospective designs are required for the estimation of survival rate and to find other prognostic factors, which have reliable sources of data.

Development of a likelihood of survival scoring system for hospitalized equine neonates using generalized boosted regression modeling.

Directory of Open Access Journals (Sweden)

Katarzyna A Dembek

Full Text Available BACKGROUND: Medical management of critically ill equine neonates (foals can be expensive and labor intensive. Predicting the odds of foal survival using clinical information could facilitate the decision-making process for owners and clinicians. Numerous prognostic indicators and mathematical models to predict outcome in foals have been published; however, a validated scoring method to predict survival in sick foals has not been reported. The goal of this study was to develop and validate a scoring system that can be used by clinicians to predict likelihood of survival of equine neonates based on clinical data obtained on admission. METHODS AND RESULTS: Data from 339 hospitalized foals of less than four days of age admitted to three equine hospitals were included to develop the model. Thirty seven variables including historical information, physical examination and laboratory findings were analyzed by generalized boosted regression modeling (GBM to determine which ones would be included in the survival score. Of these, six variables were retained in the final model. The weight for each variable was calculated using a generalized linear model and the probability of survival for each total score was determined. The highest (7 and the lowest (0 scores represented 97% and 3% probability of survival, respectively. Accuracy of this survival score was validated in a prospective study on data from 283 hospitalized foals from the same three hospitals. Sensitivity, specificity, positive and negative predictive values for the survival score in the prospective population were 96%, 71%, 91%, and 85%, respectively. CONCLUSIONS: The survival score developed in our study was validated in a large number of foals with a wide range of diseases and can be easily implemented using data available in most equine hospitals. GBM was a useful tool to develop the survival score. Further evaluations of this scoring system in field conditions are needed.

Regression analysis of censored data using pseudo-observations

DEFF Research Database (Denmark)

Parner, Erik T.; Andersen, Per Kragh

2010-01-01

We draw upon a series of articles in which a method based on pseu- dovalues is proposed for direct regression modeling of the survival function, the restricted mean, and the cumulative incidence function in competing risks with right-censored data. The models, once the pseudovalues have been...... computed, can be fit using standard generalized estimating equation software. Here we present Stata procedures for computing these pseudo-observations. An example from a bone marrow transplantation study is used to illustrate the method....

Common pitfalls in statistical analysis: Linear regression analysis

Directory of Open Access Journals (Sweden)

Rakesh Aggarwal

2017-01-01

Full Text Available In a previous article in this series, we explained correlation analysis which describes the strength of relationship between two continuous variables. In this article, we deal with linear regression analysis which predicts the value of one continuous variable from another. We also discuss the assumptions and pitfalls associated with this analysis.

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.

Two Paradoxes in Linear Regression Analysis

Science.gov (United States)

FENG, Ge; PENG, Jing; TU, Dongke; ZHENG, Julia Z.; FENG, Changyong

2016-01-01

Summary Regression is one of the favorite tools in applied statistics. However, misuse and misinterpretation of results from regression analysis are common in biomedical research. In this paper we use statistical theory and simulation studies to clarify some paradoxes around this popular statistical method. In particular, we show that a widely used model selection procedure employed in many publications in top medical journals is wrong. Formal procedures based on solid statistical theory should be used in model selection. PMID:28638214

Using Dominance Analysis to Determine Predictor Importance in Logistic Regression

Science.gov (United States)

Azen, Razia; Traxel, Nicole

2009-01-01

This article proposes an extension of dominance analysis that allows researchers to determine the relative importance of predictors in logistic regression models. Criteria for choosing logistic regression R[superscript 2] analogues were determined and measures were selected that can be used to perform dominance analysis in logistic regression. A…

Linear regression and sensitivity analysis in nuclear reactor design

International Nuclear Information System (INIS)

Kumar, Akansha; Tsvetkov, Pavel V.; McClarren, Ryan G.

2015-01-01

Highlights: • Presented a benchmark for the applicability of linear regression to complex systems. • Applied linear regression to a nuclear reactor power system. • Performed neutronics, thermal–hydraulics, and energy conversion using Brayton’s cycle for the design of a GCFBR. • Performed detailed sensitivity analysis to a set of parameters in a nuclear reactor power system. • Modeled and developed reactor design using MCNP, regression using R, and thermal–hydraulics in Java. - Abstract: The paper presents a general strategy applicable for sensitivity analysis (SA), and uncertainity quantification analysis (UA) of parameters related to a nuclear reactor design. This work also validates the use of linear regression (LR) for predictive analysis in a nuclear reactor design. The analysis helps to determine the parameters on which a LR model can be fit for predictive analysis. For those parameters, a regression surface is created based on trial data and predictions are made using this surface. A general strategy of SA to determine and identify the influential parameters those affect the operation of the reactor is mentioned. Identification of design parameters and validation of linearity assumption for the application of LR of reactor design based on a set of tests is performed. The testing methods used to determine the behavior of the parameters can be used as a general strategy for UA, and SA of nuclear reactor models, and thermal hydraulics calculations. A design of a gas cooled fast breeder reactor (GCFBR), with thermal–hydraulics, and energy transfer has been used for the demonstration of this method. MCNP6 is used to simulate the GCFBR design, and perform the necessary criticality calculations. Java is used to build and run input samples, and to extract data from the output files of MCNP6, and R is used to perform regression analysis and other multivariate variance, and analysis of the collinearity of data

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...

Design and analysis of experiments classical and regression approaches with SAS

CERN Document Server

Onyiah, Leonard C

2008-01-01

Introductory Statistical Inference and Regression Analysis Elementary Statistical Inference Regression Analysis Experiments, the Completely Randomized Design (CRD)-Classical and Regression Approaches Experiments Experiments to Compare Treatments Some Basic Ideas Requirements of a Good Experiment One-Way Experimental Layout or the CRD: Design and Analysis Analysis of Experimental Data (Fixed Effects Model) Expected Values for the Sums of Squares The Analysis of Variance (ANOVA) Table Follow-Up Analysis to Check fo

Sparse Regression by Projection and Sparse Discriminant Analysis

KAUST Repository

Qi, Xin

2015-04-03

© 2015, © American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America. Recent years have seen active developments of various penalized regression methods, such as LASSO and elastic net, to analyze high-dimensional data. In these approaches, the direction and length of the regression coefficients are determined simultaneously. Due to the introduction of penalties, the length of the estimates can be far from being optimal for accurate predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high-dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths and the tuning parameters are determined by a cross-validation procedure to achieve the largest prediction accuracy. We provide a theoretical result for simultaneous model selection consistency and parameter estimation consistency of our method in high dimension. This new framework is then generalized such that it can be applied to principal components analysis, partial least squares, and canonical correlation analysis. We also adapt this framework for discriminant analysis. Compared with the existing methods, where there is relatively little control of the dependency among the sparse components, our method can control the relationships among the components. We present efficient algorithms and related theory for solving the sparse regression by projection problem. Based on extensive simulations and real data analysis, we demonstrate that our method achieves good predictive performance and variable selection in the regression setting, and the ability to control relationships between the sparse components leads to more accurate classification. In supplementary materials available online, the details of the algorithms and theoretical proofs, and R codes for all simulation studies are provided.

The Use of Nonparametric Kernel Regression Methods in Econometric Production Analysis

DEFF Research Database (Denmark)

Czekaj, Tomasz Gerard

and nonparametric estimations of production functions in order to evaluate the optimal firm size. The second paper discusses the use of parametric and nonparametric regression methods to estimate panel data regression models. The third paper analyses production risk, price uncertainty, and farmers' risk preferences...... within a nonparametric panel data regression framework. The fourth paper analyses the technical efficiency of dairy farms with environmental output using nonparametric kernel regression in a semiparametric stochastic frontier analysis. The results provided in this PhD thesis show that nonparametric......This PhD thesis addresses one of the fundamental problems in applied econometric analysis, namely the econometric estimation of regression functions. The conventional approach to regression analysis is the parametric approach, which requires the researcher to specify the form of the regression...

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...

Simulation Experiments in Practice: Statistical Design and Regression Analysis

OpenAIRE

Kleijnen, J.P.C.

2007-01-01

In practice, simulation analysts often change only one factor at a time, and use graphical analysis of the resulting Input/Output (I/O) data. The goal of this article is to change these traditional, naïve methods of design and analysis, because statistical theory proves that more information is obtained when applying Design Of Experiments (DOE) and linear regression analysis. Unfortunately, classic DOE and regression analysis assume a single simulation response that is normally and independen...

Survival analysis and classification methods for forest fire size.

Science.gov (United States)

Tremblay, Pier-Olivier; Duchesne, Thierry; Cumming, Steven G

2018-01-01

Factors affecting wildland-fire size distribution include weather, fuels, and fire suppression activities. We present a novel application of survival analysis to quantify the effects of these factors on a sample of sizes of lightning-caused fires from Alberta, Canada. Two events were observed for each fire: the size at initial assessment (by the first fire fighters to arrive at the scene) and the size at "being held" (a state when no further increase in size is expected). We developed a statistical classifier to try to predict cases where there will be a growth in fire size (i.e., the size at "being held" exceeds the size at initial assessment). Logistic regression was preferred over two alternative classifiers, with covariates consistent with similar past analyses. We conducted survival analysis on the group of fires exhibiting a size increase. A screening process selected three covariates: an index of fire weather at the day the fire started, the fuel type burning at initial assessment, and a factor for the type and capabilities of the method of initial attack. The Cox proportional hazards model performed better than three accelerated failure time alternatives. Both fire weather and fuel type were highly significant, with effects consistent with known fire behaviour. The effects of initial attack method were not statistically significant, but did suggest a reverse causality that could arise if fire management agencies were to dispatch resources based on a-priori assessment of fire growth potentials. We discuss how a more sophisticated analysis of larger data sets could produce unbiased estimates of fire suppression effect under such circumstances.

General Nature of Multicollinearity in Multiple Regression Analysis.

Science.gov (United States)

Liu, Richard

1981-01-01

Discusses multiple regression, a very popular statistical technique in the field of education. One of the basic assumptions in regression analysis requires that independent variables in the equation should not be highly correlated. The problem of multicollinearity and some of the solutions to it are discussed. (Author)

Quantifying discrimination of Framingham risk functions with different survival C statistics.

Science.gov (United States)

Pencina, Michael J; D'Agostino, Ralph B; Song, Linye

2012-07-10

Cardiovascular risk prediction functions offer an important diagnostic tool for clinicians and patients themselves. They are usually constructed with the use of parametric or semi-parametric survival regression models. It is essential to be able to evaluate the performance of these models, preferably with summaries that offer natural and intuitive interpretations. The concept of discrimination, popular in the logistic regression context, has been extended to survival analysis. However, the extension is not unique. In this paper, we define discrimination in survival analysis as the model's ability to separate those with longer event-free survival from those with shorter event-free survival within some time horizon of interest. This definition remains consistent with that used in logistic regression, in the sense that it assesses how well the model-based predictions match the observed data. Practical and conceptual examples and numerical simulations are employed to examine four C statistics proposed in the literature to evaluate the performance of survival models. We observe that they differ in the numerical values and aspects of discrimination that they capture. We conclude that the index proposed by Harrell is the most appropriate to capture discrimination described by the above definition. We suggest researchers report which C statistic they are using, provide a rationale for their selection, and be aware that comparing different indices across studies may not be meaningful. Copyright © 2012 John Wiley & Sons, Ltd.

On logistic regression analysis of dichotomized responses.

Science.gov (United States)

Lu, Kaifeng

2017-01-01

We study the properties of treatment effect estimate in terms of odds ratio at the study end point from logistic regression model adjusting for the baseline value when the underlying continuous repeated measurements follow a multivariate normal distribution. Compared with the analysis that does not adjust for the baseline value, the adjusted analysis produces a larger treatment effect as well as a larger standard error. However, the increase in standard error is more than offset by the increase in treatment effect so that the adjusted analysis is more powerful than the unadjusted analysis for detecting the treatment effect. On the other hand, the true adjusted odds ratio implied by the normal distribution of the underlying continuous variable is a function of the baseline value and hence is unlikely to be able to be adequately represented by a single value of adjusted odds ratio from the logistic regression model. In contrast, the risk difference function derived from the logistic regression model provides a reasonable approximation to the true risk difference function implied by the normal distribution of the underlying continuous variable over the range of the baseline distribution. We show that different metrics of treatment effect have similar statistical power when evaluated at the baseline mean. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

On two flexible methods of 2-dimensional regression analysis

Czech Academy of Sciences Publication Activity Database

Volf, Petr

2012-01-01

Roč. 18, č. 4 (2012), s. 154-164 ISSN 1803-9782 Grant - others:GA ČR(CZ) GAP209/10/2045 Institutional support: RVO:67985556 Keywords : regression analysis * Gordon surface * prediction error * projection pursuit Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2013/SI/volf-on two flexible methods of 2-dimensional regression analysis.pdf

Understanding survival analysis: Kaplan-Meier estimate.

Science.gov (United States)

Goel, Manish Kumar; Khanna, Pardeep; Kishore, Jugal

2010-10-01

Kaplan-Meier estimate is one of the best options to be used to measure the fraction of subjects living for a certain amount of time after treatment. In clinical trials or community trials, the effect of an intervention is assessed by measuring the number of subjects survived or saved after that intervention over a period of time. The time starting from a defined point to the occurrence of a given event, for example death is called as survival time and the analysis of group data as survival analysis. This can be affected by subjects under study that are uncooperative and refused to be remained in the study or when some of the subjects may not experience the event or death before the end of the study, although they would have experienced or died if observation continued, or we lose touch with them midway in the study. We label these situations as censored observations. The Kaplan-Meier estimate is the simplest way of computing the survival over time in spite of all these difficulties associated with subjects or situations. The survival curve can be created assuming various situations. It involves computing of probabilities of occurrence of event at a certain point of time and multiplying these successive probabilities by any earlier computed probabilities to get the final estimate. This can be calculated for two groups of subjects and also their statistical difference in the survivals. This can be used in Ayurveda research when they are comparing two drugs and looking for survival of subjects.

Bayesian Analysis for Dynamic Generalized Linear Latent Model with Application to Tree Survival Rate

Directory of Open Access Journals (Sweden)

Yu-sheng Cheng

2014-01-01

Full Text Available Logistic regression model is the most popular regression technique, available for modeling categorical data especially for dichotomous variables. Classic logistic regression model is typically used to interpret relationship between response variables and explanatory variables. However, in real applications, most data sets are collected in follow-up, which leads to the temporal correlation among the data. In order to characterize the different variables correlations, a new method about the latent variables is introduced in this study. At the same time, the latent variables about AR (1 model are used to depict time dependence. In the framework of Bayesian analysis, parameters estimates and statistical inferences are carried out via Gibbs sampler with Metropolis-Hastings (MH algorithm. Model comparison, based on the Bayes factor, and forecasting/smoothing of the survival rate of the tree are established. A simulation study is conducted to assess the performance of the proposed method and a pika data set is analyzed to illustrate the real application. Since Bayes factor approaches vary significantly, efficiency tests have been performed in order to decide which solution provides a better tool for the analysis of real relational data sets.

Physical activity increases survival after heart valve surgery

DEFF Research Database (Denmark)

Lund, K.; Sibilitz, Kirstine Lærum; Kikkenborg Berg, Selina

2016-01-01

physical activity levels 6-12 months after heart valve surgery and (1) survival, (2) hospital readmission 18-24 months after surgery and (3) participation in exercise-based cardiac rehabilitation. METHODS: Prospective cohort study with registry data from The CopenHeart survey, The Danish National Patient......OBJECTIVES: Increased physical activity predicts survival and reduces risk of readmission in patients with coronary heart disease. However, few data show how physical activity is associated with survival and readmission after heart valve surgery. Objective were to assess the association between...... Register and The Danish Civil Registration System of 742 eligible patients. Physical activity was quantified with the International Physical Activity Questionnaire and analysed using Kaplan-Meier analysis and Cox regression and logistic regression methods. RESULTS: Patients with a moderate to high physical...

Prognostic factorsin inoperable adenocarcinoma of the lung: A multivariate regression analysis of 259 patiens

DEFF Research Database (Denmark)

Sørensen, Jens Benn; Badsberg, Jens Henrik; Olsen, Jens

1989-01-01

The prognostic factors for survival in advanced adenocarcinoma of the lung were investigated in a consecutive series of 259 patients treated with chemotherapy. Twenty-eight pretreatment variables were investigated by use of Cox's multivariate regression model, including histological subtypes and ...

Marginal regression analysis of recurrent events with coarsened censoring times.

Science.gov (United States)

Hu, X Joan; Rosychuk, Rhonda J

2016-12-01

Motivated by an ongoing pediatric mental health care (PMHC) study, this article presents weakly structured methods for analyzing doubly censored recurrent event data where only coarsened information on censoring is available. The study extracted administrative records of emergency department visits from provincial health administrative databases. The available information of each individual subject is limited to a subject-specific time window determined up to concealed data. To evaluate time-dependent effect of exposures, we adapt the local linear estimation with right censored survival times under the Cox regression model with time-varying coefficients (cf. Cai and Sun, Scandinavian Journal of Statistics 2003, 30, 93-111). We establish the pointwise consistency and asymptotic normality of the regression parameter estimator, and examine its performance by simulation. The PMHC study illustrates the proposed approach throughout the article. © 2016, The International Biometric Society.

Comparison of a radiomic biomarker with volumetric analysis for decoding tumour phenotypes of lung adenocarcinoma with different disease-specific survival

International Nuclear Information System (INIS)

Yuan, Mei; Zhang, Yu-Dong; Pu, Xue-Hui; Zhong, Yan; Yu, Tong-Fu; Li, Hai; Wu, Jiang-Fen

2017-01-01

To compare a multi-feature-based radiomic biomarker with volumetric analysis in discriminating lung adenocarcinomas with different disease-specific survival on computed tomography (CT) scans. This retrospective study obtained institutional review board approval and was Health Insurance Portability and Accountability Act (HIPAA) compliant. Pathologically confirmed lung adenocarcinoma (n = 431) manifested as subsolid nodules on CT were identified. Volume and percentage solid volume were measured by using a computer-assisted segmentation method. Radiomic features quantifying intensity, texture and wavelet were extracted from the segmented volume of interest (VOI). Twenty best features were chosen by using the Relief method and subsequently fed to a support vector machine (SVM) for discriminating adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma (IAC). Performance of the radiomic signatures was compared with volumetric analysis via receiver-operating curve (ROC) analysis and logistic regression analysis. The accuracy of proposed radiomic signatures for predicting AIS/MIA from IAC achieved 80.5% with ROC analysis (Az value, 0.829; sensitivity, 72.1%; specificity, 80.9%), which showed significantly higher accuracy than volumetric analysis (69.5%, P = 0.049). Regression analysis showed that radiomic signatures had superior prognostic performance to volumetric analysis, with AIC values of 81.2% versus 70.8%, respectively. The radiomic tumour-phenotypes biomarker exhibited better diagnostic accuracy than traditional volumetric analysis in discriminating lung adenocarcinoma with different disease-specific survival. (orig.)

Comparison of a radiomic biomarker with volumetric analysis for decoding tumour phenotypes of lung adenocarcinoma with different disease-specific survival

Energy Technology Data Exchange (ETDEWEB)

Yuan, Mei; Zhang, Yu-Dong; Pu, Xue-Hui; Zhong, Yan; Yu, Tong-Fu [First Affiliated Hospital of Nanjing Medical University, Department of Radiology, Nanjing, Jiangsu Province (China); Li, Hai [First Affiliated Hospital of Nanjing Medical University, Department of Pathology, Nanjing (China); Wu, Jiang-Fen [GE Healthcare, Shanghai (China)

2017-11-15

To compare a multi-feature-based radiomic biomarker with volumetric analysis in discriminating lung adenocarcinomas with different disease-specific survival on computed tomography (CT) scans. This retrospective study obtained institutional review board approval and was Health Insurance Portability and Accountability Act (HIPAA) compliant. Pathologically confirmed lung adenocarcinoma (n = 431) manifested as subsolid nodules on CT were identified. Volume and percentage solid volume were measured by using a computer-assisted segmentation method. Radiomic features quantifying intensity, texture and wavelet were extracted from the segmented volume of interest (VOI). Twenty best features were chosen by using the Relief method and subsequently fed to a support vector machine (SVM) for discriminating adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma (IAC). Performance of the radiomic signatures was compared with volumetric analysis via receiver-operating curve (ROC) analysis and logistic regression analysis. The accuracy of proposed radiomic signatures for predicting AIS/MIA from IAC achieved 80.5% with ROC analysis (Az value, 0.829; sensitivity, 72.1%; specificity, 80.9%), which showed significantly higher accuracy than volumetric analysis (69.5%, P = 0.049). Regression analysis showed that radiomic signatures had superior prognostic performance to volumetric analysis, with AIC values of 81.2% versus 70.8%, respectively. The radiomic tumour-phenotypes biomarker exhibited better diagnostic accuracy than traditional volumetric analysis in discriminating lung adenocarcinoma with different disease-specific survival. (orig.)

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.

Development of a User Interface for a Regression Analysis Software Tool

Science.gov (United States)

Ulbrich, Norbert Manfred; Volden, Thomas R.

2010-01-01

An easy-to -use user interface was implemented in a highly automated regression analysis tool. The user interface was developed from the start to run on computers that use the Windows, Macintosh, Linux, or UNIX operating system. Many user interface features were specifically designed such that a novice or inexperienced user can apply the regression analysis tool with confidence. Therefore, the user interface s design minimizes interactive input from the user. In addition, reasonable default combinations are assigned to those analysis settings that influence the outcome of the regression analysis. These default combinations will lead to a successful regression analysis result for most experimental data sets. The user interface comes in two versions. The text user interface version is used for the ongoing development of the regression analysis tool. The official release of the regression analysis tool, on the other hand, has a graphical user interface that is more efficient to use. This graphical user interface displays all input file names, output file names, and analysis settings for a specific software application mode on a single screen which makes it easier to generate reliable analysis results and to perform input parameter studies. An object-oriented approach was used for the development of the graphical user interface. This choice keeps future software maintenance costs to a reasonable limit. Examples of both the text user interface and graphical user interface are discussed in order to illustrate the user interface s overall design approach.

Survival analysis and classification methods for forest fire size

Science.gov (United States)

2018-01-01

Factors affecting wildland-fire size distribution include weather, fuels, and fire suppression activities. We present a novel application of survival analysis to quantify the effects of these factors on a sample of sizes of lightning-caused fires from Alberta, Canada. Two events were observed for each fire: the size at initial assessment (by the first fire fighters to arrive at the scene) and the size at “being held” (a state when no further increase in size is expected). We developed a statistical classifier to try to predict cases where there will be a growth in fire size (i.e., the size at “being held” exceeds the size at initial assessment). Logistic regression was preferred over two alternative classifiers, with covariates consistent with similar past analyses. We conducted survival analysis on the group of fires exhibiting a size increase. A screening process selected three covariates: an index of fire weather at the day the fire started, the fuel type burning at initial assessment, and a factor for the type and capabilities of the method of initial attack. The Cox proportional hazards model performed better than three accelerated failure time alternatives. Both fire weather and fuel type were highly significant, with effects consistent with known fire behaviour. The effects of initial attack method were not statistically significant, but did suggest a reverse causality that could arise if fire management agencies were to dispatch resources based on a-priori assessment of fire growth potentials. We discuss how a more sophisticated analysis of larger data sets could produce unbiased estimates of fire suppression effect under such circumstances. PMID:29320497

Effect of donor ethnicity on kidney survival in different recipient pairs: an analysis of the OPTN/UNOS database.

Science.gov (United States)

Callender, C O; Cherikh, W S; Traverso, P; Hernandez, A; Oyetunji, T; Chang, D

2009-12-01

Previous multivariate analysis performed between April 1, 1994, and December 31, 2000 from the Organ Procurement Transplant Network/United Network for Organ Sharing (OPTN/UNOS) database has shown that kidneys from black donors were associated with lower graft survival. We compared graft and patient survival of different kidney donor-to-recipient ethnic combinations to see if this result still holds on a recent cohort of US kidney transplants. We included 72,495 recipients of deceased and living donor kidney alone transplants from 2001 to 2005. A multivariate Cox regression method was used to analyze the effect of donor-recipient ethnicity on graft and patient survival within 5 years of transplant, and to adjust for the effect of other donor, recipient, and transplant characteristics. Results are presented as hazard ratios (HR) with the 95% confidence limit (CL) and P values. Adjusted HRs of donor-recipient patient survival were: white to white (1); and white to black (1.22; P = .001). Graft survival HRs were black to black (1.40; P recipients. The graft and patient survival rates for Asian and Latino/Hispanic recipients, however, were not affected by donor ethnicity. This analysis underscores the need for research to better understand the reasons for these disparities and how to improve the posttransplant graft survival rates of black kidney recipients.

Method for nonlinear exponential regression analysis

Science.gov (United States)

Junkin, B. G.

1972-01-01

Two computer programs developed according to two general types of exponential models for conducting nonlinear exponential regression analysis are described. Least squares procedure is used in which the nonlinear problem is linearized by expanding in a Taylor series. Program is written in FORTRAN 5 for the Univac 1108 computer.

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

CASAS: Cancer Survival Analysis Suite, a web based application.

Science.gov (United States)

Rupji, Manali; Zhang, Xinyan; Kowalski, Jeanne

2017-01-01

We present CASAS, a shiny R based tool for interactive survival analysis and visualization of results. The tool provides a web-based one stop shop to perform the following types of survival analysis:  quantile, landmark and competing risks, in addition to standard survival analysis.  The interface makes it easy to perform such survival analyses and obtain results using the interactive Kaplan-Meier and cumulative incidence plots.  Univariate analysis can be performed on one or several user specified variable(s) simultaneously, the results of which are displayed in a single table that includes log rank p-values and hazard ratios along with their significance. For several quantile survival analyses from multiple cancer types, a single summary grid is constructed. The CASAS package has been implemented in R and is available via http://shinygispa.winship.emory.edu/CASAS/. The developmental repository is available at https://github.com/manalirupji/CASAS/.

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.

Body mass index and breast cancer survival

DEFF Research Database (Denmark)

Guo, Qi; Burgess, Stephen; Turman, Constance

2017-01-01

Background: There is increasing evidence that elevated body mass index (BMI) is associated with reduced survival for women with breast cancer. However, the underlying reasons remain unclear. We conducted a Mendelian randomization analysis to investigate a possible causal role of BMI in survival...... from breast cancer. Methods: We used individual-level data from six large breast cancer case-cohorts including a total of 36 210 individuals (2475 events) of European ancestry. We created a BMI genetic risk score (GRS) based on genotypes at 94 known BMI-associated genetic variants. Association between...... the BMI genetic score and breast cancer survival was analysed by Cox regression for each study separately. Study-specific hazard ratios were pooled using fixed-effect meta-analysis. Results: BMI genetic score was found to be associated with reduced breast cancer-specific survival for estrogen receptor (ER...

An Analysis of Bank Service Satisfaction Based on Quantile Regression and Grey Relational Analysis

Directory of Open Access Journals (Sweden)

Wen-Tsao Pan

2016-01-01

Full Text Available Bank service satisfaction is vital to the success of a bank. In this paper, we propose to use the grey relational analysis to gauge the levels of service satisfaction of the banks. With the grey relational analysis, we compared the effects of different variables on service satisfaction. We gave ranks to the banks according to their levels of service satisfaction. We further used the quantile regression model to find the variables that affected the satisfaction of a customer at a specific quantile of satisfaction level. The result of the quantile regression analysis provided a bank manager with information to formulate policies to further promote satisfaction of the customers at different quantiles of satisfaction level. We also compared the prediction accuracies of the regression models at different quantiles. The experiment result showed that, among the seven quantile regression models, the median regression model has the best performance in terms of RMSE, RTIC, and CE performance measures.

Research and analyze of physical health using multiple regression analysis

Directory of Open Access Journals (Sweden)

T. S. Kyi

2014-01-01

Full Text Available This paper represents the research which is trying to create a mathematical model of the "healthy people" using the method of regression analysis. The factors are the physical parameters of the person (such as heart rate, lung capacity, blood pressure, breath holding, weight height coefficient, flexibility of the spine, muscles of the shoulder belt, abdominal muscles, squatting, etc.., and the response variable is an indicator of physical working capacity. After performing multiple regression analysis, obtained useful multiple regression models that can predict the physical performance of boys the aged of fourteen to seventeen years. This paper represents the development of regression model for the sixteen year old boys and analyzed results.

An improved multiple linear regression and data analysis computer program package

Science.gov (United States)

Sidik, S. M.

1972-01-01

NEWRAP, an improved version of a previous multiple linear regression program called RAPIER, CREDUC, and CRSPLT, allows for a complete regression analysis including cross plots of the independent and dependent variables, correlation coefficients, regression coefficients, analysis of variance tables, t-statistics and their probability levels, rejection of independent variables, plots of residuals against the independent and dependent variables, and a canonical reduction of quadratic response functions useful in optimum seeking experimentation. A major improvement over RAPIER is that all regression calculations are done in double precision arithmetic.

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.

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.

Analysis of Relationship Between Personality and Favorite Places with Poisson Regression Analysis

Directory of Open Access Journals (Sweden)

Yoon Song Ha

2018-01-01

Full Text Available A relationship between human personality and preferred locations have been a long conjecture for human mobility research. In this paper, we analyzed the relationship between personality and visiting place with Poisson Regression. Poisson Regression can analyze correlation between countable dependent variable and independent variable. For this analysis, 33 volunteers provided their personality data and 49 location categories data are used. Raw location data is preprocessed to be normalized into rates of visit and outlier data is prunned. For the regression analysis, independent variables are personality data and dependent variables are preprocessed location data. Several meaningful results are found. For example, persons with high tendency of frequent visiting to university laboratory has personality with high conscientiousness and low openness. As well, other meaningful location categories are presented in this paper.

application of multilinear regression analysis in modeling of soil

African Journals Online (AJOL)

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Accordingly [1, 3] in their work, they applied linear regression ... (MLRA) is a statistical technique that uses several explanatory ... order to check this, they adopted bivariate correlation analysis .... groups, namely A-1 through A-7, based on their relative expected ..... Multivariate Regression in Gorgan Province North of Iran” ...

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.)

The survival analysis of beta thalassemia major patients in South East of Iran

International Nuclear Information System (INIS)

Roudbari, M.; Soltani-Rad, M.; Roudbari, S.

2008-01-01

The objective was to determine the survival of beta-thalassemia major patients with transfusion, and its related factors in Southeast of Iran. This cross-sectional study was performed in Zahedan, Iran in 2007. The sample included patients who were referred from all over the Zahedan Thalassemia Center from 1998 to 2006. The data were collected using the patient's records, which were recorded by the staff during transfusion. The data included demographic and medical information blood group, blood RH, the kind of transfused blood [KTB], annual number of transfusions [ANOT], accompanied disease [AD], Hemoglobin [Hb] and ferritin level. For data analysis, the Kaplan-Meyer method, and Long Rank test together with Cox Regression were used. Forty-six of 578 patients died and 99% survived for the first year. The ages survival proportions were 5 (97.9%), 10 (97%), 15 (92.1%), and 20 (81.2%) years. The survival time showed significant relationships with the ANOT p=0.0053, KTB p=0.003, Hb=0.002 and ferritin level p=0.0087, and AD p=0.00. Using regular transfusion, paying attention to screening of transfused blood, increasing the families knowledge on the disease to prevent the bearing of thalassemia fetus, are recommended; finally, the detection and treating of the AD, are of great importance to extend the lifetime of the patients. (author)

ATM and p53 combined analysis predicts survival in glioblastoma multiforme patients: A clinicopathologic study.

Science.gov (United States)

Romano, Francesco Jacopo; Guadagno, Elia; Solari, Domenico; Borrelli, Giorgio; Pignatiello, Sara; Cappabianca, Paolo; Del Basso De Caro, Marialaura

2018-06-01

Glioblastoma is one of the most malignant cancers, with a distinguishing dismal prognosis: surgery followed by chemo- and radiotherapy represents the current standard of care, and chemo- and radioresistance underlie disease recurrence and short overall survival of patients suffering from this malignancy. ATM is a kinase activated by autophosphorylation upon DNA doublestrand breaks arising from errors during replication, byproducts of metabolism, chemotherapy or ionizing radiations; TP53 is one of the most popular tumor suppressor, with a preeminent role in DNA damage response and repair. To study the effects of the immunohistochemical expression of p-ATM and p53 in glioblastoma patients, 21 cases were retrospectively examined. In normal brain tissue, p-ATM was expressed only in neurons; conversely, in tumors cells, the protein showed a variable cytoplasmic expression (score: +,++,+++), with being completely undetectable in three cases. Statistical analysis revealed that high p-ATM score (++/+++) strongly correlated to shorter survival (P = 0.022). No difference in overall survival was registered between p53 normally expressed (NE) and overexpressed (OE) glioblastoma patients (P = 0.669). Survival analysis performed on the results from combined assessment of the two proteins showed that patients with NE p53 /low pATM score had longer overall survival than the NE p53/ high pATM score counterpart. Cox-regression analysis confirmed this finding (HR = 0.025; CI 95% = 0.002-0.284; P = 0.003). Our study outlined the immunohistochemical expression of p-ATM/p53 in glioblastomas and provided data on their possible prognostic/predictive of response role. A "non-oncogene addiction" to ATM for NEp53 glioblastoma could be postulated, strengthening the rationale for development of ATM inhibiting drugs. © 2018 Wiley Periodicals, Inc.

Risk factors for pedicled flap necrosis in hand soft tissue reconstruction: a multivariate logistic regression analysis.

Science.gov (United States)

Gong, Xu; Cui, Jianli; Jiang, Ziping; Lu, Laijin; Li, Xiucun

2018-03-01

Few clinical retrospective studies have reported the risk factors of pedicled flap necrosis in hand soft tissue reconstruction. The aim of this study was to identify non-technical risk factors associated with pedicled flap perioperative necrosis in hand soft tissue reconstruction via a multivariate logistic regression analysis. For patients with hand soft tissue reconstruction, we carefully reviewed hospital records and identified 163 patients who met the inclusion criteria. The characteristics of these patients, flap transfer procedures and postoperative complications were recorded. Eleven predictors were identified. The correlations between pedicled flap necrosis and risk factors were analysed using a logistic regression model. Of 163 skin flaps, 125 flaps survived completely without any complications. The pedicled flap necrosis rate in hands was 11.04%, which included partial flap necrosis (7.36%) and total flap necrosis (3.68%). Soft tissue defects in fingers were noted in 68.10% of all cases. The logistic regression analysis indicated that the soft tissue defect site (P = 0.046, odds ratio (OR) = 0.079, confidence interval (CI) (0.006, 0.959)), flap size (P = 0.020, OR = 1.024, CI (1.004, 1.045)) and postoperative wound infection (P < 0.001, OR = 17.407, CI (3.821, 79.303)) were statistically significant risk factors for pedicled flap necrosis of the hand. Soft tissue defect site, flap size and postoperative wound infection were risk factors associated with pedicled flap necrosis in hand soft tissue defect reconstruction. © 2017 Royal Australasian College of Surgeons.

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...

Survival analysis of patients with uveal melanoma after organ preserving and liquidation treatment

Directory of Open Access Journals (Sweden)

E. E. Grishina

2018-01-01

Full Text Available Rationale: Uveal melanoma is the most common primary malignancy of the eye.Aim: To evaluate survival in patients with uveal melanoma stratified according to the type of treatment and to identify factors significantly associated with their survival.Materials and methods: The study was performed on the data extracted from medical files and follow-up forms of patients with uveal melanoma seen in the Ophthalmological Clinical Hospital of the Department of Healthcare, Moscow, from 1977 to 2012. Analysis of survival was used to assess the life longevity of patients with uveal melanoma. The analysis was censored at January 2013, when vital status (dead or alive of all patients was assessed. The factors included into the study analysis, were those taken from the follow-up forms. The incidence of uveal melanoma in Moscow (2012 was 0.9 per 100,000 of the population, whereas its prevalence was 11.1 per 100,000.Results: 698 patients with uveal melanoma were included into the study, among them 260 (37% men (aged from 19 to 87 years, median age 60 years and 438 (63% women (aged from 18 to 93 years, median age 63 years; therefore, the proportion of women under the follow-up monitoring was by 26% higher than that of men. The liquidation treatment (mostly enucleation was performed in 358 (51% of the patients, whereas the organ preserving treatment in 340 (49%. At 5, 7, and 10 years of the follow-up, the disease-specific survival of patients with uveal melanoma after the organ preserving treatment (median survival has not been reached and after the liquidation treatment (median, 88 months were 89 ± 2, 83 ± 3, and 75 ± 4% versus 63 ± 3, 52 ± 4, and 47 ± 5%, respectively (р = 0.001. Overall survival and disease-specific survival of the patients after the liquidation treatment were significantly lower than in the patients after the organ-preserving treatment. According to multiple regression analysis, this was associated not with the type of

Application of principal component regression and partial least squares regression in ultraviolet spectrum water quality detection

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Li, Jiangtong; Luo, Yongdao; Dai, Honglin

2018-01-01

Water is the source of life and the essential foundation of all life. With the development of industrialization, the phenomenon of water pollution is becoming more and more frequent, which directly affects the survival and development of human. Water quality detection is one of the necessary measures to protect water resources. Ultraviolet (UV) spectral analysis is an important research method in the field of water quality detection, which partial least squares regression (PLSR) analysis method is becoming predominant technology, however, in some special cases, PLSR's analysis produce considerable errors. In order to solve this problem, the traditional principal component regression (PCR) analysis method was improved by using the principle of PLSR in this paper. The experimental results show that for some special experimental data set, improved PCR analysis method performance is better than PLSR. The PCR and PLSR is the focus of this paper. Firstly, the principal component analysis (PCA) is performed by MATLAB to reduce the dimensionality of the spectral data; on the basis of a large number of experiments, the optimized principal component is extracted by using the principle of PLSR, which carries most of the original data information. Secondly, the linear regression analysis of the principal component is carried out with statistic package for social science (SPSS), which the coefficients and relations of principal components can be obtained. Finally, calculating a same water spectral data set by PLSR and improved PCR, analyzing and comparing two results, improved PCR and PLSR is similar for most data, but improved PCR is better than PLSR for data near the detection limit. Both PLSR and improved PCR can be used in Ultraviolet spectral analysis of water, but for data near the detection limit, improved PCR's result better than PLSR.

Outcome and histopathologic regression in oral squamous cell carcinoma after preoperative radiochemotherapy

International Nuclear Information System (INIS)

Driemel, Oliver; Ettl, Tobias; Reichert, Torsten E.; Koelbl, Oliver; Dresp, Bernd V.; Reuther, Juergen; Pistner, Hans

2009-01-01

Background and purpose: preoperative radiochemotherapy has been reported to enhance tumor response and to improve long-term survival in advanced squamous cell carcinoma of the head and neck. This retrospective study evaluates regression rate and long-term survival in 228 patients with primary oral squamous cell carcinoma treated by neoadjuvant radiochemotherapy and radical surgery. Patients and methods: all patients with biopsy-proven, resectable oral squamous cell carcinoma - TNM stages II-IV without distant metastasis - received preoperative treatment consisting of fractioned irradiation of the primary and the regional lymph nodes with a total dose of 40 Gy and additional cisplatin (n = 160) or carboplatin (n = 68) during the 1st week of treatment. Radical surgery and neck dissection followed after a delay of 10-14 days. The study only included cases with histologically negative resection margins. Results: after a median follow-up of 5.2 years, 53 patients (23.2%) had experienced local-regional recurrence. The median 2-year disease-specific survival (DSS) rate was 86.2%. 5-year DSS and 10-year DSS were 76.3% and 66.7%, respectively. Complete histological local tumor regression after surgery (ypTO) was observed in 50 patients (21.9%) and was independent of pretreatment tumor classification. Uni- and multivariate survival analysis revealed that ypT- and ypN-stage were the most decisive predictors for DSS. Conclusion: preoperative radiochemotherapy with cisplatin/carboplatin followed by radical surgery attains favorable long-term survival rates. This applies especially to cases with complete histological tumor regression after radiochemotherapy, which can be assumed for one of five patients. (orig.)

Background stratified Poisson regression analysis of cohort data.

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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.

Atrial fibrillation and survival in colorectal cancer

Directory of Open Access Journals (Sweden)

Justin Timothy A

2004-11-01

Full Text Available Abstract Background Survival in colorectal cancer may correlate with the degree of systemic inflammatory response to the tumour. Atrial fibrillation may be regarded as an inflammatory complication. We aimed to determine if atrial fibrillation is a prognostic factor in colorectal cancer. Patients and methods A prospective colorectal cancer patient database was cross-referenced with the hospital clinical-coding database to identify patients who had underwent colorectal cancer surgery and were in atrial fibrillation pre- or postoperatively. Results A total of 175 patients underwent surgery for colorectal cancer over a two-year period. Of these, 13 patients had atrial fibrillation pre- or postoperatively. Atrial fibrillation correlated with worse two-year survival (p = 0.04; log-rank test. However, in a Cox regression analysis, atrial fibrillation was not significantly associated with survival. Conclusion The presence or development of atrial fibrillation in patients undergoing surgery for colorectal cancer is associated with worse overall survival, however it was not found to be an independent factor in multivariate analysis.

Retrospective Analysis of the Survival Benefit of Induction Chemotherapy in Stage IVa-b Nasopharyngeal Carcinoma.

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Lan, Xiao-Wen; Zou, Xue-Bin; Xiao, Yao; Tang, Jie; OuYang, Pu-Yun; Su, Zhen; Xie, Fang-Yun

2016-01-01

The value of adding induction chemotherapy to chemoradiotherapy in locoregionally advanced nasopharyngeal carcinoma (LA-NPC) remains controversial, yet high-risk patients with LA-NPC have poor outcomes after chemoradiotherapy. We aimed to assess the survival benefits of induction chemotherapy in stage IVa-b NPC. A total of 602 patients with stage IVa-b NPC treated with intensity-modulated radiation therapy (IMRT) and concurrent chemotherapy with or without induction chemotherapy were retrospectively analyzed. Overall survival (OS), locoregional relapse-free survival (LRFS), distant metastasis-free survival (DMFS) and progression-free survival (PFS) were evaluated using the Kaplan-Meier method, log-rank test and Cox regression analysis. In univariate analysis, 5-year OS was 83.2% for induction chemotherapy plus concurrent chemotherapy and 74.8% for concurrent chemotherapy alone, corresponding to an absolute risk reduction of 8.4% (P = 0.022). Compared to concurrent chemotherapy alone, addition of induction chemotherapy improved 5-year DMFS (83.2% vs. 74.4%, P = 0.018) but not 5-year LRFS (83.7% vs. 83.0%, P = 0.848) or PFS (71.9% vs. 66.0%, P = 0.12). Age, T category, N category, chemotherapy strategy and clinical stage were associated with 5-year OS (P = 0.017, P = 0.031, P = 0.007, P = 0.022, P = 0.001, respectively). In multivariate analysis, induction chemotherapy plus concurrent chemotherapy was an independent favorable prognostic factor for OS (HR, 0.62; 95% CI, 0.43-0.90, P = 0.012) and DMFS (HR, 0.57; 95% CI, 0.38-0.83, P = 0.004). In subgroup analysis, induction chemotherapy significantly improved 5-year DMFS in stage IVa (86.8% vs. 77.3%, P = 0.008), but provided no significant benefit in stage IVb. In patients with stage IVa-b NPC treated with IMRT, addition of induction chemotherapy to concurrent chemotherapy significantly improved 5-year OS and 5-year DMFS. This study provides a basis for selection of high risk patients in future clinical therapeutic

Mathematical Methods in Survival Analysis, Reliability and Quality of Life

CERN Document Server

Huber, Catherine; Mesbah, Mounir

2008-01-01

Reliability and survival analysis are important applications of stochastic mathematics (probability, statistics and stochastic processes) that are usually covered separately in spite of the similarity of the involved mathematical theory. This title aims to redress this situation: it includes 21 chapters divided into four parts: Survival analysis, Reliability, Quality of life, and Related topics. Many of these chapters were presented at the European Seminar on Mathematical Methods for Survival Analysis, Reliability and Quality of Life in 2006.

Poisson Regression Analysis of Illness and Injury Surveillance Data

Energy Technology Data Exchange (ETDEWEB)

Frome E.L., Watkins J.P., Ellis E.D.

2012-12-12

The Department of Energy (DOE) uses illness and injury surveillance to monitor morbidity and assess the overall health of the work force. Data collected from each participating site include health events and a roster file with demographic information. The source data files are maintained in a relational data base, and are used to obtain stratified tables of health event counts and person time at risk that serve as the starting point for Poisson regression analysis. The explanatory variables that define these tables are age, gender, occupational group, and time. Typical response variables of interest are the number of absences due to illness or injury, i.e., the response variable is a count. Poisson regression methods are used to describe the effect of the explanatory variables on the health event rates using a log-linear main effects model. Results of fitting the main effects model are summarized in a tabular and graphical form and interpretation of model parameters is provided. An analysis of deviance table is used to evaluate the importance of each of the explanatory variables on the event rate of interest and to determine if interaction terms should be considered in the analysis. Although Poisson regression methods are widely used in the analysis of count data, there are situations in which over-dispersion occurs. This could be due to lack-of-fit of the regression model, extra-Poisson variation, or both. A score test statistic and regression diagnostics are used to identify over-dispersion. A quasi-likelihood method of moments procedure is used to evaluate and adjust for extra-Poisson variation when necessary. Two examples are presented using respiratory disease absence rates at two DOE sites to illustrate the methods and interpretation of the results. In the first example the Poisson main effects model is adequate. In the second example the score test indicates considerable over-dispersion and a more detailed analysis attributes the over-dispersion to extra

Cox regression with missing covariate data using a modified partial likelihood method

DEFF Research Database (Denmark)

Martinussen, Torben; Holst, Klaus K.; Scheike, Thomas H.

2016-01-01

Missing covariate values is a common problem in survival analysis. In this paper we propose a novel method for the Cox regression model that is close to maximum likelihood but avoids the use of the EM-algorithm. It exploits that the observed hazard function is multiplicative in the baseline hazard...

Breast cancer data analysis for survivability studies and prediction.

Science.gov (United States)

Shukla, Nagesh; Hagenbuchner, Markus; Win, Khin Than; Yang, Jack

2018-03-01

Breast cancer is the most common cancer affecting females worldwide. Breast cancer survivability prediction is challenging and a complex research task. Existing approaches engage statistical methods or supervised machine learning to assess/predict the survival prospects of patients. The main objectives of this paper is to develop a robust data analytical model which can assist in (i) a better understanding of breast cancer survivability in presence of missing data, (ii) providing better insights into factors associated with patient survivability, and (iii) establishing cohorts of patients that share similar properties. Unsupervised data mining methods viz. the self-organising map (SOM) and density-based spatial clustering of applications with noise (DBSCAN) is used to create patient cohort clusters. These clusters, with associated patterns, were used to train multilayer perceptron (MLP) model for improved patient survivability analysis. A large dataset available from SEER program is used in this study to identify patterns associated with the survivability of breast cancer patients. Information gain was computed for the purpose of variable selection. All of these methods are data-driven and require little (if any) input from users or experts. SOM consolidated patients into cohorts of patients with similar properties. From this, DBSCAN identified and extracted nine cohorts (clusters). It is found that patients in each of the nine clusters have different survivability time. The separation of patients into clusters improved the overall survival prediction accuracy based on MLP and revealed intricate conditions that affect the accuracy of a prediction. A new, entirely data driven approach based on unsupervised learning methods improves understanding and helps identify patterns associated with the survivability of patient. The results of the analysis can be used to segment the historical patient data into clusters or subsets, which share common variable values and

A Quality Assessment Tool for Non-Specialist Users of Regression Analysis

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Argyrous, George

2015-01-01

This paper illustrates the use of a quality assessment tool for regression analysis. It is designed for non-specialist "consumers" of evidence, such as policy makers. The tool provides a series of questions such consumers of evidence can ask to interrogate regression analysis, and is illustrated with reference to a recent study published…

Survival Outcomes in Resected Extrahepatic Cholangiocarcinoma: Effect of Adjuvant Radiotherapy in a Surveillance, Epidemiology, and End Results Analysis

International Nuclear Information System (INIS)

Vern-Gross, Tamara Z.; Shivnani, Anand T.; Chen, Ke; Lee, Christopher M.; Tward, Jonathan D.; MacDonald, O. Kenneth; Crane, Christopher H.; Talamonti, Mark S.; Munoz, Louis L.; Small, William

2011-01-01

Purpose: The benefit of adjuvant radiotherapy (RT) after surgical resection for extrahepatic cholangiocarcinoma has not been clearly established. We analyzed survival outcomes of patients with resected extrahepatic cholangiocarcinoma and examined the effect of adjuvant RT. Methods and Materials: Data were obtained from the Surveillance, Epidemiology, and End Results (SEER) program between 1973 and 2003. The primary endpoint was the overall survival time. Cox regression analysis was used to perform univariate and multivariate analyses of the following clinical variables: age, year of diagnosis, histologic grade, localized (Stage T1-T2) vs. regional (Stage T3 or greater and/or node positive) stage, gender, race, and the use of adjuvant RT after surgical resection. Results: The records for 2,332 patients were obtained. Patients with previous malignancy, distant disease, incomplete or conflicting records, atypical histologic features, and those treated with preoperative/intraoperative RT were excluded. Of the remaining 1,491 patients eligible for analysis, 473 (32%) had undergone adjuvant RT. After a median follow-up of 27 months (among surviving patients), the median overall survival time for the entire cohort was 20 months. Patients with localized and regional disease had a median survival time of 33 and 18 months, respectively (p < .001). The addition of adjuvant RT was not associated with an improvement in overall or cause-specific survival for patients with local or regional disease. Conclusion: Patients with localized disease had significantly better overall survival than those with regional disease. Adjuvant RT was not associated with an improvement in long-term overall survival in patients with resected extrahepatic bile duct cancer. Key data, including margin status and the use of combined chemotherapy, was not available through the SEER database.

Background stratified Poisson regression analysis of cohort data

International Nuclear Information System (INIS)

Richardson, David B.; Langholz, Bryan

2012-01-01

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. (orig.)

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.

[Survival analysis with competing risks: estimating failure probability].

Science.gov (United States)

Llorca, Javier; Delgado-Rodríguez, Miguel

2004-01-01

To show the impact of competing risks of death on survival analysis. We provide an example of survival time without chronic rejection after heart transplantation, where death before rejection acts as a competing risk. Using a computer simulation, we compare the Kaplan-Meier estimator and the multiple decrement model. The Kaplan-Meier method overestimated the probability of rejection. Next, we illustrate the use of the multiple decrement model to analyze secondary end points (in our example: death after rejection). Finally, we discuss Kaplan-Meier assumptions and why they fail in the presence of competing risks. Survival analysis should be adjusted for competing risks of death to avoid overestimation of the risk of rejection produced with the Kaplan-Meier method.

The survival analysis on localized prostate cancer treated with neoadjuvant endocrine therapy followed by intensity modulated radiation therapy

International Nuclear Information System (INIS)

Gao Hong; Li Gaofeng; Wu Qinhong; Li Xuenan; Zhong Qiuzi; Xu Yonggang

2010-01-01

Objective: To retrospectively investigate clinical outcomes and prognostic factors in localized prostate cancer treated with neoadjuvant endocrine therapy followed by intensity modulated radiotherapy (IMRT). Methods: Between March 2003 and October 2008, 54 localized prostate cancer treated by IMRT were recruited. All patients had received endocrine therapy before IMRT. The endocrine therapy included surgical castration or medical castration in combination with antiandrogens. The target of IMRT was the prostate and seminal vesicles with or without pelvis. The biochemical failure was defined according to the phoenix definition. By using the risk grouping standard proposed by D'Amico, patients were divided into three groups: low-risk group (n = 5), intermediate-risk group (n = 12), and high-risk group (n = 37). Kaplan-Meier method was used to calculate the overall survival rate. Prognostic factors were analyzed by univariate and multiple Cox regression analysis. Results: The follow-up rate was 98%. The number of patients under follow-up was 39 at 3 years and 25 at 5 years. Potential prognostic factors, including risk groups, mode of endocrine therapy, time of endocrine therapy, phoenix grouping before IMRT, the prostate specific antigen doubling time (PSADT) before radiotherapy, PSA value before IMRT, interval of endocrine therapy and IMRT, irradiation region, and irradiation dose were analyzed by survival analysis. In univariate analysis, time of endocrine therapy (75 % vs 95 %, χ 2 = 6. 45, P = 0. 011), phoenix grouping before IMRT (87% vs 96%, χ 2 = 4. 36, P = 0. 037), interval of endocrine therapy and IMRT (80% vs 95%, χ 2 = 11.60, P= 0. 001), irradiation dose (75% vs 91%, χ 2 =5.92, P= 0. 015) were statistically significant prognostic factors for 3 - year overall survival , and risk groups (85 vs 53 vs 29, χ 2 = 6. 40, P =0. 041) and PSADT before IMRT (62 vs 120, U =24. 50, P =0. 003) were significant factors for the median survival time. In the multiple Cox

Direct Survival Analysis: a new stock assessment method

Directory of Open Access Journals (Sweden)

Eduardo Ferrandis

2007-03-01

Full Text Available In this work, a new stock assessment method, Direct Survival Analysis, is proposed and described. The parameter estimation of the Weibull survival model proposed by Ferrandis (2007 is obtained using trawl survey data. This estimation is used to establish a baseline survival function, which is in turn used to estimate the specific survival functions in the different cohorts considered through an adaptation of the separable model of the fishing mortality rates introduced by Pope and Shepherd (1982. It is thus possible to test hypotheses on the evolution of survival during the period studied and to identify trends in recruitment. A link is established between the preceding analysis of trawl survey data and the commercial catch-at-age data that are generally obtained to evaluate the population using analytical models. The estimated baseline survival, with the proposed versions of the stock and catch equations and the adaptation of the Separable Model, may be applied to commercial catch-at-age data. This makes it possible to estimate the survival corresponding to the landing data, the initial size of the cohort and finally, an effective age of first capture, in order to complete the parameter model estimation and consequently the estimation of the whole survival and mortality, along with the reference parameters that are useful for management purposes. Alternatively, this estimation of an effective age of first capture may be obtained by adapting the demographic structure of trawl survey data to that of the commercial fleet through suitable selectivity models of the commercial gears. The complete model provides the evaluation of the stock at any age. The coherence (and hence the mutual “calibration” between the two kinds of information may be analysed and compared with results obtained by other methods, such as virtual population analysis (VPA, in order to improve the diagnosis of the state of exploitation of the population. The model may be

Neyman, Markov processes and survival analysis.

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Yang, Grace

2013-07-01

J. Neyman used stochastic processes extensively in his applied work. One example is the Fix and Neyman (F-N) competing risks model (1951) that uses finite homogeneous Markov processes to analyse clinical trials with breast cancer patients. We revisit the F-N model, and compare it with the Kaplan-Meier (K-M) formulation for right censored data. The comparison offers a way to generalize the K-M formulation to include risks of recovery and relapses in the calculation of a patient's survival probability. The generalization is to extend the F-N model to a nonhomogeneous Markov process. Closed-form solutions of the survival probability are available in special cases of the nonhomogeneous processes, like the popular multiple decrement model (including the K-M model) and Chiang's staging model, but these models do not consider recovery and relapses while the F-N model does. An analysis of sero-epidemiology current status data with recurrent events is illustrated. Fix and Neyman used Neyman's RBAN (regular best asymptotic normal) estimates for the risks, and provided a numerical example showing the importance of considering both the survival probability and the length of time of a patient living a normal life in the evaluation of clinical trials. The said extension would result in a complicated model and it is unlikely to find analytical closed-form solutions for survival analysis. With ever increasing computing power, numerical methods offer a viable way of investigating the problem.

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.

Linear regression analysis: part 14 of a series on evaluation of scientific publications.

Science.gov (United States)

Schneider, Astrid; Hommel, Gerhard; Blettner, Maria

2010-11-01

Regression analysis is an important statistical method for the analysis of medical data. It enables the identification and characterization of relationships among multiple factors. It also enables the identification of prognostically relevant risk factors and the calculation of risk scores for individual prognostication. This article is based on selected textbooks of statistics, a selective review of the literature, and our own experience. After a brief introduction of the uni- and multivariable regression models, illustrative examples are given to explain what the important considerations are before a regression analysis is performed, and how the results should be interpreted. The reader should then be able to judge whether the method has been used correctly and interpret the results appropriately. The performance and interpretation of linear regression analysis are subject to a variety of pitfalls, which are discussed here in detail. The reader is made aware of common errors of interpretation through practical examples. Both the opportunities for applying linear regression analysis and its limitations are presented.

Management of Industrial Performance Indicators: Regression Analysis and Simulation

Directory of Open Access Journals (Sweden)

Walter Roberto Hernandez Vergara

2017-11-01

Full Text Available Stochastic methods can be used in problem solving and explanation of natural phenomena through the application of statistical procedures. The article aims to associate the regression analysis and systems simulation, in order to facilitate the practical understanding of data analysis. The algorithms were developed in Microsoft Office Excel software, using statistical techniques such as regression theory, ANOVA and Cholesky Factorization, which made it possible to create models of single and multiple systems with up to five independent variables. For the analysis of these models, the Monte Carlo simulation and analysis of industrial performance indicators were used, resulting in numerical indices that aim to improve the goals’ management for compliance indicators, by identifying systems’ instability, correlation and anomalies. The analytical models presented in the survey indicated satisfactory results with numerous possibilities for industrial and academic applications, as well as the potential for deployment in new analytical techniques.

Least-Squares Linear Regression and Schrodinger's Cat: Perspectives on the Analysis of Regression Residuals.

Science.gov (United States)

Hecht, Jeffrey B.

The analysis of regression residuals and detection of outliers are discussed, with emphasis on determining how deviant an individual data point must be to be considered an outlier and the impact that multiple suspected outlier data points have on the process of outlier determination and treatment. Only bivariate (one dependent and one independent)…

Restaging and Survival Analysis of 4036 Ovarian Cancer Patients According to the 2013 FIGO Classification for Ovarian, Fallopian Tube, and Primary Peritoneal Cancer

DEFF Research Database (Denmark)

Rosendahl, Mikkel; Høgdall, Claus Kim; Mosgaard, Berit Jul

2016-01-01

OBJECTIVE: With the 2013 International Federation of Gynecology and Obstetrics (FIGO) staging for ovarian, fallopian tube, and primary peritoneal cancer, the number of substages changed from 10 to 14. Any classification of a malignancy should easily assign patients to prognostic groups, refer....... MATERIALS AND METHODS: Demographic, surgical, histological, and survival data from 4036 ovarian cancer patients were used in the analysis. Five-year survival rates (5YSR) and hazard ratios for the old and revised FIGO staging were calculated using Kaplan-Meier curves and Cox regression. RESULTS: A total...

Predicting Dropouts of University Freshmen: A Logit Regression Analysis.

Science.gov (United States)

Lam, Y. L. Jack

1984-01-01

Stepwise discriminant analysis coupled with logit regression analysis of freshmen data from Brandon University (Manitoba) indicated that six tested variables drawn from research on university dropouts were useful in predicting attrition: student status, residence, financial sources, distance from home town, goal fulfillment, and satisfaction with…

Simulation Experiments in Practice : Statistical Design and Regression Analysis

NARCIS (Netherlands)

Kleijnen, J.P.C.

2007-01-01

In practice, simulation analysts often change only one factor at a time, and use graphical analysis of the resulting Input/Output (I/O) data. Statistical theory proves that more information is obtained when applying Design Of Experiments (DOE) and linear regression analysis. Unfortunately, classic

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.

SURVIVAL ANALYSIS AND LENGTH-BIASED SAMPLING

Directory of Open Access Journals (Sweden)

Masoud Asgharian

2010-12-01

Full Text Available When survival data are colleted as part of a prevalent cohort study, the recruited cases have already experienced their initiating event. These prevalent cases are then followed for a fixed period of time at the end of which the subjects will either have failed or have been censored. When interests lies in estimating the survival distribution, from onset, of subjects with the disease, one must take into account that the survival times of the cases in a prevalent cohort study are left truncated. When it is possible to assume that there has not been any epidemic of the disease over the past period of time that covers the onset times of the subjects, one may assume that the underlying incidence process that generates the initiating event times is a stationary Poisson process. Under such assumption, the survival times of the recruited subjects are called “lengthbiased”. I discuss the challenges one is faced with in analyzing these type of data. To address the theoretical aspects of the work, I present asymptotic results for the NPMLE of the length-biased as well as the unbiased survival distribution. I also discuss estimating the unbiased survival function using only the follow-up time. This addresses the case that the onset times are either unknown or known with uncertainty. Some of our most recent work and open questions will be presented. These include some aspects of analysis of covariates, strong approximation, functional LIL and density estimation under length-biased sampling with right censoring. The results will be illustrated with survival data from patients with dementia, collected as part of the Canadian Study of Health and Aging (CSHA.

Survival prognosis in plantations of Pinus caribaea Morelet var. caribaea Barrett & Golfari

Directory of Open Access Journals (Sweden)

Ouorou Ganni Mariel Guera

2018-01-01

Full Text Available The present study was carried out with the objective of obtaining regression equations and Artificial Neural Networks (ANNs for the prognosis of Pinus caribaea var. caribaea survival in Macurije Forest Company, province of Pinar del Río - Cuba. The data used in the modeling comes from the measurement of the variables age (years and survival (density in circular permanent plots of 500 m² established in P. caribaea var. caribaea plantations. The study was divided into three stages: i Adjustment of survival traditional regression models; ii Training of ANNs for survival prognosis, including categorical variables «site» and «Basic Units of Forest Production»; iii Comparison of regression equations performance with those of ANNs in survival prognosis. The best models and ANNs were selected based on: adjusted determination coefficient - R2aj (%, square root of the mean square error - RMSE (% and residue distribution analysis. The evaluation of the models goodness of fit also included the verification of the assumptions of normality, homocedasticity and absence of serial autocorrelation in the residues by Kolmogorov-Smirnov, White and Durbin-Watson tests, respectively. The model of Pienaar and Shiver (1981 turned out to be the best fit in survival prognosis. The ANN MLP 13-10-1 was the one with the best generalization capacity and presented a performance similar to that of Pienaar and Shiver equation.

Survival benefit of radiotherapy to patients with small cell esophagus carcinoma: an analysis of Surveillance Epidemiology and End Results (SEER) data.

Science.gov (United States)

Song, Yaqi; Wang, Wanwei; Tao, Guangzhou; Zhu, Weiguo; Zhou, Xilei; Pan, Peng

2016-03-29

Small cell esophageal carcinoma (SCEC) is a rare malignant tumor. So far, few studies are found to research the effect of radiotherapy (RT) to it. This study is designed to explore the prognostic factors, and analyze survival benefit of RT to patients with SCEC. Patients with SCEC were more likely to be in female, older, higher disease stage than those with non-small cell esophageal carcinoma. RT was used in more than 50% SCEC patients. RT tended be reduced as the disease stage raise in SCEC. Univariate and multivariate analysis showed that age, year, disease stage, and RT were the prognostic factors of survival (P 0.05) and nearly 30% risks of death in distant stage (P > 0.05). SCEC patients between 1973 and 2012 were searched from the Surveillance Epidemiology and End Results (SEER) data. Clinical factors including age, year, sex, race, stage, surgery, and RT were summarized. Univariate and multivariate analysis were performed to explore the independent prognostic factors of SCEC. Cox regression survival analysis was performed to evaluate the effect of RT to SCEC based on different stages. Stage, age, year, and RT are independent prognostic factors of SCEC. Survival benefit of RT exists in any disease stage, but is only statistically significant in localized stage of SCEC.

Regression Analysis of Combined Gene Expression Regulation in Acute Myeloid Leukemia

Science.gov (United States)

Li, Yue; Liang, Minggao; Zhang, Zhaolei

2014-01-01

Gene expression is a combinatorial function of genetic/epigenetic factors such as copy number variation (CNV), DNA methylation (DM), transcription factors (TF) occupancy, and microRNA (miRNA) post-transcriptional regulation. At the maturity of microarray/sequencing technologies, large amounts of data measuring the genome-wide signals of those factors became available from Encyclopedia of DNA Elements (ENCODE) and The Cancer Genome Atlas (TCGA). However, there is a lack of an integrative model to take full advantage of these rich yet heterogeneous data. To this end, we developed RACER (Regression Analysis of Combined Expression Regulation), which fits the mRNA expression as response using as explanatory variables, the TF data from ENCODE, and CNV, DM, miRNA expression signals from TCGA. Briefly, RACER first infers the sample-specific regulatory activities by TFs and miRNAs, which are then used as inputs to infer specific TF/miRNA-gene interactions. Such a two-stage regression framework circumvents a common difficulty in integrating ENCODE data measured in generic cell-line with the sample-specific TCGA measurements. As a case study, we integrated Acute Myeloid Leukemia (AML) data from TCGA and the related TF binding data measured in K562 from ENCODE. As a proof-of-concept, we first verified our model formalism by 10-fold cross-validation on predicting gene expression. We next evaluated RACER on recovering known regulatory interactions, and demonstrated its superior statistical power over existing methods in detecting known miRNA/TF targets. Additionally, we developed a feature selection procedure, which identified 18 regulators, whose activities clustered consistently with cytogenetic risk groups. One of the selected regulators is miR-548p, whose inferred targets were significantly enriched for leukemia-related pathway, implicating its novel role in AML pathogenesis. Moreover, survival analysis using the inferred activities identified C-Fos as a potential AML

Enhanced secondary analysis of survival data: reconstructing the data from published Kaplan-Meier survival curves.

Science.gov (United States)

Guyot, Patricia; Ades, A E; Ouwens, Mario J N M; Welton, Nicky J

2012-02-01

The results of Randomized Controlled Trials (RCTs) on time-to-event outcomes that are usually reported are median time to events and Cox Hazard Ratio. These do not constitute the sufficient statistics required for meta-analysis or cost-effectiveness analysis, and their use in secondary analyses requires strong assumptions that may not have been adequately tested. In order to enhance the quality of secondary data analyses, we propose a method which derives from the published Kaplan Meier survival curves a close approximation to the original individual patient time-to-event data from which they were generated. We develop an algorithm that maps from digitised curves back to KM data by finding numerical solutions to the inverted KM equations, using where available information on number of events and numbers at risk. The reproducibility and accuracy of survival probabilities, median survival times and hazard ratios based on reconstructed KM data was assessed by comparing published statistics (survival probabilities, medians and hazard ratios) with statistics based on repeated reconstructions by multiple observers. The validation exercise established there was no material systematic error and that there was a high degree of reproducibility for all statistics. Accuracy was excellent for survival probabilities and medians, for hazard ratios reasonable accuracy can only be obtained if at least numbers at risk or total number of events are reported. The algorithm is a reliable tool for meta-analysis and cost-effectiveness analyses of RCTs reporting time-to-event data. It is recommended that all RCTs should report information on numbers at risk and total number of events alongside KM curves.

Using Survival Analysis to Evaluate Medical Equipment Battery Life.

Science.gov (United States)

Kuhajda, David

2016-01-01

As hospital medical device managers obtain more data, opportunities exist for using the data to improve medical device management, enhance patient safety, and evaluate costs of decisions. As a demonstration of the ability to use data analytics, this article applies survival analysis statistical techniques to assist in making decisions on medical equipment maintenance. The analysis was performed on a large amount of data related to failures of an infusion pump manufacturer's lithium battery and two aftermarket replacement lithium batteries from one hospital facility. The survival analysis resulted in statistical evidence showing that one of the third-party batteries had a lower survival curve than the infusion pump manufacturer's battery. This lower survival curve translates to a shorter expected life before replacement is needed. The data suggested that to limit unexpected failures, replacing batteries at a two-year interval, rather than the current industry recommendation of three years, may be warranted. For less than $5,400 in additional annual cost, the risk of unexpected battery failures can be reduced from an estimated 28% to an estimated 7%.

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.

Regression analysis of radiological parameters in nuclear power plants

International Nuclear Information System (INIS)

Bhargava, Pradeep; Verma, R.K.; Joshi, M.L.

2003-01-01

Indian Pressurized Heavy Water Reactors (PHWRs) have now attained maturity in their operations. Indian PHWR operation started in the year 1972. At present there are 12 operating PHWRs collectively producing nearly 2400 MWe. Sufficient radiological data are available for analysis to draw inferences which may be utilised for better understanding of radiological parameters influencing the collective internal dose. Tritium is the main contributor to the occupational internal dose originating in PHWRs. An attempt has been made to establish the relationship between radiological parameters, which may be useful to draw inferences about the internal dose. Regression analysis have been done to find out the relationship, if it exist, among the following variables: A. Specific tritium activity of heavy water (Moderator and PHT) and tritium concentration in air at various work locations. B. Internal collective occupational dose and tritium release to environment through air route. C. Specific tritium activity of heavy water (Moderator and PHT) and collective internal occupational dose. For this purpose multivariate regression analysis has been carried out. D. Tritium concentration in air at various work location and tritium release to environment through air route. For this purpose multivariate regression analysis has been carried out. This analysis reveals that collective internal dose has got very good correlation with the tritium activity release to the environment through air route. Whereas no correlation has been found between specific tritium activity in the heavy water systems and collective internal occupational dose. The good correlation has been found in case D and F test reveals that it is not by chance. (author)

Comparison of cranial sex determination by discriminant analysis and logistic regression.

Science.gov (United States)

Amores-Ampuero, Anabel; Alemán, Inmaculada

2016-04-05

Various methods have been proposed for estimating dimorphism. The objective of this study was to compare sex determination results from cranial measurements using discriminant analysis or logistic regression. The study sample comprised 130 individuals (70 males) of known sex, age, and cause of death from San José cemetery in Granada (Spain). Measurements of 19 neurocranial dimensions and 11 splanchnocranial dimensions were subjected to discriminant analysis and logistic regression, and the percentages of correct classification were compared between the sex functions obtained with each method. The discriminant capacity of the selected variables was evaluated with a cross-validation procedure. The percentage accuracy with discriminant analysis was 78.2% for the neurocranium (82.4% in females and 74.6% in males) and 73.7% for the splanchnocranium (79.6% in females and 68.8% in males). These percentages were higher with logistic regression analysis: 85.7% for the neurocranium (in both sexes) and 94.1% for the splanchnocranium (100% in females and 91.7% in males).

Regression Analysis: Instructional Resource for Cost/Managerial Accounting

Science.gov (United States)

Stout, David E.

2015-01-01

This paper describes a classroom-tested instructional resource, grounded in principles of active learning and a constructivism, that embraces two primary objectives: "demystify" for accounting students technical material from statistics regarding ordinary least-squares (OLS) regression analysis--material that students may find obscure or…

The influence of sarcopenia on survival and surgical complications in ovarian cancer patients undergoing primary debulking surgery.

Science.gov (United States)

Rutten, I J G; Ubachs, J; Kruitwagen, R F P M; van Dijk, D P J; Beets-Tan, R G H; Massuger, L F A G; Olde Damink, S W M; Van Gorp, T

2017-04-01

Sarcopenia, severe skeletal muscle loss, has been identified as a prognostic factor in various malignancies. This study aims to investigate whether sarcopenia is associated with overall survival (OS) and surgical complications in patients with advanced ovarian cancer undergoing primary debulking surgery (PDS). Ovarian cancer patients (n = 216) treated with PDS were enrolled retrospectively. Total skeletal muscle surface area was measured on axial computed tomography at the level of the third lumbar vertebra. Optimum stratification was used to find the optimal skeletal muscle index cut-off to define sarcopenia (≤38.73 cm 2 /m 2 ). Cox-regression and Kaplan-Meier analysis were used to analyse the relationship between sarcopenia and OS. The effect of sarcopenia on the development of major surgical complications was studied with logistic regression. Kaplan-Meier analysis showed a significant survival disadvantage for patients with sarcopenia compared to patients without sarcopenia (p = 0.010). Sarcopenia univariably predicted OS (HR 1.536 (95% CI 1.105-2.134), p = 0.011) but was not significant in multivariable Cox-regression analysis (HR 1.362 (95% CI 0.968-1.916), p = 0.076). Significant predictors for OS in multivariable Cox-regression analysis were complete PDS, treatment in a specialised centre and the development of major complications. Sarcopenia was not predictive of major complications. Sarcopenia was not predictive of OS or major complications in ovarian cancer patients undergoing primary debulking surgery. However a strong trend towards a survival disadvantage for patients with sarcopenia was seen. Future prospective studies should focus on interventions to prevent or reverse sarcopenia and possibly increase ovarian cancer survival. Complete cytoreduction remains the strongest predictor of ovarian cancer survival. Copyright © 2017 Elsevier Ltd, BASO ~ The Association for Cancer Surgery, and the European Society of Surgical Oncology. All rights

Translating Response During Therapy into Ultimate Treatment Outcome: A Personalized 4-Dimensional MRI Tumor Volumetric Regression Approach in Cervical Cancer

International Nuclear Information System (INIS)

Mayr, Nina A.; Wang, Jian Z.; Lo, Simon S.; Zhang Dongqing; Grecula, John C.; Lu Lanchun; Montebello, Joseph F.; Fowler, Jeffrey M.; Yuh, William T.C.

2010-01-01

Purpose: To assess individual volumetric tumor regression pattern in cervical cancer during therapy using serial four-dimensional MRI and to define the regression parameters' prognostic value validated with local control and survival correlation. Methods and Materials: One hundred and fifteen patients with Stage IB 2 -IVA cervical cancer treated with radiation therapy (RT) underwent serial MRI before (MRI 1) and during RT, at 2-2.5 weeks (MRI 2, at 20-25 Gy), and at 4-5 weeks (MRI 3, at 40-50 Gy). Eighty patients had a fourth MRI 1-2 months post-RT. Mean follow-up was 5.3 years. Tumor volume was measured by MRI-based three-dimensional volumetry, and plotted as dose(time)/volume regression curves. Volume regression parameters were correlated with local control, disease-specific, and overall survival. Results: Residual tumor volume, slope, and area under the regression curve correlated significantly with local control and survival. Residual volumes ≥20% at 40-50 Gy were independently associated with inferior 5-year local control (53% vs. 97%, p <0.001) and disease-specific survival rates (50% vs. 72%, p = 0.009) than smaller volumes. Patients with post-RT residual volumes ≥10% had 0% local control and 17% disease-specific survival, compared with 91% and 72% for <10% volume (p <0.001). Conclusion: Using more accurate four-dimensional volumetric regression analysis, tumor response can now be directly translated into individual patients' outcome for clinical application. Our results define two temporal thresholds critically influencing local control and survival. In patients with ≥20% residual volume at 40-50 Gy and ≥10% post-RT, the risk for local failure and death are so high that aggressive intervention may be warranted.

Analysis of survival data with dependent censoring copula-based approaches

CERN Document Server

Emura, Takeshi

2018-01-01

This book introduces readers to copula-based statistical methods for analyzing survival data involving dependent censoring. Primarily focusing on likelihood-based methods performed under copula models, it is the first book solely devoted to the problem of dependent censoring. The book demonstrates the advantages of the copula-based methods in the context of medical research, especially with regard to cancer patients’ survival data. Needless to say, the statistical methods presented here can also be applied to many other branches of science, especially in reliability, where survival analysis plays an important role. The book can be used as a textbook for graduate coursework or a short course aimed at (bio-) statisticians. To deepen readers’ understanding of copula-based approaches, the book provides an accessible introduction to basic survival analysis and explains the mathematical foundations of copula-based survival models.

Trends of Incidence and Survival of Gastrointestinal Neuroendocrine Tumors in the United States: A Seer Analysis

Directory of Open Access Journals (Sweden)

Vassiliki L. Tsikitis, Betsy C. Wertheim, Marlon A. Guerrero

2012-01-01

Full Text Available OBJECTIVES: To examine trends in detection and survival of hollow viscus gastrointestinal neuroendocrine tumors (NETs across time and geographic regions of the U.S.METHODS: We used the Surveillance, Epidemiology and End Results (SEER database to investigate 19,669 individuals with newly diagnosed gastrointestinal NETs. Trends in incidence were tested using Poisson regression. Cox proportional hazards regression was used to examine survival.RESULTS: Incidence increased over time for NETs of all gastrointestinal sites (all P < 0.001, except appendix. Rates have risen faster for NETs of the small intestine and rectum than stomach and colon. Rectal NETs were detected at a faster pace among blacks than whites (P < 0.001 and slower in the East than other regions (P < 0.001. We observed that appendiceal and rectal NETs carry the best prognosis and survival of small intestinal and colon NETs has improved for both men and women. Colon NETs showed different temporal trends in survival according to geographic region (Pinteraction = 0.028. Improved prognosis was more consistent across the country for small intestinal NETs.CONCLUSIONS: Incidence of gastrointestinal NETs has increased, accompanied by inconsistently improved survival for different anatomic sites among certain groups defined by race and geographic region.

Robust Mediation Analysis Based on Median Regression

Science.gov (United States)

Yuan, Ying; MacKinnon, David P.

2014-01-01

Mediation analysis has many applications in psychology and the social sciences. The most prevalent methods typically assume that the error distribution is normal and homoscedastic. However, this assumption may rarely be met in practice, which can affect the validity of the mediation analysis. To address this problem, we propose robust mediation analysis based on median regression. Our approach is robust to various departures from the assumption of homoscedasticity and normality, including heavy-tailed, skewed, contaminated, and heteroscedastic distributions. Simulation studies show that under these circumstances, the proposed method is more efficient and powerful than standard mediation analysis. We further extend the proposed robust method to multilevel mediation analysis, and demonstrate through simulation studies that the new approach outperforms the standard multilevel mediation analysis. We illustrate the proposed method using data from a program designed to increase reemployment and enhance mental health of job seekers. PMID:24079925

Regression modeling strategies with applications to linear models, logistic and ordinal regression, and survival analysis

CERN Document Server

Harrell , Jr , Frank E

2015-01-01

This highly anticipated second edition features new chapters and sections, 225 new references, and comprehensive R software. In keeping with the previous edition, this book is about the art and science of data analysis and predictive modeling, which entails choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for fitting nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap.  The reader will gain a keen understanding of predictive accuracy, and the harm of categorizing continuous predictors or outcomes.  This text realistically...

Multiplication factor versus regression analysis in stature estimation from hand and foot dimensions.

Science.gov (United States)

Krishan, Kewal; Kanchan, Tanuj; Sharma, Abhilasha

2012-05-01

Estimation of stature is an important parameter in identification of human remains in forensic examinations. The present study is aimed to compare the reliability and accuracy of stature estimation and to demonstrate the variability in estimated stature and actual stature using multiplication factor and regression analysis methods. The study is based on a sample of 246 subjects (123 males and 123 females) from North India aged between 17 and 20 years. Four anthropometric measurements; hand length, hand breadth, foot length and foot breadth taken on the left side in each subject were included in the study. Stature was measured using standard anthropometric techniques. Multiplication factors were calculated and linear regression models were derived for estimation of stature from hand and foot dimensions. Derived multiplication factors and regression formula were applied to the hand and foot measurements in the study sample. The estimated stature from the multiplication factors and regression analysis was compared with the actual stature to find the error in estimated stature. The results indicate that the range of error in estimation of stature from regression analysis method is less than that of multiplication factor method thus, confirming that the regression analysis method is better than multiplication factor analysis in stature estimation. Copyright © 2012 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Enhanced secondary analysis of survival data: reconstructing the data from published Kaplan-Meier survival curves

Directory of Open Access Journals (Sweden)

Guyot Patricia

2012-02-01

Full Text Available Abstract Background The results of Randomized Controlled Trials (RCTs on time-to-event outcomes that are usually reported are median time to events and Cox Hazard Ratio. These do not constitute the sufficient statistics required for meta-analysis or cost-effectiveness analysis, and their use in secondary analyses requires strong assumptions that may not have been adequately tested. In order to enhance the quality of secondary data analyses, we propose a method which derives from the published Kaplan Meier survival curves a close approximation to the original individual patient time-to-event data from which they were generated. Methods We develop an algorithm that maps from digitised curves back to KM data by finding numerical solutions to the inverted KM equations, using where available information on number of events and numbers at risk. The reproducibility and accuracy of survival probabilities, median survival times and hazard ratios based on reconstructed KM data was assessed by comparing published statistics (survival probabilities, medians and hazard ratios with statistics based on repeated reconstructions by multiple observers. Results The validation exercise established there was no material systematic error and that there was a high degree of reproducibility for all statistics. Accuracy was excellent for survival probabilities and medians, for hazard ratios reasonable accuracy can only be obtained if at least numbers at risk or total number of events are reported. Conclusion The algorithm is a reliable tool for meta-analysis and cost-effectiveness analyses of RCTs reporting time-to-event data. It is recommended that all RCTs should report information on numbers at risk and total number of events alongside KM curves.

A comparative study of multiple regression analysis and back ...

Indian Academy of Sciences (India)

Abhijit Sarkar

artificial neural network (ANN) models to predict weld bead geometry and HAZ width in submerged arc welding ... Keywords. Submerged arc welding (SAW); multi-regression analysis (MRA); artificial neural network ..... Degree of freedom.

Solitary plasmacytoma: population-based analysis of survival trends and effect of various treatment modalities in the USA.

Science.gov (United States)

Thumallapally, Nishitha; Meshref, Ahmed; Mousa, Mohammed; Terjanian, Terenig

2017-01-05

Solitary plasmacytoma (SP) is a localized neoplastic plasma cell disorder with an annual incidence of less than 450 cases. Given the rarity of this disorder, it is difficult to conduct large-scale population studies. Consequently, very limited information on the disorder is available, making it difficult to estimate the incidence and survival rates. Furthermore, limited information is available on the efficacy of various treatment modalities in relation to primary tumor sites. The data for this retrospective study were drawn from the Surveillance, Epidemiology and End Results (SEER) database, which comprises 18 registries; patient demographics, treatment modalities and survival rates were obtained for those diagnosed with SP from 1998 to 2007. Various prognostic factors were analyzed via Kaplan-Meier analysis and log-rank test, with 5-year relative survival rate defined as the primary outcome of interest. Cox regression analysis was employed in the multivariate analysis. The SEER search from 1998 to 2007 yielded records for 1691 SP patients. The median age at diagnosis was 63 years. The patient cohort was 62.4% male, 37.6% female, 80% Caucasian, 14.6% African American and 5.4% other races. Additionally, 57.8% had osseous plasmacytoma, and 31.9% had extraosseous involvement. Unspecified plasmacytoma was noted in 10.2% of patients. The most common treatment modalities were radiotherapy (RT) (48.8%), followed by combination surgery with RT (21.2%) and surgery alone (11.6%). Univariate analysis of prognostic factors revealed that the survival outcomes were better for younger male patients who received RT with surgery (p multiple myeloma (MM) was noted in 551 patients. Age >60 years was associated with a lower 5-year survival in patients who progressed to MM compared to those who were diagnosed initially with MM (15.1 vs 16.6%). Finally, those who received RT and progressed to MM still had a higher chance of survival than those who were diagnosed with MM initially and

Marital status and survival of patients with oral cavity squamous cell carcinoma: a population-based study.

Science.gov (United States)

Shi, Xiao; Zhang, Ting-Ting; Hu, Wei-Ping; Ji, Qing-Hai

2017-04-25

The relationship between marital status and oral cavity squamous cell carcinoma (OCSCC) survival has not been explored. The objective of our study was to evaluate the impact of marital status on OCSCC survival and investigate the potential mechanisms. Married patients had better 5-year cancer-specific survival (CSS) (66.7% vs 54.9%) and 5-year overall survival (OS) (56.0% vs 41.1%). In multivariate Cox regression models, unmarried patients also showed higher mortality risk for both CSS (Hazard Ratio [HR]: 1.260, 95% confidence interval (CI): 1.187-1.339, P married patients were more likely to be diagnosed at earlier stage (P Married patients still demonstrated better prognosis in the 1:1 matched group analysis (CSS: 62.9% vs 60.8%, OS: 52.3% vs 46.5%). 11022 eligible OCSCC patients were identified from Surveillance, Epidemiology, and End Results (SEER) database, including 5902 married and 5120 unmarried individuals. Kaplan-Meier analysis, Log-rank test and Cox proportional hazards regression model were used to analyze survival and mortality risk. Influence of marital status on stage, age at diagnosis and selection of treatment was determined by binomial and multinomial logistic regression. Propensity score matching method was adopted to perform a 1:1 matched cohort. Marriage has an independently protective effect on OCSCC survival. Earlier diagnosis and more sufficient treatment are possible explanations. Besides, even after 1:1 matching, survival advantage of married group still exists, indicating that spousal support from other aspects may also play an important role.

Multilayer perceptron for robust nonlinear interval regression analysis using genetic algorithms.

Science.gov (United States)

Hu, Yi-Chung

2014-01-01

On the basis of fuzzy regression, computational models in intelligence such as neural networks have the capability to be applied to nonlinear interval regression analysis for dealing with uncertain and imprecise data. When training data are not contaminated by outliers, computational models perform well by including almost all given training data in the data interval. Nevertheless, since training data are often corrupted by outliers, robust learning algorithms employed to resist outliers for interval regression analysis have been an interesting area of research. Several approaches involving computational intelligence are effective for resisting outliers, but the required parameters for these approaches are related to whether the collected data contain outliers or not. Since it seems difficult to prespecify the degree of contamination beforehand, this paper uses multilayer perceptron to construct the robust nonlinear interval regression model using the genetic algorithm. Outliers beyond or beneath the data interval will impose slight effect on the determination of data interval. Simulation results demonstrate that the proposed method performs well for contaminated datasets.

Exploratory regression analysis: a tool for selecting models and determining predictor importance.

Science.gov (United States)

Braun, Michael T; Oswald, Frederick L

2011-06-01

Linear regression analysis is one of the most important tools in a researcher's toolbox for creating and testing predictive models. Although linear regression analysis indicates how strongly a set of predictor variables, taken together, will predict a relevant criterion (i.e., the multiple R), the analysis cannot indicate which predictors are the most important. Although there is no definitive or unambiguous method for establishing predictor variable importance, there are several accepted methods. This article reviews those methods for establishing predictor importance and provides a program (in Excel) for implementing them (available for direct download at http://dl.dropbox.com/u/2480715/ERA.xlsm?dl=1) . The program investigates all 2(p) - 1 submodels and produces several indices of predictor importance. This exploratory approach to linear regression, similar to other exploratory data analysis techniques, has the potential to yield both theoretical and practical benefits.

Prehospital helicopter transport and survival of patients with traumatic brain injury.

Science.gov (United States)

Bekelis, Kimon; Missios, Symeon; Mackenzie, Todd A

2015-03-01

To investigate the association of helicopter transport with survival of patients with traumatic brain injury (TBI), in comparison with ground emergency medical services (EMS). Helicopter utilization and its effect on the outcomes of TBI remain controversial. We performed a retrospective cohort study involving patients with TBI who were registered in the National Trauma Data Bank between 2009 and 2011. Regression techniques with propensity score matching were used to investigate the association of helicopter transport with survival of patients with TBI, in comparison with ground EMS. During the study period, there were 209,529 patients with TBI who were registered in the National Trauma Data Bank and met the inclusion criteria. Of these patients, 35,334 were transported via helicopters and 174,195 via ground EMS. For patients transported to level I trauma centers, 2797 deaths (12%) were recorded after helicopter transport and 8161 (7.8%) after ground EMS. Multivariable logistic regression analysis demonstrated an association of helicopter transport with increased survival [OR (odds ratio), 1.95; 95% confidence interval (CI), 1.81-2.10; absolute risk reduction (ARR), 6.37%]. This persisted after propensity score matching (OR, 1.88; 95% CI, 1.74-2.03; ARR, 5.93%). For patients transported to level II trauma centers, 1282 deaths (10.6%) were recorded after helicopter transport and 5097 (7.3%) after ground EMS. Multivariable logistic regression analysis demonstrated an association of helicopter transport with increased survival (OR, 1.81; 95% CI, 1.64-2.00; ARR 5.17%). This again persisted after propensity score matching (OR, 1.73; 95% CI, 1.55-1.94; ARR, 4.69). Helicopter transport of patients with TBI to level I and II trauma centers was associated with improved survival, in comparison with ground EMS.

Regression analysis for LED color detection of visual-MIMO system

Science.gov (United States)

Banik, Partha Pratim; Saha, Rappy; Kim, Ki-Doo

2018-04-01

Color detection from a light emitting diode (LED) array using a smartphone camera is very difficult in a visual multiple-input multiple-output (visual-MIMO) system. In this paper, we propose a method to determine the LED color using a smartphone camera by applying regression analysis. We employ a multivariate regression model to identify the LED color. After taking a picture of an LED array, we select the LED array region, and detect the LED using an image processing algorithm. We then apply the k-means clustering algorithm to determine the number of potential colors for feature extraction of each LED. Finally, we apply the multivariate regression model to predict the color of the transmitted LEDs. In this paper, we show our results for three types of environmental light condition: room environmental light, low environmental light (560 lux), and strong environmental light (2450 lux). We compare the results of our proposed algorithm from the analysis of training and test R-Square (%) values, percentage of closeness of transmitted and predicted colors, and we also mention about the number of distorted test data points from the analysis of distortion bar graph in CIE1931 color space.

Evaluation of syngas production unit cost of bio-gasification facility using regression analysis techniques

Energy Technology Data Exchange (ETDEWEB)

Deng, Yangyang; Parajuli, Prem B.

2011-08-10

Evaluation of economic feasibility of a bio-gasification facility needs understanding of its unit cost under different production capacities. The objective of this study was to evaluate the unit cost of syngas production at capacities from 60 through 1800Nm 3/h using an economic model with three regression analysis techniques (simple regression, reciprocal regression, and log-log regression). The preliminary result of this study showed that reciprocal regression analysis technique had the best fit curve between per unit cost and production capacity, with sum of error squares (SES) lower than 0.001 and coefficient of determination of (R 2) 0.996. The regression analysis techniques determined the minimum unit cost of syngas production for micro-scale bio-gasification facilities of $0.052/Nm 3, under the capacity of 2,880 Nm 3/h. The results of this study suggest that to reduce cost, facilities should run at a high production capacity. In addition, the contribution of this technique could be the new categorical criterion to evaluate micro-scale bio-gasification facility from the perspective of economic analysis.

Hepatorenal Syndrome: Outcome of Response to Therapy and Predictors of Survival

Directory of Open Access Journals (Sweden)

Jan Heidemann

2015-01-01

Full Text Available Aim. Treatment of hepatorenal syndrome (HRS in patients with liver cirrhosis is still challenging and characterized by a very high mortality. This study aimed to delineate treatment patterns and clinical outcomes of patients with HRS intravenously treated with terlipressin. Methods. In this retrospective single-center cohort study, 119 patients (median [IQR]; 56.50 [50.75–63.00] years of age with HRS were included. All patients were treated with terlipressin and human albumin intravenously. Those with response to treatment (n=65 were compared to the patient cohort without improvement (n=54. Patient characteristics and clinical parameters (Child stage, ascites, hepatic encephalopathy, HRS type I/II, and initial MELD score were retrieved. Univariate analysis of factors influencing the success of terlipressin therapy and Cox regression analysis of factors influencing survival was carried out. Results. One-month survival was significantly longer in the group of responders (p=0.048. Cox regression analysis identified age [Hazard ratio, 95% confidence interval (CI; 1.05, 1.01–1.09, resp.], alcohol abuse [HR 3.05, 95% CI 1.11–8.38], duration of treatment [HR 0.92, 95% CI 0.88–0.96], and MELD score [HR 1.08, 95% CI 1.02–1.14] to be independent predictors of survival. Conclusions. Survival of HRS patients after treatment depends on age, etiology of liver disease, and the duration of treatment.

A primer for biomedical scientists on how to execute model II linear regression analysis.

Science.gov (United States)

Ludbrook, John

2012-04-01

1. There are two very different ways of executing linear regression analysis. One is Model I, when the x-values are fixed by the experimenter. The other is Model II, in which the x-values are free to vary and are subject to error. 2. I have received numerous complaints from biomedical scientists that they have great difficulty in executing Model II linear regression analysis. This may explain the results of a Google Scholar search, which showed that the authors of articles in journals of physiology, pharmacology and biochemistry rarely use Model II regression analysis. 3. I repeat my previous arguments in favour of using least products linear regression analysis for Model II regressions. I review three methods for executing ordinary least products (OLP) and weighted least products (WLP) regression analysis: (i) scientific calculator and/or computer spreadsheet; (ii) specific purpose computer programs; and (iii) general purpose computer programs. 4. Using a scientific calculator and/or computer spreadsheet, it is easy to obtain correct values for OLP slope and intercept, but the corresponding 95% confidence intervals (CI) are inaccurate. 5. Using specific purpose computer programs, the freeware computer program smatr gives the correct OLP regression coefficients and obtains 95% CI by bootstrapping. In addition, smatr can be used to compare the slopes of OLP lines. 6. When using general purpose computer programs, I recommend the commercial programs systat and Statistica for those who regularly undertake linear regression analysis and I give step-by-step instructions in the Supplementary Information as to how to use loss functions. © 2011 The Author. Clinical and Experimental Pharmacology and Physiology. © 2011 Blackwell Publishing Asia Pty Ltd.

External Tank Liquid Hydrogen (LH2) Prepress Regression Analysis Independent Review Technical Consultation Report

Science.gov (United States)

Parsons, Vickie s.

2009-01-01

The request to conduct an independent review of regression models, developed for determining the expected Launch Commit Criteria (LCC) External Tank (ET)-04 cycle count for the Space Shuttle ET tanking process, was submitted to the NASA Engineering and Safety Center NESC on September 20, 2005. The NESC team performed an independent review of regression models documented in Prepress Regression Analysis, Tom Clark and Angela Krenn, 10/27/05. This consultation consisted of a peer review by statistical experts of the proposed regression models provided in the Prepress Regression Analysis. This document is the consultation's final report.

Tutorial on Biostatistics: Linear Regression Analysis of Continuous Correlated Eye Data.

Science.gov (United States)

Ying, Gui-Shuang; Maguire, Maureen G; Glynn, Robert; Rosner, Bernard

2017-04-01

To describe and demonstrate appropriate linear regression methods for analyzing correlated continuous eye data. We describe several approaches to regression analysis involving both eyes, including mixed effects and marginal models under various covariance structures to account for inter-eye correlation. We demonstrate, with SAS statistical software, applications in a study comparing baseline refractive error between one eye with choroidal neovascularization (CNV) and the unaffected fellow eye, and in a study determining factors associated with visual field in the elderly. When refractive error from both eyes were analyzed with standard linear regression without accounting for inter-eye correlation (adjusting for demographic and ocular covariates), the difference between eyes with CNV and fellow eyes was 0.15 diopters (D; 95% confidence interval, CI -0.03 to 0.32D, p = 0.10). Using a mixed effects model or a marginal model, the estimated difference was the same but with narrower 95% CI (0.01 to 0.28D, p = 0.03). Standard regression for visual field data from both eyes provided biased estimates of standard error (generally underestimated) and smaller p-values, while analysis of the worse eye provided larger p-values than mixed effects models and marginal models. In research involving both eyes, ignoring inter-eye correlation can lead to invalid inferences. Analysis using only right or left eyes is valid, but decreases power. Worse-eye analysis can provide less power and biased estimates of effect. Mixed effects or marginal models using the eye as the unit of analysis should be used to appropriately account for inter-eye correlation and maximize power and precision.

Metagenes Associated with Survival in Non-Small Cell Lung Cancer

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Urgard, Egon; Vooder, Tõnu; Võsa, Urmo; Välk, Kristjan; Liu, Mingming; Luo, Cheng; Hoti, Fabian; Roosipuu, Retlav; Annilo, Tarmo; Laine, Jukka; Frenz, Christopher M.; Zhang, Liqing; Metspalu, Andres

2011-01-01

NSCLC (non-small cell lung cancer) comprises about 80% of all lung cancer cases worldwide. Surgery is most effective treatment for patients with early-stage disease. However, 30%–55% of these patients develop recurrence within 5 years. Therefore, markers that can be used to accurately classify early-stage NSCLC patients into different prognostic groups may be helpful in selecting patients who should receive specific therapies. A previously published dataset was used to evaluate gene expression profiles of different NSCLC subtypes. A moderated two-sample t-test was used to identify differentially expressed genes between all tumor samples and cancer-free control tissue, between SCC samples and AC/BC samples and between stage I tumor samples and all other tumor samples. Gene expression microarray measurements were validated using qRT-PCR. Bayesian regression analysis and Kaplan-Meier survival analysis were performed to determine metagenes associated with survival. We identified 599 genes which were down-regulated and 402 genes which were up-regulated in NSCLC compared to the normal lung tissue and 112 genes which were up-regulated and 101 genes which were down-regulated in AC/BC compared to the SCC. Further, for stage Ib patients the metagenes potentially associated with survival were identified. Genes that expressed differently between normal lung tissue and cancer showed enrichment in gene ontology terms which were associated with mitosis and proliferation. Bayesian regression and Kaplan-Meier analysis showed that gene-expression patterns and metagene profiles can be applied to predict the probability of different survival outcomes in NSCLC patients. PMID:21695068

Survival analysis for customer satisfaction: A case study

Science.gov (United States)

Hadiyat, M. A.; Wahyudi, R. D.; Sari, Y.

2017-11-01

Most customer satisfaction surveys are conducted periodically to track their dynamics. One of the goals of this survey was to evaluate the service design by recognizing the trend of satisfaction score. Many researchers recommended in redesigning the service when the satisfaction scores were decreasing, so that the service life cycle could be predicted qualitatively. However, these scores were usually set in Likert scale and had quantitative properties. Thus, they should also be analyzed in quantitative model so that the predicted service life cycle would be done by applying the survival analysis. This paper discussed a starting point for customer satisfaction survival analysis with a case study in healthcare service.

Non-stationary hydrologic frequency analysis using B-spline quantile regression

Science.gov (United States)

Nasri, B.; Bouezmarni, T.; St-Hilaire, A.; Ouarda, T. B. M. J.

2017-11-01

Hydrologic frequency analysis is commonly used by engineers and hydrologists to provide the basic information on planning, design and management of hydraulic and water resources systems under the assumption of stationarity. However, with increasing evidence of climate change, it is possible that the assumption of stationarity, which is prerequisite for traditional frequency analysis and hence, the results of conventional analysis would become questionable. In this study, we consider a framework for frequency analysis of extremes based on B-Spline quantile regression which allows to model data in the presence of non-stationarity and/or dependence on covariates with linear and non-linear dependence. A Markov Chain Monte Carlo (MCMC) algorithm was used to estimate quantiles and their posterior distributions. A coefficient of determination and Bayesian information criterion (BIC) for quantile regression are used in order to select the best model, i.e. for each quantile, we choose the degree and number of knots of the adequate B-spline quantile regression model. The method is applied to annual maximum and minimum streamflow records in Ontario, Canada. Climate indices are considered to describe the non-stationarity in the variable of interest and to estimate the quantiles in this case. The results show large differences between the non-stationary quantiles and their stationary equivalents for an annual maximum and minimum discharge with high annual non-exceedance probabilities.

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

Statistical models and methods for reliability and survival analysis

CERN Document Server

Couallier, Vincent; Huber-Carol, Catherine; Mesbah, Mounir; Huber -Carol, Catherine; Limnios, Nikolaos; Gerville-Reache, Leo

2013-01-01

Statistical Models and Methods for Reliability and Survival Analysis brings together contributions by specialists in statistical theory as they discuss their applications providing up-to-date developments in methods used in survival analysis, statistical goodness of fit, stochastic processes for system reliability, amongst others. Many of these are related to the work of Professor M. Nikulin in statistics over the past 30 years. The authors gather together various contributions with a broad array of techniques and results, divided into three parts - Statistical Models and Methods, Statistical

Multiparametric analysis of magnetic resonance images for glioma grading and patient survival time prediction

International Nuclear Information System (INIS)

Garzon, Benjamin; Emblem, Kyrre E.; Mouridsen, Kim; Nedregaard, Baard; Due-Toennessen, Paulina; Nome, Terje; Hald, John K.; Bjoernerud, Atle; Haaberg, Asta K.; Kvinnsland, Yngve

2011-01-01

Background. A systematic comparison of magnetic resonance imaging (MRI) options for glioma diagnosis is lacking. Purpose. To investigate multiple MR-derived image features with respect to diagnostic accuracy in tumor grading and survival prediction in glioma patients. Material and Methods. T1 pre- and post-contrast, T2 and dynamic susceptibility contrast scans of 74 glioma patients with histologically confirmed grade were acquired. For each patient, a set of statistical features was obtained from the parametric maps derived from the original images, in a region-of-interest encompassing the tumor volume. A forward stepwise selection procedure was used to find the best combinations of features for grade prediction with a cross-validated logistic model and survival time prediction with a cox proportional-hazards regression. Results. Presence/absence of enhancement paired with kurtosis of the FM (first moment of the first-pass curve) was the feature combination that best predicted tumor grade (grade II vs. grade III-IV; median AUC 0.96), with the main contribution being due to the first of the features. A lower predictive value (median AUC = 0.82) was obtained when grade IV tumors were excluded. Presence/absence of enhancement alone was the best predictor for survival time, and the regression was significant (P < 0.0001). Conclusion. Presence/absence of enhancement, reflecting transendothelial leakage, was the feature with highest predictive value for grade and survival time in glioma patients

Multiparametric analysis of magnetic resonance images for glioma grading and patient survival time prediction

Energy Technology Data Exchange (ETDEWEB)

Garzon, Benjamin (Dept. of Circulation and Medical Imaging, NTNU, Trondheim (Norway)), email: benjamin.garzon@ntnu.no; Emblem, Kyrre E. (The Interventional Center, Rikshospitalet, Oslo Univ. Hospital, Oslo (Norway); Dept. of Radiology, MGH-HST AA Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston (United States)); Mouridsen, Kim (Center of Functionally Integrative Neuroscience, Aarhus Univ., Aarhus (Denmark)); Nedregaard, Baard; Due-Toennessen, Paulina; Nome, Terje; Hald, John K. (Dept. of Radiology and Nuclear Medicine, Rikshospitalet, Oslo Univ. Hospital, Oslo (Norway)); Bjoernerud, Atle (The Interventional Center, Rikshospitalet, Oslo Univ. Hospital, Oslo (Norway)); Haaberg, Asta K. (Dept. of Circulation and Medical Imaging, NTNU, Trondheim (Norway); Dept. of Medical Imaging, St Olav' s Hospital, Trondheim (Norway)); Kvinnsland, Yngve (NordicImagingLab, Bergen (Norway))

2011-11-15

Background. A systematic comparison of magnetic resonance imaging (MRI) options for glioma diagnosis is lacking. Purpose. To investigate multiple MR-derived image features with respect to diagnostic accuracy in tumor grading and survival prediction in glioma patients. Material and Methods. T1 pre- and post-contrast, T2 and dynamic susceptibility contrast scans of 74 glioma patients with histologically confirmed grade were acquired. For each patient, a set of statistical features was obtained from the parametric maps derived from the original images, in a region-of-interest encompassing the tumor volume. A forward stepwise selection procedure was used to find the best combinations of features for grade prediction with a cross-validated logistic model and survival time prediction with a cox proportional-hazards regression. Results. Presence/absence of enhancement paired with kurtosis of the FM (first moment of the first-pass curve) was the feature combination that best predicted tumor grade (grade II vs. grade III-IV; median AUC 0.96), with the main contribution being due to the first of the features. A lower predictive value (median AUC = 0.82) was obtained when grade IV tumors were excluded. Presence/absence of enhancement alone was the best predictor for survival time, and the regression was significant (P < 0.0001). Conclusion. Presence/absence of enhancement, reflecting transendothelial leakage, was the feature with highest predictive value for grade and survival time in glioma patients

Survival prediction model for postoperative hepatocellular carcinoma patients.

Science.gov (United States)

Ren, Zhihui; He, Shasha; Fan, Xiaotang; He, Fangping; Sang, Wei; Bao, Yongxing; Ren, Weixin; Zhao, Jinming; Ji, Xuewen; Wen, Hao

2017-09-01

This study is to establish a predictive index (PI) model of 5-year survival rate for patients with hepatocellular carcinoma (HCC) after radical resection and to evaluate its prediction sensitivity, specificity, and accuracy.Patients underwent HCC surgical resection were enrolled and randomly divided into prediction model group (101 patients) and model evaluation group (100 patients). Cox regression model was used for univariate and multivariate survival analysis. A PI model was established based on multivariate analysis and receiver operating characteristic (ROC) curve was drawn accordingly. The area under ROC (AUROC) and PI cutoff value was identified.Multiple Cox regression analysis of prediction model group showed that neutrophil to lymphocyte ratio, histological grade, microvascular invasion, positive resection margin, number of tumor, and postoperative transcatheter arterial chemoembolization treatment were the independent predictors for the 5-year survival rate for HCC patients. The model was PI = 0.377 × NLR + 0.554 × HG + 0.927 × PRM + 0.778 × MVI + 0.740 × NT - 0.831 × transcatheter arterial chemoembolization (TACE). In the prediction model group, AUROC was 0.832 and the PI cutoff value was 3.38. The sensitivity, specificity, and accuracy were 78.0%, 80%, and 79.2%, respectively. In model evaluation group, AUROC was 0.822, and the PI cutoff value was well corresponded to the prediction model group with sensitivity, specificity, and accuracy of 85.0%, 83.3%, and 84.0%, respectively.The PI model can quantify the mortality risk of hepatitis B related HCC with high sensitivity, specificity, and accuracy.

Statistical analysis of medical data using SAS

CERN Document Server

Der, Geoff

2005-01-01

An Introduction to SASDescribing and Summarizing DataBasic InferenceScatterplots Correlation: Simple Regression and SmoothingAnalysis of Variance and CovarianceMultiple RegressionLogistic RegressionThe Generalized Linear ModelGeneralized Additive ModelsNonlinear Regression ModelsThe Analysis of Longitudinal Data IThe Analysis of Longitudinal Data II: Models for Normal Response VariablesThe Analysis of Longitudinal Data III: Non-Normal ResponseSurvival AnalysisAnalysis Multivariate Date: Principal Components and Cluster AnalysisReferences

On macroeconomic values investigation using fuzzy linear regression analysis

Directory of Open Access Journals (Sweden)

Richard Pospíšil

2017-06-01

Full Text Available The theoretical background for abstract formalization of the vague phenomenon of complex systems is the fuzzy set theory. In the paper, vague data is defined as specialized fuzzy sets - fuzzy numbers and there is described a fuzzy linear regression model as a fuzzy function with fuzzy numbers as vague parameters. To identify the fuzzy coefficients of the model, the genetic algorithm is used. The linear approximation of the vague function together with its possibility area is analytically and graphically expressed. A suitable application is performed in the tasks of the time series fuzzy regression analysis. The time-trend and seasonal cycles including their possibility areas are calculated and expressed. The examples are presented from the economy field, namely the time-development of unemployment, agricultural production and construction respectively between 2009 and 2011 in the Czech Republic. The results are shown in the form of the fuzzy regression models of variables of time series. For the period 2009-2011, the analysis assumptions about seasonal behaviour of variables and the relationship between them were confirmed; in 2010, the system behaved fuzzier and the relationships between the variables were vaguer, that has a lot of causes, from the different elasticity of demand, through state interventions to globalization and transnational impacts.

Better midterm survival in women after transcatheter aortic valve implantation.

Science.gov (United States)

Takagi, Hisato; Umemoto, Takuya

2017-08-01

In previous meta-analyses demonstrating better midterm overall survival in women undergoing transcatheter aortic valve implantation (TAVI), unadjusted risk and odds ratios were combined. To determine whether female gender is independently associated with better survival after TAVI, we performed a meta-analysis pooling adjusted hazard ratios (HRs) based on multivariate Cox proportional hazard regression. MEDLINE and EMBASE were searched through September 2015 using PubMed and OVID. Studies considered for inclusion met the following criteria: the study population was patients undergoing TAVI; and main outcomes included midterm (mean or median ≥6 months) overall survival or all-cause mortality in women and men. An unadjusted and/or adjusted HR of all-cause mortality for women versus men was abstracted from each individual study. Of 1347 potentially relevant articles screened initially, 16 reports of eligible studies were identified and included. A primary meta-analysis of the 9 adjusted HRs demonstrated a significantly better midterm overall survival in women than men (N.=6891; HR=0.80; 95% confidence interval [CI]: 0.65 to 0.97; P=0.03). A secondary meta-analysis adding 5 statistically non-significant unadjusted HR also indicated better survival in women (N.=8645; HR=0.83; 95% CI: 0.72 to 0.96; P=0.01). Although statistical tests for the primary meta-analysis revealed funnel plot asymmetry in favor of women, the secondary meta-analysis produced a symmetrical funnel plot. Female gender may be independently associated with better midterm overall survival after TAVI.

Extended cox regression model: The choice of timefunction

Science.gov (United States)

Isik, Hatice; Tutkun, Nihal Ata; Karasoy, Durdu

2017-07-01

Cox regression model (CRM), which takes into account the effect of censored observations, is one the most applicative and usedmodels in survival analysis to evaluate the effects of covariates. Proportional hazard (PH), requires a constant hazard ratio over time, is the assumptionofCRM. Using extended CRM provides the test of including a time dependent covariate to assess the PH assumption or an alternative model in case of nonproportional hazards. In this study, the different types of real data sets are used to choose the time function and the differences between time functions are analyzed and discussed.

REGRESSION ANALYSIS OF SEA-SURFACE-TEMPERATURE PATTERNS FOR THE NORTH PACIFIC OCEAN.

Science.gov (United States)

SEA WATER, *SURFACE TEMPERATURE, *OCEANOGRAPHIC DATA, PACIFIC OCEAN, REGRESSION ANALYSIS , STATISTICAL ANALYSIS, UNDERWATER EQUIPMENT, DETECTION, UNDERWATER COMMUNICATIONS, DISTRIBUTION, THERMAL PROPERTIES, COMPUTERS.

SAMSN1 is highly expressed and associated with a poor survival in glioblastoma multiforme.

Directory of Open Access Journals (Sweden)

Yong Yan

Full Text Available OBJECTIVES: To study the expression pattern and prognostic significance of SAMSN1 in glioma. METHODS: Affymetrix and Arrystar gene microarray data in the setting of glioma was analyzed to preliminarily study the expression pattern of SAMSN1 in glioma tissues, and Hieratical clustering of gene microarray data was performed to filter out genes that have prognostic value in malignant glioma. Survival analysis by Kaplan-Meier estimates stratified by SAMSN1 expression was then made based on the data of more than 500 GBM cases provided by The Cancer Genome Atlas (TCGA project. At last, we detected the expression of SAMSN1 in large numbers of glioma and normal brain tissue samples using Tissue Microarray (TMA. Survival analysis by Kaplan-Meier estimates in each grade of glioma was stratified by SAMSN1 expression. Multivariate survival analysis was made by Cox proportional hazards regression models in corresponding groups of glioma. RESULTS: With the expression data of SAMSN1 and 68 other genes, high-grade glioma could be classified into two groups with clearly different prognoses. Gene and large sample tissue microarrays showed high expression of SAMSN1 in glioma particularly in GBM. Survival analysis based on the TCGA GBM data matrix and TMA multi-grade glioma dataset found that SAMSN1 expression was closely related to the prognosis of GBM, either PFS or OS (P<0.05. Multivariate survival analysis with Cox proportional hazards regression models confirmed that high expression of SAMSN1 was a strong risk factor for PFS and OS of GBM patients. CONCLUSION: SAMSN1 is over-expressed in glioma as compared with that found in normal brains, especially in GBM. High expression of SAMSN1 is a significant risk factor for the progression free and overall survival of GBM.

Regression analysis understanding and building business and economic models using Excel

CERN Document Server

Wilson, J Holton

2012-01-01

The technique of regression analysis is used so often in business and economics today that an understanding of its use is necessary for almost everyone engaged in the field. This book will teach you the essential elements of building and understanding regression models in a business/economic context in an intuitive manner. The authors take a non-theoretical treatment that is accessible even if you have a limited statistical background. It is specifically designed to teach the correct use of regression, while advising you of its limitations and teaching about common pitfalls. This book describe

Testing homogeneity in Weibull-regression models.

Science.gov (United States)

Bolfarine, Heleno; Valença, Dione M

2005-10-01

In survival studies with families or geographical units it may be of interest testing whether such groups are homogeneous for given explanatory variables. In this paper we consider score type tests for group homogeneity based on a mixing model in which the group effect is modelled as a random variable. As opposed to hazard-based frailty models, this model presents survival times that conditioned on the random effect, has an accelerated failure time representation. The test statistics requires only estimation of the conventional regression model without the random effect and does not require specifying the distribution of the random effect. The tests are derived for a Weibull regression model and in the uncensored situation, a closed form is obtained for the test statistic. A simulation study is used for comparing the power of the tests. The proposed tests are applied to real data sets with censored data.

Regression models for the restricted residual mean life for right-censored and left-truncated data

DEFF Research Database (Denmark)

Cortese, Giuliana; Holmboe, Stine A.; Scheike, Thomas H.

2017-01-01

The hazard ratios resulting from a Cox's regression hazards model are hard to interpret and to be converted into prolonged survival time. As the main goal is often to study survival functions, there is increasing interest in summary measures based on the survival function that are easier to inter......The hazard ratios resulting from a Cox's regression hazards model are hard to interpret and to be converted into prolonged survival time. As the main goal is often to study survival functions, there is increasing interest in summary measures based on the survival function that are easier...... to interpret than the hazard ratio; the residual mean time is an important example of those measures. However, because of the presence of right censoring, the tail of the survival distribution is often difficult to estimate correctly. Therefore, we consider the restricted residual mean time, which represents...... a partial area under the survival function, given any time horizon τ, and is interpreted as the residual life expectancy up to τ of a subject surviving up to time t. We present a class of regression models for this measure, based on weighted estimating equations and inverse probability of censoring weighted...

Nonlinear regression analysis for evaluating tracer binding parameters using the programmable K1003 desk computer

International Nuclear Information System (INIS)

Sarrach, D.; Strohner, P.

1986-01-01

The Gauss-Newton algorithm has been used to evaluate tracer binding parameters of RIA by nonlinear regression analysis. The calculations were carried out on the K1003 desk computer. Equations for simple binding models and its derivatives are presented. The advantages of nonlinear regression analysis over linear regression are demonstrated

Predictors of survival in surgically treated patients of spinal metastasis

Directory of Open Access Journals (Sweden)

Pravin Padalkar

2011-01-01

Full Text Available Background: The spinal metastasis occurs in up to 40% of cancer patient. We compared the Tokuhashi and Tomita scoring systems, two commonly used scoring systems for prognosis in spinal metastases. We also assessed the different variables separately with respect to their value in predicting postsurgical life expectancy. Finally, we suggest criteria for selecting patients for surgery based on the postoperative survival pattern. Materials and Methods: We retrospectively analyzed 102 patients who had been operated for metastatic disease of the spine. Predictive scoring was done according to the scoring systems proposed by Tokuhashi and Tomita. Overall survival was assessed using Kaplan-Meier survival analysis. Using the log rank test and Cox regression model we assessed the value of the individual components of each scoring system for predicting survival in these patients. Result: The factors that were most significantly associated with survival were the general condition score (Karnofsky Performance Scale (P=.000, log rank test, metastasis to internal organs (P=.0002 log rank test, and number of extraspinal bone metastases (P=.0058. Type of primary tumor was not found to be significantly associated with survival according to the revised Tokuhashi scoring system (P=.9131, log rank test. Stepwise logistic regression revealed that the Tomita score correlated more closely with survival than the Tokuhashi score. Conclusion: The patient′s performance status, extent of visceral metastasis, and extent of bone metastases are significant predictors of survival in patients with metastatic disease. Both revised Tokuhashi and Tomita scores were significantly correlated with survival. A revised Tokuhashi score of 7 or more and a Tomita score of 6 or less indicated >50% chance of surviving 6 months postoperatively. We recommend that the Tomita score be used for prognostication in patients who are contemplating surgery, as it is simpler to score and has a higher

Regression analysis for the social sciences

CERN Document Server

Gordon, Rachel A

2010-01-01

The book provides graduate students in the social sciences with the basic skills that they need to estimate, interpret, present, and publish basic regression models using contemporary standards. Key features of the book include: interweaving the teaching of statistical concepts with examples developed for the course from publicly-available social science data or drawn from the literature. thorough integration of teaching statistical theory with teaching data processing and analysis. teaching of both SAS and Stata "side-by-side" and use of chapter exercises in which students practice programming and interpretation on the same data set and course exercises in which students can choose their own research questions and data set.

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.

Survival analysis, the infinite Gaussian mixture model, FDG-PET and non-imaging data in the prediction of progression from mild cognitive impairment

OpenAIRE

Li, Rui; Perneczky, Robert; Drzezga, Alexander; Kramer, Stefan; Initiative, for the Alzheimer's Disease Neuroimaging

2015-01-01

We present a method to discover interesting brain regions in [18F] fluorodeoxyglucose positron emission tomography (PET) scans, showing also the benefits when PET scans are in combined use with non-imaging variables. The discriminative brain regions facilitate a better understanding of Alzheimer's disease (AD) progression, and they can also be used for predicting conversion from mild cognitive impairment (MCI) to AD. A survival analysis(Cox regression) and infinite Gaussian mixture model (IGM...

Composite marginal quantile regression analysis for longitudinal adolescent body mass index data.

Science.gov (United States)

Yang, Chi-Chuan; Chen, Yi-Hau; Chang, Hsing-Yi

2017-09-20

Childhood and adolescenthood overweight or obesity, which may be quantified through the body mass index (BMI), is strongly associated with adult obesity and other health problems. Motivated by the child and adolescent behaviors in long-term evolution (CABLE) study, we are interested in individual, family, and school factors associated with marginal quantiles of longitudinal adolescent BMI values. We propose a new method for composite marginal quantile regression analysis for longitudinal outcome data, which performs marginal quantile regressions at multiple quantile levels simultaneously. The proposed method extends the quantile regression coefficient modeling method introduced by Frumento and Bottai (Biometrics 2016; 72:74-84) to longitudinal data accounting suitably for the correlation structure in longitudinal observations. A goodness-of-fit test for the proposed modeling is also developed. Simulation results show that the proposed method can be much more efficient than the analysis without taking correlation into account and the analysis performing separate quantile regressions at different quantile levels. The application to the longitudinal adolescent BMI data from the CABLE study demonstrates the practical utility of our proposal. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Causal inference in survival analysis using pseudo-observations.

Science.gov (United States)

Andersen, Per K; Syriopoulou, Elisavet; Parner, Erik T

2017-07-30

Causal inference for non-censored response variables, such as binary or quantitative outcomes, is often based on either (1) direct standardization ('G-formula') or (2) inverse probability of treatment assignment weights ('propensity score'). To do causal inference in survival analysis, one needs to address right-censoring, and often, special techniques are required for that purpose. We will show how censoring can be dealt with 'once and for all' by means of so-called pseudo-observations when doing causal inference in survival analysis. The pseudo-observations can be used as a replacement of the outcomes without censoring when applying 'standard' causal inference methods, such as (1) or (2) earlier. We study this idea for estimating the average causal effect of a binary treatment on the survival probability, the restricted mean lifetime, and the cumulative incidence in a competing risks situation. The methods will be illustrated in a small simulation study and via a study of patients with acute myeloid leukemia who received either myeloablative or non-myeloablative conditioning before allogeneic hematopoetic cell transplantation. We will estimate the average causal effect of the conditioning regime on outcomes such as the 3-year overall survival probability and the 3-year risk of chronic graft-versus-host disease. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Treating experimental data of inverse kinetic method by unitary linear regression analysis

International Nuclear Information System (INIS)

Zhao Yusen; Chen Xiaoliang

2009-01-01

The theory of treating experimental data of inverse kinetic method by unitary linear regression analysis was described. Not only the reactivity, but also the effective neutron source intensity could be calculated by this method. Computer code was compiled base on the inverse kinetic method and unitary linear regression analysis. The data of zero power facility BFS-1 in Russia were processed and the results were compared. The results show that the reactivity and the effective neutron source intensity can be obtained correctly by treating experimental data of inverse kinetic method using unitary linear regression analysis and the precision of reactivity measurement is improved. The central element efficiency can be calculated by using the reactivity. The result also shows that the effect to reactivity measurement caused by external neutron source should be considered when the reactor power is low and the intensity of external neutron source is strong. (authors)

Regression analysis of informative current status data with the additive hazards model.

Science.gov (United States)

Zhao, Shishun; Hu, Tao; Ma, Ling; Wang, Peijie; Sun, Jianguo

2015-04-01

This paper discusses regression analysis of current status failure time data arising from the additive hazards model in the presence of informative censoring. Many methods have been developed for regression analysis of current status data under various regression models if the censoring is noninformative, and also there exists a large literature on parametric analysis of informative current status data in the context of tumorgenicity experiments. In this paper, a semiparametric maximum likelihood estimation procedure is presented and in the method, the copula model is employed to describe the relationship between the failure time of interest and the censoring time. Furthermore, I-splines are used to approximate the nonparametric functions involved and the asymptotic consistency and normality of the proposed estimators are established. A simulation study is conducted and indicates that the proposed approach works well for practical situations. An illustrative example is also provided.

Intratumoral heterogeneity of 18F-FLT uptake predicts proliferation and survival in patients with newly diagnosed gliomas

International Nuclear Information System (INIS)

Mitamura, Katsuya; Yamamoto, Yuka; Kudomi, Nobuyuki; Norikane, Takashi; Miyake, Keisuke; Nishiyama, Yoshihiro; Maeda, Yukito

2017-01-01

The nucleoside analog 3'-deoxy-3'- 18 F-fluorothymidine (FLT) has been investigated for evaluating tumor proliferating activity in brain tumors. We evaluated FLT uptake heterogeneity using textural features from the histogram analysis in patients with newly diagnosed gliomas and examined correlation of the results with proliferative activity and patient prognosis, in comparison with the conventional PET parameters. FLT PET was investigated in 37 patients with newly diagnosed gliomas. The conventional parameters [tumor-to-contralateral normal brain tissue (T/N) ratio and metabolic tumor volume (MTV)] and textural parameters (standard deviation, skewness, kurtosis, entropy, and uniformity) were derived from FLT PET images. Linear regression analysis was used to compare PET parameters and the proliferative activity as indicated by the Ki-67 index. The associations between parameters and overall survival (OS) were tested by Cox regression analysis. Median OS was 662 days. For the conventional parameters, linear regression analysis indicated a significant correlation between T/N ratio and Ki-67 index (p = 0.02) and MTV and Ki-67 index (p = 0.02). Among textural parameters, linear regression analysis indicated a significant correlation for kurtosis (p = 0.003), entropy (p < 0.001), and uniformity (p < 0.001) as compared to Ki-67 index, exceeding those of the conventional parameters. The results of univariate analysis suggested that skewness and kurtosis were associated with OS (p = 0.03 and 0.02, respectively). Mean survival for patients with skewness values less than 0.65 was 1462 days, compared with 917 days for those with values greater than 0.65 (p = 0.02). Mean survival for patients with kurtosis values less than 6.16 was 1616 days, compared with 882 days for those with values greater than 6.16 (p = 0.006). Based on the results of this preliminary study in a small patient population, textural features reflecting heterogeneity on FLT PET images seem to be

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....

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.

Real-time regression analysis with deep convolutional neural networks

OpenAIRE

Huerta, E. A.; George, Daniel; Zhao, Zhizhen; Allen, Gabrielle

2018-01-01

We discuss the development of novel deep learning algorithms to enable real-time regression analysis for time series data. We showcase the application of this new method with a timely case study, and then discuss the applicability of this approach to tackle similar challenges across science domains.

[Comparison of application of Cochran-Armitage trend test and linear regression analysis for rate trend analysis in epidemiology study].

Science.gov (United States)

Wang, D Z; Wang, C; Shen, C F; Zhang, Y; Zhang, H; Song, G D; Xue, X D; Xu, Z L; Zhang, S; Jiang, G H

2017-05-10

We described the time trend of acute myocardial infarction (AMI) from 1999 to 2013 in Tianjin incidence rate with Cochran-Armitage trend (CAT) test and linear regression analysis, and the results were compared. Based on actual population, CAT test had much stronger statistical power than linear regression analysis for both overall incidence trend and age specific incidence trend (Cochran-Armitage trend P valuelinear regression P value). The statistical power of CAT test decreased, while the result of linear regression analysis remained the same when population size was reduced by 100 times and AMI incidence rate remained unchanged. The two statistical methods have their advantages and disadvantages. It is necessary to choose statistical method according the fitting degree of data, or comprehensively analyze the results of two methods.

Rituximab is associated with improved survival in Burkitt lymphoma: a retrospective analysis from two US academic medical centers.

Science.gov (United States)

Wildes, Tanya M; Farrington, Laura; Yeung, Cecilia; Harrington, Alexandra M; Foyil, Kelley V; Liu, Jingxia; Kreisel, Friederike; Bartlett, Nancy L; Fenske, Timothy S

2014-02-01

Burkitt lymphoma (BL) is a rare, highly aggressive B-cell malignancy treated most successfully with brief-duration, high-intensity chemotherapeutic regimens. The benefit of the addition of rituximab to these regimens remains uncertain. We sought to examine the effectiveness of chemotherapy with and without rituximab in patients with BL. This study is a retrospective cohort study of all adult patients with BL diagnosed and treated with modern, dose-intense chemotherapeutic regimens from 1998-2008 at two tertiary care institutions. All cases were confirmed by application of WHO 2008 criteria by hematopathologists. Medical records were reviewed for patient-, disease-, and treatment- related factors as well as treatment response and survival. Factors associated with survival were analyzed using Cox proportional hazards modeling. A total of 35 patients were analyzed: 18 patients received rituximab with chemotherapy (R-chemo) and 17 received chemotherapy (chemo) alone. The median age was 42 (range 20-74 years); 57% were male; 71% had Ann Arbor Stage IV disease; 33% had central nervous system involvement; 78% had an Eastern Cooperative Oncology Group (ECOG) performance status of 0-1. R-chemo was associated with significantly longer overall survival (OS) than chemo alone (5-year OS 70% and 29%, respectively, p = 0.040). On multivariate regression analysis, poor performance status and central nervous system involvement were associated with poorer survival. The addition of rituximab to chemotherapy was associated with improved OS in patients with Burkitt lymphoma. Poor performance status and central nervous system involvement were prognostically significant on multivariate analysis.

Clinical Outcomes of Volume-Modulated Arc Therapy in 205 Patients with Nasopharyngeal Carcinoma: An Analysis of Survival and Treatment Toxicities.

Directory of Open Access Journals (Sweden)

Rui Guo

Full Text Available To investigate the clinical efficacy and treatment toxicity of volume-modulated arc therapy (VMAT for nasopharyngeal carcinoma (NPC.205 VMAT-treated NPC patients from our cancer center were prospectively entrolled. All patients received 68-70 Gy irradiation based on the planning target volume of the primary gross tumor volume. Acute and late toxicities were graded according to the Common Terminology Criteria for Adverse Events v3.0 and Radiation Therapy Oncology Group Late Radiation Morbidity Scoring Criteria.The median follow-up period was 37.3 months (range, 6.3-45.1 months. The 3-year estimated local failure-free survival, regional failure-free survival, locoregional failure-free survival, distant metastasis-free survival, disease-free survival and overall survival were 95.5%, 97.0%, 94.0%, 92.1%, 86.8% and 97.0%, respectively. Cox regression analysis showed primary gross tumor volume, N stage and EBV-DNA to be independent predictors of VMAT outcomes (P < 0.05. The most common acute and late side effects were grade 2-3 mucositis (78% and xerostomia (83%, 61%, 34%, and 9% at 3, 6, 12 and 24 months after VMAT, respectively.VMAT for the primary treatment of NPC achieved very high locoregional control with a favorable toxicity profile. The time-saving benefit of VMAT will enable more patients to receive precision radiotherapy.

Survival, causes of death, and prognostic factors in systemic sclerosis: analysis of 947 Brazilian patients.

Science.gov (United States)

Sampaio-Barros, Percival D; Bortoluzzo, Adriana B; Marangoni, Roberta G; Rocha, Luiza F; Del Rio, Ana Paula T; Samara, Adil M; Yoshinari, Natalino H; Marques-Neto, João Francisco

2012-10-01

To analyze survival, prognostic factors, and causes of death in a large cohort of patients with systemic sclerosis (SSc). From 1991 to 2010, 947 patients with SSc were treated at 2 referral university centers in Brazil. Causes of death were considered SSc-related and non-SSc-related. Multiple logistic regression analysis was used to identify prognostic factors. Survival at 5 and 10 years was estimated using the Kaplan-Meier method. One hundred sixty-eight patients died during the followup. Among the 110 deaths considered related to SSc, there was predominance of lung (48.1%) and heart (24.5%) involvement. Most of the 58 deaths not related to SSc were caused by infection, cardiovascular or cerebrovascular disease, and cancer. Male sex, modified Rodnan skin score (mRSS) > 20, osteoarticular involvement, lung involvement, and renal crisis were the main prognostic factors associated to death. Overall survival rate was 90% for 5 years and 84% for 10 years. Patients presented worse prognosis if they had diffuse SSc (85% vs 92% at 5 yrs, respectively, and 77% vs 87% at 10 yrs, compared to limited SSc), male sex (77% vs 90% at 5 yrs and 64% vs 86% at 10 yrs, compared to female sex), and mRSS > 20 (83% vs 90% at 5 yrs and 66% vs 86% at 10 yrs, compared to mRSS < 20). Survival was worse in male patients with diffuse SSc, and lung and heart involvement represented the main causes of death in this South American series of patients with SSc.

The evolution of GDP in USA using cyclic regression analysis

OpenAIRE

Catalin Angelo IOAN; Gina IOAN

2013-01-01

Based on the four major types of economic cycles (Kondratieff, Juglar, Kitchin, Kuznet), the paper aims to determine their actual length (for the U.S. economy) using cyclic regressions based on Fourier analysis.

One year Survival Rate of Ketac Molar versus Vitro Molar for Occlusoproximal ART Restorations: a RCT

Directory of Open Access Journals (Sweden)

PACHECO Anna Luisa de Brito

2017-11-01

Full Text Available Abstract Good survival rates for single-surface Atraumatic Restorative Treatment (ART restorations have been reported, while multi-surface ART restorations have not shown similar results. The aim of this study was to evaluate the survival rate of occluso-proximal ART restorations using two different filling materials: Ketac Molar EasyMix (3M ESPE and Vitro Molar (DFL. A total of 117 primary molars with occluso-proximal caries lesions were selected in 4 to 8 years old children in Barueri city, Brazil. Only one tooth was selected per child. The subjetcs were randomly allocated in two groups according to the filling material. All treatments were performed following the ART premises and all restorations were evaluated after 2, 6 and 12 months. Restoration survival was evaluated using Kaplan-Meier survival analysis and Log-rank test, while Cox regression analysis was used for testing association with clinical factors (α = 5%. There was no difference in survival rate between the materials tested, (HR = 1.60, CI = 0.98–2.62, p = 0.058. The overall survival rate of restorations was 42.74% and the survival rate per group was Ketac Molar = 50,8% and Vitro Molar G2 = 34.5%. Cox regression test showed no association between the analyzed clinical variables and the success of the restorations. After 12 months evaluation, no difference in the survival rate of ART occluso-proximal restorations was found between tested materials.

Quantile regression for the statistical analysis of immunological data with many non-detects.

Science.gov (United States)

Eilers, Paul H C; Röder, Esther; Savelkoul, Huub F J; van Wijk, Roy Gerth

2012-07-07

Immunological parameters are hard to measure. A well-known problem is the occurrence of values below the detection limit, the non-detects. Non-detects are a nuisance, because classical statistical analyses, like ANOVA and regression, cannot be applied. The more advanced statistical techniques currently available for the analysis of datasets with non-detects can only be used if a small percentage of the data are non-detects. Quantile regression, a generalization of percentiles to regression models, models the median or higher percentiles and tolerates very high numbers of non-detects. We present a non-technical introduction and illustrate it with an implementation to real data from a clinical trial. We show that by using quantile regression, groups can be compared and that meaningful linear trends can be computed, even if more than half of the data consists of non-detects. Quantile regression is a valuable addition to the statistical methods that can be used for the analysis of immunological datasets with non-detects.

Optimal choice of basis functions in the linear regression analysis

International Nuclear Information System (INIS)

Khotinskij, A.M.

1988-01-01

Problem of optimal choice of basis functions in the linear regression analysis is investigated. Step algorithm with estimation of its efficiency, which holds true at finite number of measurements, is suggested. Conditions, providing the probability of correct choice close to 1 are formulated. Application of the step algorithm to analysis of decay curves is substantiated. 8 refs

Reporting and methodological quality of survival analysis in articles published in Chinese oncology journals.

Science.gov (United States)

Zhu, Xiaoyan; Zhou, Xiaobin; Zhang, Yuan; Sun, Xiao; Liu, Haihua; Zhang, Yingying

2017-12-01

Survival analysis methods have gained widespread use in the filed of oncology. For achievement of reliable results, the methodological process and report quality is crucial. This review provides the first examination of methodological characteristics and reporting quality of survival analysis in articles published in leading Chinese oncology journals.To examine methodological and reporting quality of survival analysis, to identify some common deficiencies, to desirable precautions in the analysis, and relate advice for authors, readers, and editors.A total of 242 survival analysis articles were included to be evaluated from 1492 articles published in 4 leading Chinese oncology journals in 2013. Articles were evaluated according to 16 established items for proper use and reporting of survival analysis.The application rates of Kaplan-Meier, life table, log-rank test, Breslow test, and Cox proportional hazards model (Cox model) were 91.74%, 3.72%, 78.51%, 0.41%, and 46.28%, respectively, no article used the parametric method for survival analysis. Multivariate Cox model was conducted in 112 articles (46.28%). Follow-up rates were mentioned in 155 articles (64.05%), of which 4 articles were under 80% and the lowest was 75.25%, 55 articles were100%. The report rates of all types of survival endpoint were lower than 10%. Eleven of 100 articles which reported a loss to follow-up had stated how to treat it in the analysis. One hundred thirty articles (53.72%) did not perform multivariate analysis. One hundred thirty-nine articles (57.44%) did not define the survival time. Violations and omissions of methodological guidelines included no mention of pertinent checks for proportional hazard assumption; no report of testing for interactions and collinearity between independent variables; no report of calculation method of sample size. Thirty-six articles (32.74%) reported the methods of independent variable selection. The above defects could make potentially inaccurate

Progression-free survival, post-progression survival, and tumor response as surrogate markers for overall survival in patients with extensive small cell lung cancer

Directory of Open Access Journals (Sweden)

Hisao Imai

2015-01-01

Full Text Available Objectives: The effects of first-line chemotherapy on overall survival (OS might be confounded by subsequent therapies in patients with small cell lung cancer (SCLC. We examined whether progression-free survival (PFS, post-progression survival (PPS, and tumor response could be valid surrogate endpoints for OS after first-line chemotherapies for patients with extensive SCLC using individual-level data. Methods: Between September 2002 and November 2012, we analyzed 49 cases of patients with extensive SCLC who were treated with cisplatin and irinotecan as first-line chemotherapy. The relationships of PFS, PPS, and tumor response with OS were analyzed at the individual level. Results: Spearman rank correlation analysis and linear regression analysis showed that PPS was strongly correlated with OS (r = 0.97, p < 0.05, R 2 = 0.94, PFS was moderately correlated with OS (r = 0.58, p < 0.05, R 2 = 0.24, and tumor shrinkage was weakly correlated with OS (r = 0.37, p < 0.05, R 2 = 0.13. The best response to second-line treatment, and the number of regimens employed after progression beyond first-line chemotherapy were both significantly associated with PPS ( p ≤ 0.05. Conclusion: PPS is a potential surrogate for OS in patients with extensive SCLC. Our findings also suggest that subsequent treatment after disease progression following first-line chemotherapy may greatly influence OS.

Regression analysis for the social sciences

CERN Document Server

Gordon, Rachel A

2015-01-01

Provides graduate students in the social sciences with the basic skills they need to estimate, interpret, present, and publish basic regression models using contemporary standards. Key features of the book include: interweaving the teaching of statistical concepts with examples developed for the course from publicly-available social science data or drawn from the literature. thorough integration of teaching statistical theory with teaching data processing and analysis. teaching of Stata and use of chapter exercises in which students practice programming and interpretation on the same data set. A separate set of exercises allows students to select a data set to apply the concepts learned in each chapter to a research question of interest to them, all updated for this edition.

A Framework for RFID Survivability Requirement Analysis and Specification

Science.gov (United States)

Zuo, Yanjun; Pimple, Malvika; Lande, Suhas

Many industries are becoming dependent on Radio Frequency Identification (RFID) technology for inventory management and asset tracking. The data collected about tagged objects though RFID is used in various high level business operations. The RFID system should hence be highly available, reliable, and dependable and secure. In addition, this system should be able to resist attacks and perform recovery in case of security incidents. Together these requirements give rise to the notion of a survivable RFID system. The main goal of this paper is to analyze and specify the requirements for an RFID system to become survivable. These requirements, if utilized, can assist the system in resisting against devastating attacks and recovering quickly from damages. This paper proposes the techniques and approaches for RFID survivability requirements analysis and specification. From the perspective of system acquisition and engineering, survivability requirement is the important first step in survivability specification, compliance formulation, and proof verification.

A taylor series approach to survival analysis

International Nuclear Information System (INIS)

Brodsky, J.B.; Groer, P.G.

1984-09-01

A method of survival analysis using hazard functions is developed. The method uses the well known mathematical theory for Taylor Series. Hypothesis tests of the adequacy of many statistical models, including proportional hazards and linear and/or quadratic dose responses, are obtained. A partial analysis of leukemia mortality in the Life Span Study cohort is used as an example. Furthermore, a relatively robust estimation procedure for the proportional hazards model is proposed. (author)

A method for nonlinear exponential regression analysis

Science.gov (United States)

Junkin, B. G.

1971-01-01

A computer-oriented technique is presented for performing a nonlinear exponential regression analysis on decay-type experimental data. The technique involves the least squares procedure wherein the nonlinear problem is linearized by expansion in a Taylor series. A linear curve fitting procedure for determining the initial nominal estimates for the unknown exponential model parameters is included as an integral part of the technique. A correction matrix was derived and then applied to the nominal estimate to produce an improved set of model parameters. The solution cycle is repeated until some predetermined criterion is satisfied.

Analysis of Functional Data with Focus on Multinomial Regression and Multilevel Data

DEFF Research Database (Denmark)

Mousavi, Seyed Nourollah

Functional data analysis (FDA) is a fast growing area in statistical research with increasingly diverse range of application from economics, medicine, agriculture, chemometrics, etc. Functional regression is an area of FDA which has received the most attention both in aspects of application...... and methodological development. Our main Functional data analysis (FDA) is a fast growing area in statistical research with increasingly diverse range of application from economics, medicine, agriculture, chemometrics, etc. Functional regression is an area of FDA which has received the most attention both in aspects...

Regression analysis of a chemical reaction fouling model

International Nuclear Information System (INIS)

Vasak, F.; Epstein, N.

1996-01-01

A previously reported mathematical model for the initial chemical reaction fouling of a heated tube is critically examined in the light of the experimental data for which it was developed. A regression analysis of the model with respect to that data shows that the reference point upon which the two adjustable parameters of the model were originally based was well chosen, albeit fortuitously. (author). 3 refs., 2 tabs., 2 figs

Sensitivity analysis and optimization of system dynamics models : Regression analysis and statistical design of experiments

NARCIS (Netherlands)

Kleijnen, J.P.C.

1995-01-01

This tutorial discusses what-if analysis and optimization of System Dynamics models. These problems are solved, using the statistical techniques of regression analysis and design of experiments (DOE). These issues are illustrated by applying the statistical techniques to a System Dynamics model for

Neutrophil-to-lymphocyte ratio as an independent predictor for survival in patients with localized clear cell renal cell carcinoma after radiofrequency ablation: a propensity score matching analysis.

Science.gov (United States)

Chang, Xiaofeng; Zhang, Fan; Liu, Tieshi; Wang, Wei; Guo, Hongqian

2017-06-01

To investigate the role of neutrophil-to-lymphocyte ratio as a prognostic indicator in patients with localized clear cell renal cell carcinoma treated with radiofrequency ablation. We retrospectively analyzed data from patients with renal cell carcinoma who underwent radiofrequency ablation from 2006 to 2013. The Kaplan-Meier method was used to generate the survival curves according to different categories of neutrophil-to-lymphocyte ratio. Relationships between preoperative neutrophil-to-lymphocyte ratio or the change of neutrophil-to-lymphocyte ratio and survival were evaluated with multivariable Cox proportional hazards regression analysis. A propensity score matching analysis was carried out to avoid confounding bias. A total of 185 patients were included in present study. When stratified by preoperative neutrophil-to-lymphocyte ratio cutoff value of 2.79, 5-year recurrence-free survival, 5-year disease-free survival, and 5-year overall survival rates of neutrophil-to-lymphocyte ratio analysis, 5-year recurrence-free survival, 5-year disease-free survival, and 5-year overall survival rates of neutrophil-to-lymphocyte ratio ratio with the change of neutrophil-to-lymphocyte ratio, patients with both preoperative neutrophil-to-lymphocyte ratio ≥2.79 and the change of neutrophil-to-lymphocyte ratio ≥0.40 had the worst disease-free survival. Results of multivariable analysis showed that preoperative neutrophil-to-lymphocyte ratio and the change of neutrophil-to-lymphocyte ratio correlated with cancer relapse remarkably. High preoperative neutrophil-to-lymphocyte ratio and elevated postoperative neutrophil-to-lymphocyte ratio are associated with significant increase in risk of local recurrence as well as distant metastasis. The combination of neutrophil-to-lymphocyte ratio with the other prognostic indicators can be applied in the evaluation of relapse risk in patients with clear cell renal cell carcinoma after radiofrequency ablation.

Application of multilinear regression analysis in modeling of soil ...

African Journals Online (AJOL)

The application of Multi-Linear Regression Analysis (MLRA) model for predicting soil properties in Calabar South offers a technical guide and solution in foundation designs problems in the area. Forty-five soil samples were collected from fifteen different boreholes at a different depth and 270 tests were carried out for CBR, ...

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.

Brachytherapy Improves Survival in Stage III Endometrial Cancer With Cervical Involvement

Energy Technology Data Exchange (ETDEWEB)

Bingham, Brian [Department of Radiation Oncology, Vanderbilt University, Nashville, Tennessee (United States); Orton, Andrew; Boothe, Dustin [Department of Radiation Oncology, University of Utah, Salt Lake City, Utah (United States); Stoddard, Greg [Division of Epidemiology, University of Utah, Salt Lake City, Utah (United States); Huang, Y. Jessica; Gaffney, David K. [Department of Radiation Oncology, University of Utah, Salt Lake City, Utah (United States); Poppe, Matthew M., E-mail: Matthew.poppe@hci.utah.edu [Department of Radiation Oncology, University of Utah, Salt Lake City, Utah (United States)

2017-04-01

Purpose: To evaluate the survival benefit of adding vaginal brachytherapy (BT) to pelvic external beam radiation therapy (EBRT) in women with stage III endometrial cancer. Methods and Materials: The National Cancer Data Base was used to identify patients with stage III endometrial cancer from 2004 to 2013. Only women who received adjuvant EBRT were analyzed. Women were grouped according to receipt of BT. Logistic regression modeling was used to identify predictors of receiving BT. Log–rank statistics were used to compare survival outcomes. Cox proportional hazards modeling was used to evaluate the effect of BT on survival. A propensity score–matched analysis was also conducted among women with cervical involvement. Results: We evaluated 12,988 patients with stage III endometrial carcinoma, 39% of whom received EBRT plus BT. Women who received BT were more likely to have endocervical or cervical stromal involvement (odds ratios 2.03 and 1.77; P<.01, respectively). For patients receiving EBRT alone, the 5-year survival was 66% versus 69% with the addition of BT at 5 years (P<.01). Brachytherapy remained significantly predictive of decreased risk of death (hazard ratio 0.86; P<.01) on multivariate Cox regression. The addition of BT to EBRT did not affect survival among women without cervical involvement (P=.84). For women with endocervical or cervical stromal invasion, the addition of BT significantly improved survival (log–rank P<.01). Receipt of EBRT plus BT was associated with improved survival in women with positive and negative surgical margins, and receiving chemotherapy did not alter the benefit of BT. Propensity score–matched analysis results confirmed the benefit of BT among women with cervical involvement (hazard ratio 0.80; P=.01). Conclusions: In this population of women with stage III endometrial cancer the addition of BT to EBRT was associated with an improvement in survival for women with endocervical or cervical stromal invasion.

Brachytherapy Improves Survival in Stage III Endometrial Cancer With Cervical Involvement

International Nuclear Information System (INIS)

Bingham, Brian; Orton, Andrew; Boothe, Dustin; Stoddard, Greg; Huang, Y. Jessica; Gaffney, David K.; Poppe, Matthew M.

2017-01-01

Purpose: To evaluate the survival benefit of adding vaginal brachytherapy (BT) to pelvic external beam radiation therapy (EBRT) in women with stage III endometrial cancer. Methods and Materials: The National Cancer Data Base was used to identify patients with stage III endometrial cancer from 2004 to 2013. Only women who received adjuvant EBRT were analyzed. Women were grouped according to receipt of BT. Logistic regression modeling was used to identify predictors of receiving BT. Log–rank statistics were used to compare survival outcomes. Cox proportional hazards modeling was used to evaluate the effect of BT on survival. A propensity score–matched analysis was also conducted among women with cervical involvement. Results: We evaluated 12,988 patients with stage III endometrial carcinoma, 39% of whom received EBRT plus BT. Women who received BT were more likely to have endocervical or cervical stromal involvement (odds ratios 2.03 and 1.77; P<.01, respectively). For patients receiving EBRT alone, the 5-year survival was 66% versus 69% with the addition of BT at 5 years (P<.01). Brachytherapy remained significantly predictive of decreased risk of death (hazard ratio 0.86; P<.01) on multivariate Cox regression. The addition of BT to EBRT did not affect survival among women without cervical involvement (P=.84). For women with endocervical or cervical stromal invasion, the addition of BT significantly improved survival (log–rank P<.01). Receipt of EBRT plus BT was associated with improved survival in women with positive and negative surgical margins, and receiving chemotherapy did not alter the benefit of BT. Propensity score–matched analysis results confirmed the benefit of BT among women with cervical involvement (hazard ratio 0.80; P=.01). Conclusions: In this population of women with stage III endometrial cancer the addition of BT to EBRT was associated with an improvement in survival for women with endocervical or cervical stromal invasion.

Bias due to two-stage residual-outcome regression analysis in genetic association studies.

Science.gov (United States)

Demissie, Serkalem; Cupples, L Adrienne

2011-11-01

Association studies of risk factors and complex diseases require careful assessment of potential confounding factors. Two-stage regression analysis, sometimes referred to as residual- or adjusted-outcome analysis, has been increasingly used in association studies of single nucleotide polymorphisms (SNPs) and quantitative traits. In this analysis, first, a residual-outcome is calculated from a regression of the outcome variable on covariates and then the relationship between the adjusted-outcome and the SNP is evaluated by a simple linear regression of the adjusted-outcome on the SNP. In this article, we examine the performance of this two-stage analysis as compared with multiple linear regression (MLR) analysis. Our findings show that when a SNP and a covariate are correlated, the two-stage approach results in biased genotypic effect and loss of power. Bias is always toward the null and increases with the squared-correlation between the SNP and the covariate (). For example, for , 0.1, and 0.5, two-stage analysis results in, respectively, 0, 10, and 50% attenuation in the SNP effect. As expected, MLR was always unbiased. Since individual SNPs often show little or no correlation with covariates, a two-stage analysis is expected to perform as well as MLR in many genetic studies; however, it produces considerably different results from MLR and may lead to incorrect conclusions when independent variables are highly correlated. While a useful alternative to MLR under , the two -stage approach has serious limitations. Its use as a simple substitute for MLR should be avoided. © 2011 Wiley Periodicals, Inc.

Vitamin K2 regression aortic calcification induced by warfarin via Gas6/Axl survival pathway in rats.

Science.gov (United States)

Jiang, Xiaoyu; Tao, Huiren; Qiu, Cuiting; Ma, Xiaolei; Li, Shan; Guo, Xian; Lv, Anlin; Li, Huan

2016-09-05

The aim of this study was to investigate the effect of vitamin K2 on aortic calcification induced by warfarin via Gas6/Axl survival pathway in rats. A calcification model was established by administering 3mg/g warfarin to rats. Rats were divided into 9 groups: control group (0W, 4W, 6W and 12W groups), 4W calcification group, 6W calcification group, 12W calcification group, 6W calcification+6W normal group and 6W calcification+6W vitamin K2 group. Alizarin red S staining measured aortic calcium depositions; alkaline phosphatase activity in serum was measured by a kit; apoptosis was evaluated by TUNEL assay; protein expression levels of Gas6, Axl, phosphorylated Akt (p-Akt), and Bcl-2 were determined by western blotting. The calcium content, calcium depositions, ALP activity and apoptosis were significantly higher in the calcification groups than control group. Gas6, Axl, p-Akt and Bcl-2 expression was lower in the calcification group than control group. 100μg/g vitamin K2 treatment decreased calcium depositions, ALP activity and apoptosis significantly, but increased Gas6, Axl, p-Akt and Bcl-2 expression. 100μg/g vitamin K2 reversed 44% calcification. Pearson correlation analysis showed a positive correlation between formation calcification and apoptosis (R(2)=0.8853, PK2 can inhibit warfarin-induced aortic calcification and apoptosis. The regression of aortic calcification by vitamin K2 involved the Gas6/Axl axis. This data may provide a theoretical basis for future clinical treatments for aortic calcification. Copyright © 2016 Elsevier B.V. All rights reserved.

Prognostic classification index in Iranian colorectal cancer patients: Survival tree analysis

Directory of Open Access Journals (Sweden)

Amal Saki Malehi

2016-01-01

Full Text Available Aims: The aim of this study was to determine the prognostic index for separating homogenous subgroups in colorectal cancer (CRC patients based on clinicopathological characteristics using survival tree analysis. Methods: The current study was conducted at the Research Center of Gastroenterology and Liver Disease, Shahid Beheshti Medical University in Tehran, between January 2004 and January 2009. A total of 739 patients who already have been diagnosed with CRC based on pathologic report were enrolled. The data included demographic and clinical-pathological characteristic of patients. Tree-structured survival analysis based on a recursive partitioning algorithm was implemented to evaluate prognostic factors. The probability curves were calculated according to the Kaplan-Meier method, and the hazard ratio was estimated as an interest effect size. Result: There were 526 males (71.2% of these patients. The mean survival time (from diagnosis time was 42.46± (3.4. Survival tree identified three variables as main prognostic factors and based on their four prognostic subgroups was constructed. The log-rank test showed good separation of survival curves. Patients with Stage I-IIIA and treated with surgery as the first treatment showed low risk (median = 34 months whereas patients with stage IIIB, IV, and more than 68 years have the worse survival outcome (median = 9.5 months. Conclusion: Constructing the prognostic classification index via survival tree can aid the researchers to assess interaction between clinical variables and determining the cumulative effect of these variables on survival outcome.

PROGNOSTIC FACTORS FOR SURVIVAL IN PATIENTS WITH METASTATIC COLORECTAL CANCER TREATED WITH FIRST - LINE CHEMOTHERAPY

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Deyan Davidov

2017-05-01

Full Text Available Objective: The aim of this study was to investigate the prognostic significance for survival of certain clinical and pathological factors in patients with advanced or metastatic colorectal carcinoma (CRC treated with first- line chemotherapy. Methods: From 2002 to 2011 seventy- four consecutive patients with advanced or metastatic CRC, treated in UMHAT- Dr. G. Stranski, Department of Medical Oncology entered the study. Some patient’s characteristics, hematological and pathological parameters, were evaluated for their role as predictors of overall survival. The therapeutic regimens included FOLFOX or FOlFIRI. Survival analysis was evaluated by Kaplan- Meier test. The influence of pretreatment characteristics as prognostic factor for survival was analyzed using multivariate stepwise Cox regression analyses. Results: In multivariate analysis a significant correlation was exhibited between survival, poor performance status and multiple sites of metastasis. Variables significantly associated with overall survival in univariate analysis were performance status>1, thrombocytosis, anemia and number of metastatic sites >1. Conclusion: These results indicated that poor performance status, anemia, thrombocytosis as well as multiple site of metastasis could be useful prognostic factors in patients with metastatic CRC.

Molecular Signature for Lymphatic Invasion Associated with Survival of Epithelial Ovarian Cancer.

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Paik, E Sun; Choi, Hyun Jin; Kim, Tae-Joong; Lee, Jeong-Won; Kim, Byoung-Gie; Bae, Duk-Soo; Choi, Chel Hun

2018-04-01

We aimed to develop molecular classifier that can predict lymphatic invasion and their clinical significance in epithelial ovarian cancer (EOC) patients. We analyzed gene expression (mRNA, methylated DNA) in data from The Cancer Genome Atlas. To identify molecular signatures for lymphatic invasion, we found differentially expressed genes. The performance of classifier was validated by receiver operating characteristics analysis, logistic regression, linear discriminant analysis (LDA), and support vector machine (SVM). We assessed prognostic role of classifier using random survival forest (RSF) model and pathway deregulation score (PDS). For external validation,we analyzed microarray data from 26 EOC samples of Samsung Medical Center and curatedOvarianData database. We identified 21 mRNAs, and seven methylated DNAs from primary EOC tissues that predicted lymphatic invasion and created prognostic models. The classifier predicted lymphatic invasion well, which was validated by logistic regression, LDA, and SVM algorithm (C-index of 0.90, 0.71, and 0.74 for mRNA and C-index of 0.64, 0.68, and 0.69 for DNA methylation). Using RSF model, incorporating molecular data with clinical variables improved prediction of progression-free survival compared with using only clinical variables (p < 0.001 and p=0.008). Similarly, PDS enabled us to classify patients into high-risk and low-risk group, which resulted in survival difference in mRNA profiles (log-rank p-value=0.011). In external validation, gene signature was well correlated with prediction of lymphatic invasion and patients' survival. Molecular signature model predicting lymphatic invasion was well performed and also associated with survival of EOC patients.

Lean body mass predicts long-term survival in Chinese patients on peritoneal dialysis.

Directory of Open Access Journals (Sweden)

Jenq-Wen Huang

Full Text Available BACKGROUND: Reduced lean body mass (LBM is one of the main indicators in malnutrition inflammation syndrome among patients on dialysis. However, the influence of LBM on peritoneal dialysis (PD patients' outcomes and the factors related to increasing LBM are seldom reported. METHODS: We enrolled 103 incident PD patients between 2002 and 2003, and followed them until December 2011. Clinical characteristics, PD-associated parameters, residual renal function, and serum chemistry profiles of each patient were collected at 1 month and 1 year after initiating PD. LBM was estimated using creatinine index corrected with body weight. Multiple linear regression analysis, Kaplan-Meier survival analysis, and Cox regression proportional hazard analysis were used to define independent variables and compare survival between groups. RESULTS: Using the median LBM value (70% for men and 64% for women, patients were divided into group 1 (n = 52; low LBM and group 2 (n = 51; high LBM. Group 1 patients had higher rates of peritonitis (1.6 vs. 1.1/100 patient months; p<0.05 and hospitalization (14.6 vs. 9.7/100 patient months; p<0.05. Group 1 patients also had shorter overall survival and technique survival (p<0.01. Each percentage point increase in LBM reduced the hazard ratio for mortality by 8% after adjustment for diabetes, age, sex, and body mass index (BMI. Changes in residual renal function and protein catabolic rate were independently associated with changes in LBM in the first year of PD. CONCLUSIONS: LBM serves as a good parameter in addition to BMI to predict the survival of patients on PD. Preserving residual renal function and increasing protein intake can increase LBM.

An Original Stepwise Multilevel Logistic Regression Analysis of Discriminatory Accuracy

DEFF Research Database (Denmark)

Merlo, Juan; Wagner, Philippe; Ghith, Nermin

2016-01-01

BACKGROUND AND AIM: Many multilevel logistic regression analyses of "neighbourhood and health" focus on interpreting measures of associations (e.g., odds ratio, OR). In contrast, multilevel analysis of variance is rarely considered. We propose an original stepwise analytical approach that disting...

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.

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

Mediation analysis of the relationship between institutional research activity and patient survival

DEFF Research Database (Denmark)

Rochon, Justine; du Bois, Andreas; Lange, Theis

2014-01-01

BACKGROUND: Recent studies have suggested that patients treated in research-active institutions have better outcomes than patients treated in research-inactive institutions. However, little attention has been paid to explaining such effects, probably because techniques for mediation analysis...... existing so far have not been applicable to survival data. METHODS: We investigated the underlying mechanisms using a recently developed method for mediation analysis of survival data. Our analysis of the effect of research activity on patient survival was based on 352 patients who had been diagnosed...... mediated through either optimal surgery or chemotherapy. Taken together, about 26% of the beneficial effect of research activity was mediated through the proposed pathways. CONCLUSIONS: Mediation analysis allows proceeding from the question "Does it work?" to the question "How does it work?" In particular...

Clustered survival data with left-truncation

DEFF Research Database (Denmark)

Eriksson, Frank; Martinussen, Torben; Scheike, Thomas H.

2015-01-01

Left-truncation occurs frequently in survival studies, and it is well known how to deal with this for univariate survival times. However, there are few results on how to estimate dependence parameters and regression effects in semiparametric models for clustered survival data with delayed entry....... Surprisingly, existing methods only deal with special cases. In this paper, we clarify different kinds of left-truncation and suggest estimators for semiparametric survival models under specific truncation schemes. The large-sample properties of the estimators are established. Small-sample properties...

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.

The use of cognitive ability measures as explanatory variables in regression analysis.

Science.gov (United States)

Junker, Brian; Schofield, Lynne Steuerle; Taylor, Lowell J

2012-12-01

Cognitive ability measures are often taken as explanatory variables in regression analysis, e.g., as a factor affecting a market outcome such as an individual's wage, or a decision such as an individual's education acquisition. Cognitive ability is a latent construct; its true value is unobserved. Nonetheless, researchers often assume that a test score , constructed via standard psychometric practice from individuals' responses to test items, can be safely used in regression analysis. We examine problems that can arise, and suggest that an alternative approach, a "mixed effects structural equations" (MESE) model, may be more appropriate in many circumstances.

Epidermal growth factor receptor: an independent predictor of survival in astrocytic tumors given definitive irradiation

International Nuclear Information System (INIS)

An Zhu; Shaeffer, James; Leslie, Susan; Kolm, Paul; El-Mahdi, Anas M.

1996-01-01

Purpose: To determine whether the expression of epidermal growth factor receptor (EGFR) protein was predictive of patient survival independently of other prognostic factors in astrocytic tumors. Methods and Materials: Epidermal growth factor receptor protein expression was investigated immunohistochemically in formalin-fixed, paraffin-embedded surgical specimens of 55 glioblastoma multiforme, 14 anaplastic astrocytoma, and 2 astrocytomas given definitive irradiation. We evaluated the relationship of EGFR protein expression and tumor grade, histologic features, age at diagnosis, sex, patient survival, and recurrence-free survival. Results: The percentage of tumor cells which were EGFR positive related to reduced survival by Cox regression analysis in both univariate (p = 0.0424) and multivariate analysis (p = 0.0016). Epidermal growth factor receptor positivity was the only 1 of 11 clinical and histological variables associated with decreased recurrence-free survival by either univariate (p = 0.0353) or multivariate (p = 0.0182) analysis. Epidermal growth factor receptor protein expression was not related to patient age, sex, or histologic features. Conclusion: Epidermal growth factor receptor positivity was a significant and independent prognostic indicator for overall survival and recurrence-free survival for irradiated patients with astrocytic gliomas

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

Neighborhood social capital and crime victimization: comparison of spatial regression analysis and hierarchical regression analysis.

Science.gov (United States)

Takagi, Daisuke; Ikeda, Ken'ichi; Kawachi, Ichiro

2012-11-01

Crime is an important determinant of public health outcomes, including quality of life, mental well-being, and health behavior. A body of research has documented the association between community social capital and crime victimization. The association between social capital and crime victimization has been examined at multiple levels of spatial aggregation, ranging from entire countries, to states, metropolitan areas, counties, and neighborhoods. In multilevel analysis, the spatial boundaries at level 2 are most often drawn from administrative boundaries (e.g., Census tracts in the U.S.). One problem with adopting administrative definitions of neighborhoods is that it ignores spatial spillover. We conducted a study of social capital and crime victimization in one ward of Tokyo city, using a spatial Durbin model with an inverse-distance weighting matrix that assigned each respondent a unique level of "exposure" to social capital based on all other residents' perceptions. The study is based on a postal questionnaire sent to 20-69 years old residents of Arakawa Ward, Tokyo. The response rate was 43.7%. We examined the contextual influence of generalized trust, perceptions of reciprocity, two types of social network variables, as well as two principal components of social capital (constructed from the above four variables). Our outcome measure was self-reported crime victimization in the last five years. In the spatial Durbin model, we found that neighborhood generalized trust, reciprocity, supportive networks and two principal components of social capital were each inversely associated with crime victimization. By contrast, a multilevel regression performed with the same data (using administrative neighborhood boundaries) found generally null associations between neighborhood social capital and crime. Spatial regression methods may be more appropriate for investigating the contextual influence of social capital in homogeneous cultural settings such as Japan. Copyright

Complete hazard ranking to analyze right-censored data: An ALS survival study.

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Huang, Zhengnan; Zhang, Hongjiu; Boss, Jonathan; Goutman, Stephen A; Mukherjee, Bhramar; Dinov, Ivo D; Guan, Yuanfang

2017-12-01

Survival analysis represents an important outcome measure in clinical research and clinical trials; further, survival ranking may offer additional advantages in clinical trials. In this study, we developed GuanRank, a non-parametric ranking-based technique to transform patients' survival data into a linear space of hazard ranks. The transformation enables the utilization of machine learning base-learners including Gaussian process regression, Lasso, and random forest on survival data. The method was submitted to the DREAM Amyotrophic Lateral Sclerosis (ALS) Stratification Challenge. Ranked first place, the model gave more accurate ranking predictions on the PRO-ACT ALS dataset in comparison to Cox proportional hazard model. By utilizing right-censored data in its training process, the method demonstrated its state-of-the-art predictive power in ALS survival ranking. Its feature selection identified multiple important factors, some of which conflicts with previous studies.

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

Accelerated failure time regression for backward recurrence times and current durations

DEFF Research Database (Denmark)

Keiding, N; Fine, J P; Hansen, O H

2011-01-01

Backward recurrence times in stationary renewal processes and current durations in dynamic populations observed at a cross-section may yield estimates of underlying interarrival times or survival distributions under suitable stationarity assumptions. Regression models have been proposed for these......Backward recurrence times in stationary renewal processes and current durations in dynamic populations observed at a cross-section may yield estimates of underlying interarrival times or survival distributions under suitable stationarity assumptions. Regression models have been proposed...... for these situations, but accelerated failure time models have the particularly attractive feature that they are preserved when going from the backward recurrence times to the underlying survival distribution of interest. This simple fact has recently been noticed in a sociological context and is here illustrated...... by a study of current duration of time to pregnancy...

A multi-year analysis of passage and survival at McNary Dam, 2004-09

Science.gov (United States)

Adams, Noah S.; Walker, C.E.; Perry, R.W.

2011-01-01

We analyzed 6 years (2004–09) of passage and survival data collected at McNary Dam to determine how dam operations and environmental conditions affect passage and survival of juvenile salmonids. A multinomial logistic regression was used to examine how environmental variables and dam operations relate to passage behavior of juvenile salmonids at McNary Dam. We used the Cormack-Jolly-Seber release-recapture model to determine how the survival of juvenile salmonids passing through McNary Dam relates to environmental variables and dam operations. Total project discharge and the proportion of flow passing the spillway typically had a positive effect on survival for all species and routes. As the proportion of water through the spillway increased, the number of fish passing the spillway increased, as did overall survival. Additionally, survival generally was higher at night. There was no meaningful difference in survival for fish that passed through the north or south portions of the spillway or powerhouse. Similarly, there was no difference in survival for fish released in the north, middle, or south portions of the tailrace. For subyearling Chinook salmon migrating during the summer season, increased temperatures had a drastic effect on passage and survival. As temperature increased, survival of subyearling Chinook salmon decreased through all passage routes and the number of fish that passed through the turbines increased. During years when the temporary spillway weirs (TSWs) were installed, passage through the spillway increased for spring migrants. However, due to the changes made in the location of the TSW between years and the potential effect of other confounding environmental conditions, it is not certain if the increase in spillway passage was due solely to the presence of the TSWs. The TSWs appeared to improve forebay survival during years when they were operated.

Stereotactic Radiosurgery in the Management of Brain Metastases: An Institutional Retrospective Analysis of Survival

International Nuclear Information System (INIS)

Frazier, James L.; Batra, Sachin; Kapor, Sumit; Vellimana, Ananth; Gandhi, Rahul; Carson, Kathryn A.; Shokek, Ori; Lim, Michael; Kleinberg, Lawrence; Rigamonti, Daniele

2010-01-01

Purpose: The objective of this study was to report our experience with stereotactic radiosurgery performed with the Gamma Knife (GK) in the treatment of patients with brain metastases and to compare survival for those treated with radiosurgery alone with survival for those treated with radiosurgery and whole-brain radiotherapy. Methods and Materials: Prospectively collected demographic and clinical characteristics and treatment and survival data on 237 patients with intracranial metastases who underwent radiosurgery with the GK between 2003 and 2007 were reviewed. Kaplan-Meier and Cox proportional hazards regression analyses were used to compare survival by demographic and clinical characteristics and treatment. Results: The mean age of the patient population was 56 years. The most common tumor histologies were non-small-cell lung carcinoma (34.2%) and breast cancer (13.9%). The median overall survival time was 8.5 months from the time of treatment. The median survival times for patients with one, two/three, and four or more brain metastases were 8.5, 9.4, and 6.7 months, respectively. Patients aged 65 years or greater and those aged less than 65 years had median survival times of 7.8 and 9 months, respectively (p = 0.008). The Karnofsky Performance Score (KPS) at the time of treatment was a significant predictor of survival: those patients with a KPS of 70 or less had a median survival of 2.9 months compared with 10.3 months (p = 0.034) for those with a KPS of 80 or greater. There was no statistically significant difference in survival between patients treated with radiosurgery alone and those treated with radiosurgery plus whole-brain radiotherapy. Conclusions: Radiosurgery with the GK is an efficacious treatment modality for brain metastases. A KPS greater than 70, histology of breast cancer, smaller tumor volume, and age less than 65 years were associated with a longer median survival in our study.

Factors affecting the survival of the “at risk” newborn at Korle Bu ...

African Journals Online (AJOL)

The type of delivery other than the spontaneous vaginal route also affects the outcome, though the relationship was not statistically significant. Logistic regression analysis showed that maturity, birthweight and time from birth to admission to NICU were the most significant factors associated with the survival of the neonate.

Using Survival Analysis to Understand Patterns of Sustainment within a System-Driven Implementation of Multiple Evidence-Based Practices for Children’s Mental Health Services

Directory of Open Access Journals (Sweden)

Lauren Brookman-Frazee

2018-03-01

Full Text Available Evidence-based practice (EBP implementation requires substantial resources in workforce training; yet, failure to achieve long-term sustainment can result in poor return on investment. There is limited research on EBP sustainment in mental health services long after implementation. This study examined therapists’ continued vs. discontinued practice delivery based on administrative claims for reimbursement for six EBPs [Cognitive Behavioral Interventions for Trauma in Schools (CBITS, Child–Parent Psychotherapy, Managing and Adapting Practices (MAP, Seeking Safety (SS, Trauma-Focused Cognitive Behavior Therapy (TF-CBT, and Positive Parenting Program] adopted in a system-driven implementation effort in public mental health services for children. Our goal was to identify agency and therapist factors associated with a sustained EBP delivery. Survival analysis (i.e., Kaplan–Meier survival functions, log-rank tests, and Cox regressions was used to analyze 19 fiscal quarters (i.e., approximately 57 months of claims data from the Prevention and Early Intervention Transformation within the Los Angeles County Department of Mental Health. These data comprised 2,322,389 claims made by 6,873 therapists across 88 agencies. Survival time was represented by the time elapsed from therapists’ first to final claims for each practice and for any of the six EBPs. Results indicate that therapists continued to deliver at least one EBP for a mean survival time of 21.73 months (median = 18.70. When compared to a survival curve of the five other EBPs, CBITS, SS, and TP demonstrated a higher risk of delivery discontinuation, whereas MAP and TF-CBT demonstrated a lower risk of delivery discontinuation. A multivariate Cox regression model revealed that agency (centralization and service setting and therapist (demographics, discipline, and case-mix characteristics characteristics were significantly associated with risk of delivery discontinuation for any of

Analysis of γ spectra in airborne radioactivity measurements using multiple linear regressions

International Nuclear Information System (INIS)

Bao Min; Shi Quanlin; Zhang Jiamei

2004-01-01

This paper describes the net peak counts calculating of nuclide 137 Cs at 662 keV of γ spectra in airborne radioactivity measurements using multiple linear regressions. Mathematic model is founded by analyzing every factor that has contribution to Cs peak counts in spectra, and multiple linear regression function is established. Calculating process adopts stepwise regression, and the indistinctive factors are eliminated by F check. The regression results and its uncertainty are calculated using Least Square Estimation, then the Cs peak net counts and its uncertainty can be gotten. The analysis results for experimental spectrum are displayed. The influence of energy shift and energy resolution on the analyzing result is discussed. In comparison with the stripping spectra method, multiple linear regression method needn't stripping radios, and the calculating result has relation with the counts in Cs peak only, and the calculating uncertainty is reduced. (authors)

Regression Analysis and Calibration Recommendations for the Characterization of Balance Temperature Effects

Science.gov (United States)

Ulbrich, N.; Volden, T.

2018-01-01

Analysis and use of temperature-dependent wind tunnel strain-gage balance calibration data are discussed in the paper. First, three different methods are presented and compared that may be used to process temperature-dependent strain-gage balance data. The first method uses an extended set of independent variables in order to process the data and predict balance loads. The second method applies an extended load iteration equation during the analysis of balance calibration data. The third method uses temperature-dependent sensitivities for the data analysis. Physical interpretations of the most important temperature-dependent regression model terms are provided that relate temperature compensation imperfections and the temperature-dependent nature of the gage factor to sets of regression model terms. Finally, balance calibration recommendations are listed so that temperature-dependent calibration data can be obtained and successfully processed using the reviewed analysis methods.

Survival and clinical outcome of dogs with ischaemic stroke

DEFF Research Database (Denmark)

Gredal, Hanne Birgit; Toft, Nils; Westrup, Ulrik

2013-01-01

The objectives of the present study were to investigate survival time, possible predictors of survival and clinical outcome in dogs with ischaemic stroke. A retrospective study of dogs with a previous diagnosis of ischaemic stroke diagnosed by magnetic resonance imaging (MRI) was performed....... The association between survival and the hypothesised risk factors was examined using univariable exact logistic regression. Survival was examined using Kaplan-Meier and Cox regression. Twenty-two dogs were identified. Five dogs (23%) died within the first 30days of the stroke event. Median survival in 30-day...... survivors was 505days. Four dogs (18%) were still alive by the end of the study. Right-sided lesions posed a significantly increased risk of mortality with a median survival time in dogs with right-sided lesions of 24days vs. 602days in dogs with left sided lesions (P=0.006). Clinical outcome was considered...

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

Selective principal component regression analysis of fluorescence hyperspectral image to assess aflatoxin contamination in corn

Science.gov (United States)

Selective principal component regression analysis (SPCR) uses a subset of the original image bands for principal component transformation and regression. For optimal band selection before the transformation, this paper used genetic algorithms (GA). In this case, the GA process used the regression co...

Epidermal Growth Factor Receptor Is Related to Poor Survival in Glioblastomas: Single-Institution Experience

Science.gov (United States)

Choi, Youngmin; Lee, Hyung-Sik; Hur, Won-Joo; Sung, Ki-Han; Kim, Ki-Uk; Choi, Sun-Seob; Kim, Su-Jin; Kim, Dae-Cheol

2013-01-01

Purpose There are conflicting results surrounding the prognostic significance of epidermal growth factor receptor (EGFR) status in glioblastoma (GBM) patients. Accordingly, we attempted to assess the influence of EGFR expression on the survival of GBM patients receiving postoperative radiotherapy. Materials and Methods Thirty three GBM patients who had received surgery and postoperative radiotherapy at our institute, between March 1997 and February 2006, were included. The evaluation of EGFR expression with immunohistochemistry was available for 30 patients. Kaplan-Meier survival analysis and Cox regression were used for statistical analysis. Results EGFR was expressed in 23 patients (76.7%), and not expressed in seven (23.3%). Survival in EGFR expressing GBM patients was significantly less than that in non-expressing patients (median survival: 12.5 versus 17.5 months, p=0.013). Patients who received more than 60 Gy showed improved survival over those who received up to 60 Gy (median survival: 17.0 versus 9.0 months, p=0.000). Negative EGFR expression and a higher radiation dose were significantly correlated with improved survival on multivariate analysis. Survival rates showed no differences according to age, sex, and surgical extent. Conclusion The expression of EGFR demonstrated a significantly deleterious effect on the survival of GBM patients. Therefore, approaches targeting EGFR should be considered in potential treatment methods for GBM patients, in addition to current management strategies. PMID:23225805

Determining Balıkesir’s Energy Potential Using a Regression Analysis Computer Program

Directory of Open Access Journals (Sweden)

Bedri Yüksel

2014-01-01

Full Text Available Solar power and wind energy are used concurrently during specific periods, while at other times only the more efficient is used, and hybrid systems make this possible. When establishing a hybrid system, the extent to which these two energy sources support each other needs to be taken into account. This paper is a study of the effects of wind speed, insolation levels, and the meteorological parameters of temperature and humidity on the energy potential in Balıkesir, in the Marmara region of Turkey. The relationship between the parameters was studied using a multiple linear regression method. Using a designed-for-purpose computer program, two different regression equations were derived, with wind speed being the dependent variable in the first and insolation levels in the second. The regression equations yielded accurate results. The computer program allowed for the rapid calculation of different acceptance rates. The results of the statistical analysis proved the reliability of the equations. An estimate of identified meteorological parameters and unknown parameters could be produced with a specified precision by using the regression analysis method. The regression equations also worked for the evaluation of energy potential.

Covariate Imbalance and Adjustment for Logistic Regression Analysis of Clinical Trial Data

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Ciolino, Jody D.; Martin, Reneé H.; Zhao, Wenle; Jauch, Edward C.; Hill, Michael D.; Palesch, Yuko Y.

2014-01-01

In logistic regression analysis for binary clinical trial data, adjusted treatment effect estimates are often not equivalent to unadjusted estimates in the presence of influential covariates. This paper uses simulation to quantify the benefit of covariate adjustment in logistic regression. However, International Conference on Harmonization guidelines suggest that covariate adjustment be pre-specified. Unplanned adjusted analyses should be considered secondary. Results suggest that that if adjustment is not possible or unplanned in a logistic setting, balance in continuous covariates can alleviate some (but never all) of the shortcomings of unadjusted analyses. The case of log binomial regression is also explored. PMID:24138438

The survival rate of self-immolators in Kermanshah Province 2010- 2011

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Farid Najafi

2013-12-01

Full Text Available Background: Self-immolation is one of the most violent methods of suicide, which is spreading in Iran. The highest rate of deaths due to committing suicide and self-immolation in Iran is observed in Kermanshah province. This research was conducted to study the survival rate and the factors that influence survival among the ones who commit self-immolation in Kermanshah province. Methods: In this study, all the cases who did not survive, as well as all the ones who were hospitalized due to self-immolation in Kermanshah province during 2010 and 2011 were examined. The Kaplan-Meier method was used to estimate the survival function, and in order to do the comparisons, Logrank test and Cox Regression were employed using Stata 12 software. Results: The results indicated that during 2010 and 2011, 343 individuals committed self-immolation in Kermanshah Province, while, 288 (84% were women. Also, it was found that 184 (53% did not survive, the mean and median of survival time in those who committed suicide deliberately, were 33±2.6 and 11±2 days respectively. Estimation of survival rate using Logrank test indicated that survival rate had a significant relationship with age, mental disorders, drug addiction, and TBSA (Total Body Surface Area, while it did not suggest a statistically significant relationship with gender, marital status and cause of injury. After multivariate analysis using Cox regression, only two variables of age and TBSA could remain in the model and the other variables were excluded from the model. Conclusion: The death toll due to self-immolation is very high and the mean and median of survival time among the people who committed self-immolation is very low. Therefore, it is recommended that remedial action be performed quickly without wasting time.

Causal inference in survival analysis using pseudo-observations

DEFF Research Database (Denmark)

Andersen, Per K; Syriopoulou, Elisavet; Parner, Erik T

2017-01-01

Causal inference for non-censored response variables, such as binary or quantitative outcomes, is often based on either (1) direct standardization ('G-formula') or (2) inverse probability of treatment assignment weights ('propensity score'). To do causal inference in survival analysis, one needs ...

Prehospital critical care for out-of-hospital cardiac arrest: An observational study examining survival and a stakeholder-focused cost analysis.

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von Vopelius-Feldt, Johannes; Powell, Jane; Morris, Richard; Benger, Jonathan

2016-12-07

Survival rates from out-of-hospital cardiac arrest (OHCA) remain low, despite remarkable efforts to improve care. A number of ambulance services in the United Kingdom (UK) have developed prehospital critical care teams (CCTs) which attend critically ill patients, including OHCA. However, current scientific evidence describing CCTs attending OHCA is sparse and research to date has not demonstrated clear benefits from this model of care. This prospective, observational study will describe the effect of CCTs on survival from OHCA, when compared to advanced-life-support (ALS), the current standard of prehospital care in the UK. In addition, we will describe the association between individual critical care interventions and survival, and also the costs of CCTs for OHCA. To examine the effect of CCTs on survival from OHCA, we will use routine Utstein variables data already collected in a number of UK ambulance trusts. We will use propensity score matching to adjust for imbalances between the CCT and ALS groups. The primary outcome will be survival to hospital discharge, with the secondary outcome of survival to hospital admission. We will record the critical care interventions delivered during CCT attendance at OHCA. We will describe frequencies and aim to use multiple logistic regression to examine possible associations with survival. Finally, we will undertake a stakeholder-focused cost analysis of CCTs for OHCA. This will utilise a previously published Emergency Medical Services (EMS) cost analysis toolkit and will take into account the costs incurred from use of a helicopter and the proportion of these costs currently covered by charities in the UK. Prehospital critical care for OHCA is not universally available in many EMS. In the UK, it is variable and largely funded through public donations to charities. If this study demonstrates benefit from CCTs at an acceptable cost to the public or EMS commissioners, it will provide a rationale to increase funding and service

Declining Bias and Gender Wage Discrimination? A Meta-Regression Analysis

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Jarrell, Stephen B.; Stanley, T. D.

2004-01-01

The meta-regression analysis reveals that there is a strong tendency for discrimination estimates to fall and wage discrimination exist against the woman. The biasing effect of researchers' gender of not correcting for selection bias has weakened and changes in labor market have made it less important.

Statistical methods in regression and calibration analysis of chromosome aberration data

International Nuclear Information System (INIS)

Merkle, W.

1983-01-01

The method of iteratively reweighted least squares for the regression analysis of Poisson distributed chromosome aberration data is reviewed in the context of other fit procedures used in the cytogenetic literature. As an application of the resulting regression curves methods for calculating confidence intervals on dose from aberration yield are described and compared, and, for the linear quadratic model a confidence interval is given. Emphasis is placed on the rational interpretation and the limitations of various methods from a statistical point of view. (orig./MG)

Survival time and effect of selected predictor variables on survival in owned pet cats seropositive for feline immunodeficiency and leukemia virus attending a referral clinic in northern Italy.

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Spada, Eva; Perego, Roberta; Sgamma, Elena Assunta; Proverbio, Daniela

2018-02-01

Feline immunodeficiency virus (FIV) and feline leukemia virus (FeLV) are among the most important feline infectious diseases worldwide. This retrospective study investigated survival times and effects of selected predictor factors on survival time in a population of owned pet cats in Northern Italy testing positive for the presence of FIV antibodies and FeLV antigen. One hundred and three retrovirus-seropositive cats, 53 FIV-seropositive cats, 40 FeLV-seropositive cats, and 10 FIV+FeLV-seropositive cats were included in the study. A population of 103 retrovirus-seronegative age and sex-matched cats was selected. Survival time was calculated and compared between retrovirus-seronegative, FIV, FeLV and FIV+FeLV-seropositive cats using Kaplan-Meier survival analysis. Cox proportional-hazards regression analysis was used to study the effect of selected predictor factors (male gender, peripheral blood cytopenia as reduced red blood cells - RBC- count, leukopenia, neutropenia and lymphopenia, hypercreatininemia and reduced albumin to globulin ratio) on survival time in retrovirus-seropositive populations. Median survival times for seronegative cats, FIV, FeLV and FIV+FeLV-seropositive cats were 3960, 2040, 714 and 77days, respectively. Compared to retrovirus-seronegative cats median survival time was significantly lower (P<0.000) in FeLV and FIV+FeLV-seropositive cats. Median survival time in FeLV and FIV+FeLV-seropositive cats was also significant lower (P<0.000) when compared to FIV-seropositive cats. Hazard ratio of death in FeLV and FIV+FeLV-seropositive cats being respectively 3.4 and 7.4 times higher, in comparison to seronegative cats and 2.3 and 4.8 times higher in FeLV and FIV+FeLV-seropositive cats as compared to FIV-seropositive cats. A Cox proportional-hazards regression analysis showed that FIV and FeLV-seropositive cats with reduced RBC counts at time of diagnosis of seropositivity had significantly shorter survival times when compared to FIV and Fe

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

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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.

Prognostic value of tumor regression evaluated after first course of radiotherapy for anal canal cancer

International Nuclear Information System (INIS)

Chapet, Olivier; Gerard, Jean-Pierre; Riche, Benjamin; Alessio, Annunziato; Mornex, Francoise; Romestaing, Pascale

2005-01-01

Purpose: To evaluate whether the tumor response after an initial course of irradiation predicts for colostomy-free survival and overall survival in patients with anal canal cancer. Methods and Materials: Between 1980 and 1998, 252 patients were treated by pelvic external-beam radiotherapy (EBRT) followed by a brachytherapy boost in 218 or EBRT in 34. EBRT was combined with chemotherapy in 168 patients. An evaluation of tumor regression, before the boost, was available for 221 patients. They were divided into four groups according to the tumor response: 80% but 80% vs. ≤80%. The group with a T3-T4 lesion and tumor regression ≤80% had the poorest overall (52.8% ± 12.3%), disease-free (19.9% ± 9.9%), and colostomy-free survival (24.8% ± 11.2%) rates. Conclusion: The amount of tumor regression before EBRT or brachytherapy boost is a strong prognostic factor of disease control without colostomy. When regression is ≤80% in patients with an initial T3-T4 lesion, the use of conservative RT should be carefully evaluated because of the very poor disease-free and colostomy-free survival

Cancer survival for Aboriginal and Torres Strait Islander Australians: a national study of survival rates and excess mortality.

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Condon, John R; Zhang, Xiaohua; Baade, Peter; Griffiths, Kalinda; Cunningham, Joan; Roder, David M; Coory, Michael; Jelfs, Paul L; Threlfall, Tim

2014-01-31

National cancer survival statistics are available for the total Australian population but not Indigenous Australians, although their cancer mortality rates are known to be higher than those of other Australians. We aimed to validate analysis methods and report cancer survival rates for Indigenous Australians as the basis for regular national reporting. We used national cancer registrations data to calculate all-cancer and site-specific relative survival for Indigenous Australians (compared with non-Indigenous Australians) diagnosed in 2001-2005. Because of limited availability of Indigenous life tables, we validated and used cause-specific survival (rather than relative survival) for proportional hazards regression to analyze time trends and regional variation in all-cancer survival between 1991 and 2005. Survival was lower for Indigenous than non-Indigenous Australians for all cancers combined and for many cancer sites. The excess mortality of Indigenous people with cancer was restricted to the first three years after diagnosis, and greatest in the first year. Survival was lower for rural and remote than urban residents; this disparity was much greater for Indigenous people. Survival improved between 1991 and 2005 for non-Indigenous people (mortality decreased by 28%), but to a much lesser extent for Indigenous people (11%) and only for those in remote areas; cancer survival did not improve for urban Indigenous residents. Cancer survival is lower for Indigenous than other Australians, for all cancers combined and many individual cancer sites, although more accurate recording of Indigenous status by cancer registers is required before the extent of this disadvantage can be known with certainty. Cancer care for Indigenous Australians needs to be considerably improved; cancer diagnosis, treatment, and support services need to be redesigned specifically to be accessible and acceptable to Indigenous people.

Social determinants of health and 5-year survival of colorectal cancer.

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Heidarnia, Mohammad Ali; Monfared, Esmat Davoudi; Akbari, Mohammad Esmail; Yavari, Parvin; Amanpour, Farzaneh; Mohseni, Maryam

2013-01-01

Early in the 21st century, cancers are the second cause of death worldwide. Colon cancer is third most common cancer and one of the few amenable to early diagnosis and treatment. Evaluation of factors affecting this cancer is important to increase survival time. Some of these factors affecting all diseases including cancer are social determinants of health. According to the importance of this disease and relation with these factors, this study was conducted to assess the relationship between social determinants of health and colon cancer survival. This was a cross-sectional, descriptive study for patients with colon cancer registered in the Cancer Research Center of Shahid Beheshti University of Medical Science, from April 2005 to November 2006, performed using questionnaires filled by telephone interview with patients (if patients had died, with family members). Data was analyzed with SPSS software (version 19) for descriptive analysis and STATA software for survival analysis including log rank test and three step Cox Proportional Hazard regression. Five hundred fifty nine patients with ages ranging from 23 to 88 years with mean ± standard deviation of 63 ± 11.8 years were included in the study. The five year survival was 68.3%( 387 patients were alive and 172 patients were dead by the end of the study). The Cox proportional hazard regression showed 5-year survival was related to age (HR=0.53, p=0.042 for>50 years versusmanual versus non manual jobs), region of residency (HR=3.49, p=0.018 for west versus south regions), parents in childhood (HR=2.87, p=0.012 for having both parents versus not having), anatomical cancer location (HR=2.16, psurvival of colon cancer and it may be better to consider these factors in addition to developing cancer treatment and to focus on these determinants of health in long-time planning.

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]....

Complete hazard ranking to analyze right-censored data: An ALS survival study.

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Zhengnan Huang

2017-12-01

Full Text Available Survival analysis represents an important outcome measure in clinical research and clinical trials; further, survival ranking may offer additional advantages in clinical trials. In this study, we developed GuanRank, a non-parametric ranking-based technique to transform patients' survival data into a linear space of hazard ranks. The transformation enables the utilization of machine learning base-learners including Gaussian process regression, Lasso, and random forest on survival data. The method was submitted to the DREAM Amyotrophic Lateral Sclerosis (ALS Stratification Challenge. Ranked first place, the model gave more accurate ranking predictions on the PRO-ACT ALS dataset in comparison to Cox proportional hazard model. By utilizing right-censored data in its training process, the method demonstrated its state-of-the-art predictive power in ALS survival ranking. Its feature selection identified multiple important factors, some of which conflicts with previous studies.

Re-analysis of survival data of cancer patients utilizing additive homeopathy.

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Gleiss, Andreas; Frass, Michael; Gaertner, Katharina

2016-08-01

In this short communication we present a re-analysis of homeopathic patient data in comparison to control patient data from the same Outpatient´s Unit "Homeopathy in malignant diseases" of the Medical University of Vienna. In this analysis we took account of a probable immortal time bias. For patients suffering from advanced stages of cancer and surviving the first 6 or 12 months after diagnosis, respectively, the results show that utilizing homeopathy gives a statistically significant (p<0.001) advantage over control patients regarding survival time. In conclusion, bearing in mind all limitations, the results of this retrospective study suggest that patients with advanced stages of cancer might benefit from additional homeopathic treatment until a survival time of up to 12 months after diagnosis. Copyright © 2016 Elsevier Ltd. All rights reserved.

Predictors of postoperative outcomes of cubital tunnel syndrome treatments using multiple logistic regression analysis.

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Suzuki, Taku; Iwamoto, Takuji; Shizu, Kanae; Suzuki, Katsuji; Yamada, Harumoto; Sato, Kazuki

2017-05-01

This retrospective study was designed to investigate prognostic factors for postoperative outcomes for cubital tunnel syndrome (CubTS) using multiple logistic regression analysis with a large number of patients. Eighty-three patients with CubTS who underwent surgeries were enrolled. The following potential prognostic factors for disease severity were selected according to previous reports: sex, age, type of surgery, disease duration, body mass index, cervical lesion, presence of diabetes mellitus, Workers' Compensation status, preoperative severity, and preoperative electrodiagnostic testing. Postoperative severity of disease was assessed 2 years after surgery by Messina's criteria which is an outcome measure specifically for CubTS. Bivariate analysis was performed to select candidate prognostic factors for multiple linear regression analyses. Multiple logistic regression analysis was conducted to identify the association between postoperative severity and selected prognostic factors. Both bivariate and multiple linear regression analysis revealed only preoperative severity as an independent risk factor for poor prognosis, while other factors did not show any significant association. Although conflicting results exist regarding prognosis of CubTS, this study supports evidence from previous studies and concludes early surgical intervention portends the most favorable prognosis. Copyright © 2017 The Japanese Orthopaedic Association. Published by Elsevier B.V. All rights reserved.

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.

Exploring factors associated with traumatic dental injuries in preschool children: a Poisson regression analysis.

Science.gov (United States)

Feldens, Carlos Alberto; Kramer, Paulo Floriani; Ferreira, Simone Helena; Spiguel, Mônica Hermann; Marquezan, Marcela

2010-04-01

This cross-sectional study aimed to investigate the factors associated with dental trauma in preschool children using Poisson regression analysis with robust variance. The study population comprised 888 children aged 3- to 5-year-old attending public nurseries in Canoas, southern Brazil. Questionnaires assessing information related to the independent variables (age, gender, race, mother's educational level and family income) were completed by the parents. Clinical examinations were carried out by five trained examiners in order to assess traumatic dental injuries (TDI) according to Andreasen's classification. One of the five examiners was calibrated to assess orthodontic characteristics (open bite and overjet). Multivariable Poisson regression analysis with robust variance was used to determine the factors associated with dental trauma as well as the strengths of association. Traditional logistic regression was also performed in order to compare the estimates obtained by both methods of statistical analysis. 36.4% (323/888) of the children suffered dental trauma and there was no difference in prevalence rates from 3 to 5 years of age. Poisson regression analysis showed that the probability of the outcome was almost 30% higher for children whose mothers had more than 8 years of education (Prevalence Ratio = 1.28; 95% CI = 1.03-1.60) and 63% higher for children with an overjet greater than 2 mm (Prevalence Ratio = 1.63; 95% CI = 1.31-2.03). Odds ratios clearly overestimated the size of the effect when compared with prevalence ratios. These findings indicate the need for preventive orientation regarding TDI, in order to educate parents and caregivers about supervising infants, particularly those with increased overjet and whose mothers have a higher level of education. Poisson regression with robust variance represents a better alternative than logistic regression to estimate the risk of dental trauma in preschool children.

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.

Application of range-test in multiple linear regression analysis in ...

African Journals Online (AJOL)

Application of range-test in multiple linear regression analysis in the presence of outliers is studied in this paper. First, the plot of the explanatory variables (i.e. Administration, Social/Commercial, Economic services and Transfer) on the dependent variable (i.e. GDP) was done to identify the statistical trend over the years.

A nomogram for predicting survival in patients with breast cancer brain metastasis.

Science.gov (United States)

Huang, Zhou; Sun, Bing; Wu, Shikai; Meng, Xiangying; Cong, Yang; Shen, Ge; Song, Santai

2018-05-01

Brain metastasis (BM) is common in patients with breast cancer. Predicting patient survival is critical for the clinical management of breast cancer brain metastasis (BCBM). The present study was designed to develop and evaluate a prognostic model for patients with newly diagnosed BCBM. Based on the clinical data of patients with BCBM treated in the Affiliated Hospital of Academy of Military Medical Sciences (Beijing, China) between 2002 and 2014, a nomogram was developed to predict survival using proportional hazards regression analysis. The model was validated internally by bootstrapping, and the concordance index (c-index) was calculated. A calibration curve and c-index were used to evaluate discriminatory and predictive ability, in order to compare the nomogram with widely used models, including recursive partitioning analysis (RPA), graded prognostic assessment (GPA) and breast-graded prognostic assessment (Breast-GPA). A total of 411 patients with BCBM were included in the development of this predictive model. The median overall survival time was 14.1 months. Statistically significant predictors for patient survival included biological subtype, Karnofsky performance score, leptomeningeal metastasis, extracranial metastasis, the number of brain metastases and disease-free survival. A nomogram for predicting 1- and 2-year overall survival rates was constructed, which exhibited good accuracy in predicting overall survival with a concordance index of 0.735. This model outperformed RPA, GPA and Breast-GPA, based on the comparisons of the c-indexes. The nomogram constructed based on a multiple factor analysis was able to more accurately predict the individual survival probability of patients with BCBM, compared with existing models.

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.

Survival analysis approach to account for non-exponential decay rate effects in lifetime experiments

International Nuclear Information System (INIS)

Coakley, K.J.; Dewey, M.S.; Huber, M.G.; Huffer, C.R.; Huffman, P.R.; Marley, D.E.; Mumm, H.P.; O'Shaughnessy, C.M.; Schelhammer, K.W.; Thompson, A.K.; Yue, A.T.

2016-01-01

In experiments that measure the lifetime of trapped particles, in addition to loss mechanisms with exponential survival probability functions, particles can be lost by mechanisms with non-exponential survival probability functions. Failure to account for such loss mechanisms produces systematic measurement error and associated systematic uncertainties in these measurements. In this work, we develop a general competing risks survival analysis method to account for the joint effect of loss mechanisms with either exponential or non-exponential survival probability functions, and a method to quantify the size of systematic effects and associated uncertainties for lifetime estimates. As a case study, we apply our survival analysis formalism and method to the Ultra Cold Neutron lifetime experiment at NIST. In this experiment, neutrons can escape a magnetic trap before they decay due to a wall loss mechanism with an associated non-exponential survival probability function.

Survival analysis approach to account for non-exponential decay rate effects in lifetime experiments

Energy Technology Data Exchange (ETDEWEB)

Coakley, K.J., E-mail: kevincoakley@nist.gov [National Institute of Standards and Technology, 325 Broadway, Boulder, CO 80305 (United States); Dewey, M.S.; Huber, M.G. [National Institute of Standards and Technology, 100 Bureau Drive, Stop 8461, Gaithersburg, MD 20899 (United States); Huffer, C.R.; Huffman, P.R. [North Carolina State University, 2401 Stinson Drive, Box 8202, Raleigh, NC 27695 (United States); Triangle Universities Nuclear Laboratory, 116 Science Drive, Box 90308, Durham, NC 27708 (United States); Marley, D.E. [National Institute of Standards and Technology, 100 Bureau Drive, Stop 8461, Gaithersburg, MD 20899 (United States); North Carolina State University, 2401 Stinson Drive, Box 8202, Raleigh, NC 27695 (United States); Mumm, H.P. [National Institute of Standards and Technology, 100 Bureau Drive, Stop 8461, Gaithersburg, MD 20899 (United States); O' Shaughnessy, C.M. [University of North Carolina at Chapel Hill, 120 E. Cameron Ave., CB #3255, Chapel Hill, NC 27599 (United States); Triangle Universities Nuclear Laboratory, 116 Science Drive, Box 90308, Durham, NC 27708 (United States); Schelhammer, K.W. [North Carolina State University, 2401 Stinson Drive, Box 8202, Raleigh, NC 27695 (United States); Triangle Universities Nuclear Laboratory, 116 Science Drive, Box 90308, Durham, NC 27708 (United States); Thompson, A.K.; Yue, A.T. [National Institute of Standards and Technology, 100 Bureau Drive, Stop 8461, Gaithersburg, MD 20899 (United States)

2016-03-21

In experiments that measure the lifetime of trapped particles, in addition to loss mechanisms with exponential survival probability functions, particles can be lost by mechanisms with non-exponential survival probability functions. Failure to account for such loss mechanisms produces systematic measurement error and associated systematic uncertainties in these measurements. In this work, we develop a general competing risks survival analysis method to account for the joint effect of loss mechanisms with either exponential or non-exponential survival probability functions, and a method to quantify the size of systematic effects and associated uncertainties for lifetime estimates. As a case study, we apply our survival analysis formalism and method to the Ultra Cold Neutron lifetime experiment at NIST. In this experiment, neutrons can escape a magnetic trap before they decay due to a wall loss mechanism with an associated non-exponential survival probability function.

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.

Clinical Predictors of Survival for Patients with Stage IV Cancer Referred to Radiation Oncology.

Directory of Open Access Journals (Sweden)

Johnny Kao

Full Text Available There is an urgent need for a robust, clinically useful predictive model for survival in a heterogeneous group of patients with metastatic cancer referred to radiation oncology.From May 2012 to August 2013, 143 consecutive patients with stage IV cancer were prospectively evaluated by a single radiation oncologist. We retrospectively analyzed the effect of 29 patient, laboratory and tumor-related prognostic factors on overall survival using univariate analysis. Variables that were statistically significant on univariate analysis were entered into a multivariable Cox regression to identify independent predictors of overall survival.The median overall survival was 5.5 months. Four prognostic factors significantly predicted survival on multivariable analysis including ECOG performance status (0-1 vs. 2 vs. 3-4, number of active tumors (1 to 5 vs. ≥ 6, albumin levels (≥ 3.4 vs. 2.4 to 3.3 vs. 31.4 months for very low risk patients compared to 14.5 months for low risk, 4.1 months for intermediate risk and 1.2 months for high risk (p < 0.001.These data suggest that a model that considers performance status, extent of disease, primary tumor site and serum albumin represents a simple model to accurately predict survival for patients with stage IV cancer who are potential candidates for radiation therapy.

Analysis of the influence of quantile regression model on mainland tourists' service satisfaction performance.

Science.gov (United States)

Wang, Wen-Cheng; Cho, Wen-Chien; Chen, Yin-Jen

2014-01-01

It is estimated that mainland Chinese tourists travelling to Taiwan can bring annual revenues of 400 billion NTD to the Taiwan economy. Thus, how the Taiwanese Government formulates relevant measures to satisfy both sides is the focus of most concern. Taiwan must improve the facilities and service quality of its tourism industry so as to attract more mainland tourists. This paper conducted a questionnaire survey of mainland tourists and used grey relational analysis in grey mathematics to analyze the satisfaction performance of all satisfaction question items. The first eight satisfaction items were used as independent variables, and the overall satisfaction performance was used as a dependent variable for quantile regression model analysis to discuss the relationship between the dependent variable under different quantiles and independent variables. Finally, this study further discussed the predictive accuracy of the least mean regression model and each quantile regression model, as a reference for research personnel. The analysis results showed that other variables could also affect the overall satisfaction performance of mainland tourists, in addition to occupation and age. The overall predictive accuracy of quantile regression model Q0.25 was higher than that of the other three models.

Analysis of the Influence of Quantile Regression Model on Mainland Tourists' Service Satisfaction Performance

Science.gov (United States)

Wang, Wen-Cheng; Cho, Wen-Chien; Chen, Yin-Jen

2014-01-01

It is estimated that mainland Chinese tourists travelling to Taiwan can bring annual revenues of 400 billion NTD to the Taiwan economy. Thus, how the Taiwanese Government formulates relevant measures to satisfy both sides is the focus of most concern. Taiwan must improve the facilities and service quality of its tourism industry so as to attract more mainland tourists. This paper conducted a questionnaire survey of mainland tourists and used grey relational analysis in grey mathematics to analyze the satisfaction performance of all satisfaction question items. The first eight satisfaction items were used as independent variables, and the overall satisfaction performance was used as a dependent variable for quantile regression model analysis to discuss the relationship between the dependent variable under different quantiles and independent variables. Finally, this study further discussed the predictive accuracy of the least mean regression model and each quantile regression model, as a reference for research personnel. The analysis results showed that other variables could also affect the overall satisfaction performance of mainland tourists, in addition to occupation and age. The overall predictive accuracy of quantile regression model Q0.25 was higher than that of the other three models. PMID:24574916

Analysis of the Influence of Quantile Regression Model on Mainland Tourists’ Service Satisfaction Performance

Directory of Open Access Journals (Sweden)

Wen-Cheng Wang

2014-01-01

Full Text Available It is estimated that mainland Chinese tourists travelling to Taiwan can bring annual revenues of 400 billion NTD to the Taiwan economy. Thus, how the Taiwanese Government formulates relevant measures to satisfy both sides is the focus of most concern. Taiwan must improve the facilities and service quality of its tourism industry so as to attract more mainland tourists. This paper conducted a questionnaire survey of mainland tourists and used grey relational analysis in grey mathematics to analyze the satisfaction performance of all satisfaction question items. The first eight satisfaction items were used as independent variables, and the overall satisfaction performance was used as a dependent variable for quantile regression model analysis to discuss the relationship between the dependent variable under different quantiles and independent variables. Finally, this study further discussed the predictive accuracy of the least mean regression model and each quantile regression model, as a reference for research personnel. The analysis results showed that other variables could also affect the overall satisfaction performance of mainland tourists, in addition to occupation and age. The overall predictive accuracy of quantile regression model Q0.25 was higher than that of the other three models.

Econometric analysis of realised covariation: high frequency covariance, regression and correlation in financial economics

OpenAIRE

Ole E. Barndorff-Nielsen; Neil Shephard

2002-01-01

This paper analyses multivariate high frequency financial data using realised covariation. We provide a new asymptotic distribution theory for standard methods such as regression, correlation analysis and covariance. It will be based on a fixed interval of time (e.g. a day or week), allowing the number of high frequency returns during this period to go to infinity. Our analysis allows us to study how high frequency correlations, regressions and covariances change through time. In particular w...

Survival after Second and Subsequent Recurrences in Osteosarcoma: A Retrospective Multicenter Analysis.

Science.gov (United States)

Tirtei, Elisa; Asaftei, Sebastian D; Manicone, Rosaria; Cesari, Marilena; Paioli, Anna; Rocca, Michele; Ferrari, Stefano; Fagioli, Franca

2017-05-01

Purpose Osteosarcoma (OS) is the most common primary bone tumor. Despite complete surgical removal and intensive chemotherapeutic treatment, 30%-35% of patients with OS have local or systemic recurrence. Some patients survive multiple recurrences, but overall survival after OS recurrence is poor. This analysis aims to describe and identify factors influencing post-relapse survival (PRS) after a second OS relapse. Methods This is a retrospective analysis of 60 patients with a second relapse of OS of the extremities in 2 Italian centers between 2003 and 2013. Results Treatment for first and subsequent relapses was planned according to institutional guidelines. After complete surgical remission (CSR) following the first recurrence, patients experienced a second OS relapse with a median disease-free interval (DFI) of 6 months. Lung disease was prevalent: 44 patients (76%) had pulmonary metastases. Survival after the second relapse was 22% at 5 years. Lung disease only correlated with better survival at 5 years (33.6%) compared with other sites of recurrence (5%; p = 0.008). Patients with a single pulmonary lesion had a better 5-year second PRS (42%; p = 0.02). Patients who achieved a second CSR had a 5-year second PRS of 33.4%. Chemotherapy (p<0.001) benefited patients without a third CSR. Conclusions This analysis confirms the importance of an aggressive, repeated surgical approach. Lung metastases only, the number of lesions, DFI and CSR influenced survival. It also confirms the importance of chemotherapy in patients in whom surgical treatment is not feasible.

Introduction to SURPH.1 analysis of release-recapture data for survival studies

International Nuclear Information System (INIS)

Smith, S.G.; Skalski, J.R.; Schlechte, J.W.; Hoffmann, A.; Cassen, V.

1994-12-01

Program SURPH is the culmination of several years of research to develop a comprehensive computer program to analyze survival studies of fish and wildlife populations. Development of this software was motivated by the advent of the PIT-tag (Passive Integrated Transponder) technology that permits the detection of salmonid smolt as they pass through hydroelectric facilities on the Snake and Columbia Rivers in the Pacific Northwest. Repeated detections of individually tagged smolt and analysis of their capture-histories permits estimates of downriver survival probabilities. Eventual installation of detection facilities at adult fish ladders will also permit estimation of ocean survival and upstream survival of returning salmon using the statistical methods incorporated in SURPH.1. However, the utility of SURPH.1 far exceeds solely the analysis of salmonid tagging studies. Release-recapture and radiotelemetry studies from a wide range of terrestrial and aquatic species have been analyzed using SURPH.1 to estimate discrete time survival probabilities and investigate survival relationships. The interactive computing environment of SURPH.1 was specifically developed to allow researchers to investigate the relationship between survival and capture processes and environmental, experimental and individual-based covariates. Program SURPH.1 represents a significant advancement in the ability of ecologists to investigate the interplay between morphologic, genetic, environmental and anthropogenic factors on the survival of wild species. It is hoped that this better understanding of risk factors affecting survival will lead to greater appreciation of the intricacies of nature and to improvements in the management of wild resources. This technical report is an introduction to SURPH.1 and provides a user guide for both the UNIX and MS-Windows reg-sign applications of the SURPH software

Prediction of radiation levels in residences: A methodological comparison of CART [Classification and Regression Tree Analysis] and conventional regression

International Nuclear Information System (INIS)

Janssen, I.; Stebbings, J.H.

1990-01-01

In environmental epidemiology, trace and toxic substance concentrations frequently have very highly skewed distributions ranging over one or more orders of magnitude, and prediction by conventional regression is often poor. Classification and Regression Tree Analysis (CART) is an alternative in such contexts. To compare the techniques, two Pennsylvania data sets and three independent variables are used: house radon progeny (RnD) and gamma levels as predicted by construction characteristics in 1330 houses; and ∼200 house radon (Rn) measurements as predicted by topographic parameters. CART may identify structural variables of interest not identified by conventional regression, and vice versa, but in general the regression models are similar. CART has major advantages in dealing with other common characteristics of environmental data sets, such as missing values, continuous variables requiring transformations, and large sets of potential independent variables. CART is most useful in the identification and screening of independent variables, greatly reducing the need for cross-tabulations and nested breakdown analyses. There is no need to discard cases with missing values for the independent variables because surrogate variables are intrinsic to CART. The tree-structured approach is also independent of the scale on which the independent variables are measured, so that transformations are unnecessary. CART identifies important interactions as well as main effects. The major advantages of CART appear to be in exploring data. Once the important variables are identified, conventional regressions seem to lead to results similar but more interpretable by most audiences. 12 refs., 8 figs., 10 tabs

A rotor optimization using regression analysis

Science.gov (United States)

Giansante, N.

1984-01-01

The design and development of helicopter rotors is subject to the many design variables and their interactions that effect rotor operation. Until recently, selection of rotor design variables to achieve specified rotor operational qualities has been a costly, time consuming, repetitive task. For the past several years, Kaman Aerospace Corporation has successfully applied multiple linear regression analysis, coupled with optimization and sensitivity procedures, in the analytical design of rotor systems. It is concluded that approximating equations can be developed rapidly for a multiplicity of objective and constraint functions and optimizations can be performed in a rapid and cost effective manner; the number and/or range of design variables can be increased by expanding the data base and developing approximating functions to reflect the expanded design space; the order of the approximating equations can be expanded easily to improve correlation between analyzer results and the approximating equations; gradients of the approximating equations can be calculated easily and these gradients are smooth functions reducing the risk of numerical problems in the optimization; the use of approximating functions allows the problem to be started easily and rapidly from various initial designs to enhance the probability of finding a global optimum; and the approximating equations are independent of the analysis or optimization codes used.

Alcohol Consumption and Survival after a Breast Cancer Diagnosis: A Literature-Based Meta-analysis and Collaborative Analysis of Data for 29,239 Cases

Science.gov (United States)

Ali, Alaa M.G.; Schmidt, Marjanka K.; Bolla, Manjeet K.; Wang, Qin; Gago-Dominguez, M.; Castelao, J. Esteban; Carracedo, Angel; Garzón, Victor Muñoz; Bojesen, Stig E.; Nordestgaard, Børge G.; Flyger, Henrik; Chang-Claude, Jenny; Vrieling, Alina; Rudolph, Anja; Seibold, Petra; Nevanlinna, Heli; Muranen, Taru A.; Aaltonen, Kirsimari; Blomqvist, Carl; Matsuo, Keitaro; Ito, Hidemi; Iwata, Hiroji; Horio, Akiyo; John, Esther M.; Sherman, Mark; Lissowska, Jolanta; Figueroa, Jonine; Garcia-Closas, Montserrat; Anton-Culver, Hoda; Shah, Mitul; Hopper, John L.; Trichopoulou, Antonia; Bueno-de-Mesquita, Bas; Krogh, Vittorio; Weiderpass, Elisabete; Andersson, Anne; Clavel-Chapelon, Françoise; Dossus, Laure; Fagherazzi, Guy; Peeters, Petra H.; Olsen, Anja; Wishart, Gordon C.; Easton, Douglas F.; Borgquist, Signe; Overvad, Kim; Barricarte, Aurelio; González, Carlos A.; Sánchez, María-José; Amiano, Pilar; Riboli, Elio; Key, Tim; Pharoah, Paul D.

2015-01-01

Background Evidence for an association of alcohol consumption with prognosis after a diagnosis of breast cancer has been inconsistent. We have reviewed and summarized the published evidence and evaluated the association using individual patient data from multiple case cohorts. Methods A MEDLINE search to identify studies published up to January 2013 was performed. We combined published estimates of survival time for “moderate drinkers” versus nondrinkers. An analysis of individual participant data using Cox regression was carried out using data from 11 case cohorts. Results We identified 11 published studies suitable for inclusion in the meta-analysis. Moderate post-diagnosis alcohol consumption was not associated with overall survival [HR, 0.95; 95% confidence interval (CI), 0.85–1.05], but there was some evidence of better survival associated with prediagnosis consumption (HR, 0.80; 95% CI, 0.73–0.88). Individual data on alcohol consumption for 29,239 cases with 4,839 deaths were available from the 11 case cohorts, all of which had data on estrogen receptor (ER) status. For women with ER-positive disease, there was little evidence that pre- or postdiagnosis alcohol consumption is associated with breast cancer–specific mortality, with some evidence of a negative association with all-cause mortality. On the basis of a single study, moderate postdiagnosis alcohol intake was associated with a small reduction in breast cancer–specific mortality for women with ER-negative disease. There was no association with prediagnosis intake for women with ER-negative disease. Conclusion There was little evidence that pre- or post-diagnosis alcohol consumption is associated with breast cancer–specific mortality for women with ER-positive disease. There was weak evidence that moderate post-diagnosis alcohol intake is associated with a small reduction in breast cancer–specific mortality in ER-negative disease. Impact Considering the totality of the evidence, moderate

Regression and local control rates after radiotherapy for jugulotympanic paragangliomas: Systematic review and meta-analysis

International Nuclear Information System (INIS)

Hulsteijn, Leonie T. van; Corssmit, Eleonora P.M.; Coremans, Ida E.M.; Smit, Johannes W.A.; Jansen, Jeroen C.; Dekkers, Olaf M.

2013-01-01

The primary treatment goal of radiotherapy for paragangliomas of the head and neck region (HNPGLs) is local control of the tumor, i.e. stabilization of tumor volume. Interestingly, regression of tumor volume has also been reported. Up to the present, no meta-analysis has been performed giving an overview of regression rates after radiotherapy in HNPGLs. The main objective was to perform a systematic review and meta-analysis to assess regression of tumor volume in HNPGL-patients after radiotherapy. A second outcome was local tumor control. Design of the study is systematic review and meta-analysis. PubMed, EMBASE, Web of Science, COCHRANE and Academic Search Premier and references of key articles were searched in March 2012 to identify potentially relevant studies. Considering the indolent course of HNPGLs, only studies with ⩾12 months follow-up were eligible. Main outcomes were the pooled proportions of regression and local control after radiotherapy as initial, combined (i.e. directly post-operatively or post-embolization) or salvage treatment (i.e. after initial treatment has failed) for HNPGLs. A meta-analysis was performed with an exact likelihood approach using a logistic regression with a random effect at the study level. Pooled proportions with 95% confidence intervals (CI) were reported. Fifteen studies were included, concerning a total of 283 jugulotympanic HNPGLs in 276 patients. Pooled regression proportions for initial, combined and salvage treatment were respectively 21%, 33% and 52% in radiosurgery studies and 4%, 0% and 64% in external beam radiotherapy studies. Pooled local control proportions for radiotherapy as initial, combined and salvage treatment ranged from 79% to 100%. Radiotherapy for jugulotympanic paragangliomas results in excellent local tumor control and therefore is a valuable treatment for these types of tumors. The effects of radiotherapy on regression of tumor volume remain ambiguous, although the data suggest that regression can

Impact of donor-recipient sex match on long-term survival after heart transplantation in children: An analysis of 5797 pediatric heart transplants.

Science.gov (United States)

Kemna, Mariska; Albers, Erin; Bradford, Miranda C; Law, Sabrina; Permut, Lester; McMullan, D Mike; Law, Yuk

2016-03-01

The effect of donor-recipient sex matching on long-term survival in pediatric heart transplantation is not well known. Adult data have shown worse survival when male recipients receive a sex-mismatched heart, with conflicting results in female recipients. We analyzed 5795 heart transplant recipients ≤ 18 yr in the Scientific Registry of Transplant Recipients (1990-2012). Recipients were stratified based on donor and recipient sex, creating four groups: MM (N = 1888), FM (N = 1384), FF (N = 1082), and MF (N = 1441). Males receiving sex-matched donor hearts had increased unadjusted allograft survival at five yr (73.2 vs. 71%, p = 0.01). However, this survival advantage disappeared with longer follow-up and when adjusted for additional risk factors by multivariable Cox regression analysis. In contrast, for females, receiving a sex-mismatched heart was associated with an 18% higher risk of allograft loss over time compared to receiving a sex-matched heart (HR 1.18, 95% CI: 1.00-1.38) and a 26% higher risk compared to sex-matched male recipients (HR 1.26, 95% CI: 1.10-1.45). Females who receive a heart from a male donor appear to have a distinct long-term survival disadvantage compared to all other groups. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Robust analysis of trends in noisy tokamak confinement data using geodesic least squares regression

Energy Technology Data Exchange (ETDEWEB)

Verdoolaege, G., E-mail: geert.verdoolaege@ugent.be [Department of Applied Physics, Ghent University, B-9000 Ghent (Belgium); Laboratory for Plasma Physics, Royal Military Academy, B-1000 Brussels (Belgium); Shabbir, A. [Department of Applied Physics, Ghent University, B-9000 Ghent (Belgium); Max Planck Institute for Plasma Physics, Boltzmannstr. 2, 85748 Garching (Germany); Hornung, G. [Department of Applied Physics, Ghent University, B-9000 Ghent (Belgium)

2016-11-15

Regression analysis is a very common activity in fusion science for unveiling trends and parametric dependencies, but it can be a difficult matter. We have recently developed the method of geodesic least squares (GLS) regression that is able to handle errors in all variables, is robust against data outliers and uncertainty in the regression model, and can be used with arbitrary distribution models and regression functions. We here report on first results of application of GLS to estimation of the multi-machine scaling law for the energy confinement time in tokamaks, demonstrating improved consistency of the GLS results compared to standard least squares.

A retrospective analysis of survival and prognostic factors after stereotactic radiosurgery for aggressive meningiomas

International Nuclear Information System (INIS)

Ferraro, Daniel J; Zoberi, Imran; Simpson, Joseph R; Jaboin, Jerry J; Funk, Ryan K; Blackett, John William; Ju, Michelle R; DeWees, Todd A; Chicoine, Michael R; Dowling, Joshua L; Rich, Keith M; Drzymala, Robert E

2014-01-01

While most meningiomas are benign, aggressive meningiomas are associated with high levels of recurrence and mortality. A single institution’s Gamma Knife radiosurgical experience with atypical and malignant meningiomas is presented, stratified by the most recent WHO classification. Thirty-one patients with atypical and 4 patients with malignant meningiomas treated with Gamma Knife radiosurgery between July 2000 and July 2011 were retrospectively reviewed. All patients underwent prior surgical resection. Overall survival was the primary endpoint and rate of disease recurrence in the brain was a secondary endpoint. Patients who had previous radiotherapy or prior surgical resection were included. Kaplan-Meier and Cox proportional hazards models were used to estimate survival and identify factors predictive of recurrence and survival. Post-Gamma Knife recurrence was identified in 11 patients (31.4%) with a median overall survival of 36 months and progression-free survival of 25.8 months. Nine patients (25.7%) had died. Three-year overall survival (OS) and progression-free survival (PFS) rates were 78.0% and 65.0%, respectively. WHO grade II 3-year OS and PFS were 83.4% and 70.1%, while WHO grade III 3-year OS and PFS were 33.3% and 0%. Recurrence rate was significantly higher in patients with a prior history of benign meningioma, nuclear atypia, high mitotic rate, spontaneous necrosis, and WHO grade III diagnosis on univariate analysis; only WHO grade III diagnosis was significant on multivariate analysis. Overall survival was adversely affected in patients with WHO grade III diagnosis, prior history of benign meningioma, prior fractionated radiotherapy, larger tumor volume, and higher isocenter number on univariate analysis; WHO grade III diagnosis and larger treated tumor volume were significant on multivariate analysis. Atypical and anaplastic meningiomas remain difficult tumors to treat. WHO grade III diagnosis and treated tumor volume were significantly

Postmastectomy Radiation Therapy Is Associated With Improved Survival in Node-Positive Male Breast Cancer: A Population Analysis

Energy Technology Data Exchange (ETDEWEB)

Abrams, Matthew J., E-mail: mabrams@tuftsmedicalcenter.org [Department of Radiation Oncology, Tufts University School of Medicine, Tufts Medical Center, Boston, Massachusetts (United States); Koffer, Paul P. [Department of Radiation Oncology, Tufts University School of Medicine, Tufts Medical Center, Boston, Massachusetts (United States); Wazer, David E. [Department of Radiation Oncology, Tufts University School of Medicine, Tufts Medical Center, Boston, Massachusetts (United States); Department of Radiation Oncology, The Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island (United States); Hepel, Jaroslaw T. [Department of Radiation Oncology, The Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island (United States)

2017-06-01

Purpose: Because of its rarity, there are no randomized trials investigating postmastectomy radiation therapy (PMRT) in male breast cancer. This study retrospectively examines the impact of PMRT in male breast cancer patients in the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) database. Methods and Materials: The SEER database 8.3.2 was queried for men ages 20+ with a diagnosis of localized or regional nonmetastatic invasive ductal/lobular carcinoma from 1998 to 2013. Included patients were treated by modified radical mastectomy (MRM), with or without adjuvant external beam radiation. Univariate and multivariate analyses evaluated predictors for PMRT use after MRM. Kaplan-Meier overall survival (OS) curves of the entire cohort and a case-matched cohort were calculated and compared by the log-rank test. Cox regression was used for multivariate survival analyses. Results: A total of 1933 patients were included in the unmatched cohort. There was no difference in 5-year OS between those who received PMRT and those who did not (78% vs 77%, respectively, P=.371); however, in the case-matched analysis, PMRT was associated with improved OS at 5 years (83% vs 54%, P<.001). On subset analysis of the unmatched cohort, PMRT was associated with improved OS in men with 1 to 3 positive nodes (5-year OS 79% vs 72% P=.05) and those with 4+ positive nodes (5-year OS 73% vs 53% P<.001). On multivariate analysis of the unmatched cohort, independent predictors for improved OS were use of PMRT: HR=0.551 (0.412-0.737) and estrogen receptor–positive disease: HR=0.577 (0.339-0.983). Predictors for a survival detriment were higher grade 3/4: HR=1.825 (1.105-3.015), larger tumor T2: HR=1.783 (1.357-2.342), T3/T4: HR=2.683 (1.809-3.978), higher N-stage: N1 HR=1.574 (1.184-2.091), N2/N3: HR=2.328 (1.684-3.218), black race: HR=1.689 (1.222-2.336), and older age 81+: HR=4.164 (1.497-11.582). Conclusions: There may be a survival benefit with the

Survival after Abdominoperineal and Sphincter-Preserving Resection in Nonmetastatic Rectal Cancer: A Population-Based Time-Trend and Propensity Score-Matched SEER Analysis

Directory of Open Access Journals (Sweden)

Rene Warschkow

2017-01-01

Full Text Available Background. Abdominoperineal resection (APR has been associated with impaired survival in nonmetastatic rectal cancer patients. It is unclear whether this adverse outcome is due to the surgical procedure itself or is a consequence of tumor-related characteristics. Study Design. Patients were identified from the Surveillance, Epidemiology, and End Results database. The impact of APR compared to coloanal anastomosis (CAA on survival was assessed by Cox regression and propensity-score matching. Results. In 36,488 patients with rectal cancer resection, the APR rate declined from 31.8% in 1998 to 19.2% in 2011, with a significant trend change in 2004 at 21.6% (P<0.001. To minimize a potential time-trend bias, survival analysis was limited to patients diagnosed after 2004. APR was associated with an increased risk of cancer-specific mortality after unadjusted analysis (HR = 1.61, 95% CI: 1.28–2.03, P<0.01 and multivariable adjustment (HR = 1.39, 95% CI: 1.10–1.76, P<0.01. After optimal adjustment of highly biased patient characteristics by propensity-score matching, APR was not identified as a risk factor for cancer-specific mortality (HR = 0.85, 95% CI: 0.56–1.29, P=0.456. Conclusions. The current propensity score-adjusted analysis provides evidence that worse oncological outcomes in patients undergoing APR compared to CAA are caused by different patient characteristics and not by the surgical procedure itself.

The Impact of Chemoembolization Endpoints on Survival in Hepatocellular Carcinoma Patients

Science.gov (United States)

Jin, Brian; Wang, Dingxin; Lewandowski, Robert J.; Riaz, Ahsun; Ryu, Robert K.; Sato, Kent T.; Larson, Andrew C.; Salem, Riad; Omary, Reed A.

2010-01-01

OBJECTIVE To investigate the relationship between angiographic embolic endpoints of transarterial chemoembolization (TACE) and survival in patients with hepatocellular carcinoma (HCC). MATERIALS AND METHODS This study retrospectively assessed 105 patients with surgically unresectable HCC who underwent TACE. Patients were classified according to a previously established subjective angiographic chemoembolization endpoint (SACE) scale. Only one patient was classified as SACE level 1 and thus excluded from all subsequent analysis. Survival was evaluated with Kaplan-Meier analysis. Multivariate analysis with Cox’s proportional hazard regression model was used to determine independent prognostic risk factors of survival. RESULTS Overall median survival was 21.1 months (95% confidence interval [CI], 15.9–26.4). Patients embolized to SACE levels 2 and 3 were aggregated and had a significantly higher median survival (25.6 months; 95% CI, 16.2–35.0) than patients embolized to SACE level 4 (17.1 months; 95% CI, 13.3–20.9) (p = 0.035). Multivariate analysis indicated that SACE level 4 (Hazard ratio [HR], 2.49; 95% CI, 1.41–4.42; p = 0.002), European Cooperative Oncology Group performance status > 0 (HR, 1.97; 95% CI, 1.15–3.37; p = 0.013), American Joint Committee on Cancer stage 3 or 4 (HR, 2.42; 95% CI, 1.27–4.60; p = 0.007), and Child-Pugh class B (HR, 1.94; 95% CI, 1.09–3.46; p = 0.025) were all independent negative prognostic indicators of survival. CONCLUSION Embolization to an intermediate, sub-stasis endpoint (SACE levels 2 and 3) during TACE improves survival compared to embolization to a higher, stasis endpoint (SACE level 4). Interventional oncologists should consider targeting these intermediate, sub-stasis angiographic endpoints during TACE. PMID:21427346

Evaluating disease management program effectiveness: an introduction to survival analysis.

Science.gov (United States)

Linden, Ariel; Adams, John L; Roberts, Nancy

2004-01-01

Currently, the most widely used method in the disease management industry for evaluating program effectiveness is the "total population approach." This model is a pretest-posttest design, with the most basic limitation being that without a control group, there may be sources of bias and/or competing extraneous confounding factors that offer plausible rationale explaining the change from baseline. Survival analysis allows for the inclusion of data from censored cases, those subjects who either "survived" the program without experiencing the event (e.g., achievement of target clinical levels, hospitalization) or left the program prematurely, due to disenrollement from the health plan or program, or were lost to follow-up. Additionally, independent variables may be included in the model to help explain the variability in the outcome measure. In order to maximize the potential of this statistical method, validity of the model and research design must be assured. This paper reviews survival analysis as an alternative, and more appropriate, approach to evaluating DM program effectiveness than the current total population approach.

The tourism and travel industry and its effect on the Great Recession: A multilevel survival analysis

Directory of Open Access Journals (Sweden)

Zdravko Šergo

2017-12-01

Full Text Available Does a country with a heavy dependence on a tourism economy have a tendency to succumb to more risk in a recession? With the shift from manufacturing-based economies in the developing world toward service-based industries, including tourism, a reliance on the tourism industry may erode economic stability in tourism-based countries, making them more prone to fall into a recession due to higher risks. In this paper, we wish to emphasise the positive impact of tourism specialisation indices in the international economy on the probability occurrence of a so-called Great Recession. This article uses a multilevel survival analysis and a generalised linear mixed-effect (GLMM structure modelling to investigate the impact of tourism development on the probability of recession frequency (risk in terms of months of duration and severity, by using data collected from 2007 to 2013 from 71 countries around the world, to see if recession frequency is positively correlated with the various indicators of tourism development. Two GLMMs were fitted to this data: logistic regression and count regression with a Poisson distribution. Results for both regressions show considerable evidence that the ratio between the number of overnight stays and the resident population and travel services as a percentage of commercial service exports positively impacts the probability for a country (from our sample to experience a recession event and can make recession worse in terms of severity, measured in months.

Treatment and survival outcomes of small cell carcinoma of the esophagus: an analysis of the National Cancer Data Base.

Science.gov (United States)

Wong, Andrew T; Shao, Meng; Rineer, Justin; Osborn, Virginia; Schwartz, David; Schreiber, David

2017-02-01

Given the paucity of esophageal small cell carcinoma (SCC) cases, there are few large studies evaluating this disease. In this study, the National Cancer Data Base (NCDB) was utilized to analyze the clinical features, treatment, and survival of patients with esophageal SCC in a large, population-based dataset. We selected patients diagnosed with esophageal SCC from 1998 to 2011. Patients were identified as having no treatment, chemotherapy alone, radiation ± sequential chemotherapy, concurrent chemoradiation, and esophagectomy ± chemotherapy and/or radiation. Overall survival (OS) was analyzed using the Kaplan-Meier method and compared using the log-rank test. Multivariate Cox regression analysis was conducted to identify factors associated with OS. A total of 583 patients were identified. Most patients had stage IV disease (41.7%). Regarding treatment selection, chemoradiation was the most commonly utilized for patients with nonmetasatic disease, whereas chemotherapy alone was most common for metastatic patients. Esophagectomy (median survival 44.9 months with 3 year OS 50.5%) was associated with the best OS for patients with localized (node-negative) disease compared with chemotherapy alone (p < 0.001) or chemoradiation (p = 0.01). For locoregional (node-positive) disease, treatment with chemoradiation resulted in a median survival of 17.8 months and a 3 year OS 31.6%. On multivariate analysis, treatment with chemotherapy alone (p = 0.003) was associated with worse OS while esophagectomy (p = 0.04) was associated with improved OS compared to chemoradiation. Esophageal SCC is an aggressive malignancy with most patients presenting with metastatic disease. Either esophagectomy or chemoradiation as part of multimodality treatment appear to improve OS for selected patients with nonmetastatic disease. © 2016 International Society for Diseases of the Esophagus.

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.

Number of Lymph Nodes Removed and Survival after Gastric Cancer Resection: An Analysis from the US Gastric Cancer Collaborative.

Science.gov (United States)

Gholami, Sepideh; Janson, Lucas; Worhunsky, David J; Tran, Thuy B; Squires, Malcolm Hart; Jin, Linda X; Spolverato, Gaya; Votanopoulos, Konstantinos I; Schmidt, Carl; Weber, Sharon M; Bloomston, Mark; Cho, Clifford S; Levine, Edward A; Fields, Ryan C; Pawlik, Timothy M; Maithel, Shishir K; Efron, Bradley; Norton, Jeffrey A; Poultsides, George A

2015-08-01

Examination of at least 16 lymph nodes (LNs) has been traditionally recommended during gastric adenocarcinoma resection to optimize staging, but the impact of this strategy on survival is uncertain. Because recent randomized trials have demonstrated a therapeutic benefit from extended lymphadenectomy, we sought to investigate the impact of the number of LNs removed on prognosis after gastric adenocarcinoma resection. We analyzed patients who underwent gastrectomy for gastric adenocarcinoma from 2000 to 2012, at 7 US academic institutions. Patients with M1 disease or R2 resections were excluded. Disease-specific survival (DSS) was calculated using the Kaplan-Meier method and compared using log-rank and Cox regression analyses. Of 742 patients, 257 (35%) had 7 to 15 LNs removed and 485 (65%) had ≥16 LNs removed. Disease-specific survival was not significantly longer after removal of ≥16 vs 7 to 15 LNs (10-year survival, 55% vs 47%, respectively; p = 0.53) for the entire cohort, but was significantly improved in the subset of patients with stage IA to IIIA (10-year survival, 74% vs 57%, respectively; p = 0.018) or N0-2 disease (72% vs 55%, respectively; p = 0.023). Similarly, for patients who were classified to more likely be "true N0-2," based on frequentist analysis incorporating both the number of positive and of total LNs removed, the hazard ratio for disease-related death (adjusted for T stage, R status, grade, receipt of neoadjuvant and adjuvant therapy, and institution) significantly decreased as the number of LNs removed increased. The number of LNs removed during gastrectomy for adenocarcinoma appears itself to have prognostic implications for long-term survival. Copyright © 2015 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

Italian regional health system structure and expected cancer survival.

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Vercelli, Marina; Lillini, Roberto; Quaglia, Alberto; Capocaccia, Riccardo

2014-01-01

Few studies deal with the association of socioeconomic and health system resource variables with cancer survival at the Italian regional level, where the greatest number of decisions about social and health policies and resource allocations are taken. The present study aimed to describe the causal relationships between socioeconomic and health system resource factors and regional cancer survival and to compute the expected cancer survival at provincial, regional and area levels. Age-standardized relative survival at 5 years from diagnosis of cases incident in 1995-1998 and followed up to 2004 were derived by gender for 11 sites from the Italian Association of Cancer Registries data bank. The socioeconomic and health system resource variables, describing at a regional level the macro-economy, demography, labor market, and health resources for 1995-2005, came from the Health for All database. A principal components factor analysis was applied to the socioeconomic and health system resource variables. For every site, linear regression models were computed considering the relative survival at 5 years as a dependent variable and the principal components factor analysis factors as independent variables. The factors described the socioeconomic and health-related features of the regional systems and were causally related to the characteristics of the patient taken in charge. The models built by the factors allowed computation of the expected relative survival at 5 years with very good concordance with those observed at regional, macro-regional and national levels. In the regions without any cancer registry, survival was coherent with that of neighboring regions with similar socioeconomic and health system resources characteristics. The models highlighted the causal correlations between socioeconomic and health system resources and cancer survival, suggesting that they could be good evaluation tools for the efficiency of the resources allocation and use.

Survival of Sami cancer patients

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Leena Soininen

2012-07-01

Full Text Available Objectives. The incidence of cancer among the indigenous Sami people of Northern Finland is lower than among the Finnish general population. The survival of Sami cancer patients is not known, and therefore it is the object of this study. Study design. The cohort consisted of 2,091 Sami and 4,161 non-Sami who lived on 31 December 1978 in the two Sami municipalities of Inari and Utsjoki, which are located in Northern Finland and are 300–500 km away from the nearest central hospital. The survival experience of Sami and non-Sami cancer patients diagnosed in this cohort during 1979–2009 was compared with that of the Finnish patients outside the cohort. Methods. The Sami and non-Sami cancer patients were matched to other Finnish cancer patients for gender, age and year of diagnosis and for the site of cancer. An additional matching was done for the stage at diagnosis. Cancer-specific survival analyses were made using the Kaplan–Meier method and Cox regression modelling. Results. There were 204 Sami and 391 non-Sami cancer cases in the cohort, 20,181 matched controls without matching with stage, and 7,874 stage-matched controls. In the cancer-specific analysis without stage variable, the hazard ratio for Sami was 1.05 (95% confidence interval 0.85–1.30 and for non-Sami 1.02 (0.86–1.20, indicating no difference between the survival of those groups and other patients in Finland. Likewise, when the same was done by also matching the stage, there was no difference in cancer survival. Conclusion. Long distances to medical care or Sami ethnicity have no influence on the cancer patient survival in Northern Finland.

Survival after radiotherapy in gastric cancer: Systematic review and meta-analysis

International Nuclear Information System (INIS)

Valentini, Vincenzo; Cellini, Francesco; Minsky, Bruce D.; Mattiucci, Gian Carlo; Balducci, Mario; D'Agostino, Giuseppe; D'Angelo, Elisa; Dinapoli, Nicola; Nicolotti, Nicola; Valentini, Chiara; La Torre, Giuseppe

2009-01-01

Background and purpose: A systematic review and meta-analysis was performed to assess the impact of radiotherapy on both 3- and 5-year survival in patients with resectable gastric cancer. Methods: Randomized Clinical Trials (RCTs) in which radiotherapy, (preoperative, postoperative and/or intraoperative), was compared with surgery alone or surgery plus chemotherapy in resectable gastric cancer were identified by searching web-based databases and supplemented by manual examination of reference lists. Meta-analysis was performed using Risk Ratios (RRs). Random or fixed effects models were used to combine data. The methodological quality was evaluated by Chalmers' score. Results: Radiotherapy had a significant impact on 5-year survival. Using an intent to treat (ITT) and a Per Protocol (PP) analysis, the overall 5-year RR was 1.26 (95% CI: 1.08-1.48; NNT = 17) and 1.31 (95% CI: 1.04-1.66; NNT = 13), respectively. Although the quality of the studies was variable, the data were consistent and no clear publication bias was found. Conclusion: This meta-analysis showed a statistically significant 5-year survival benefit with the addition of radiotherapy in patients with resectable gastric cancer. Radiotherapy remains a standard component in the treatment of resectable gastric cancer and new RCTs need to address the impact of new conformal radiotherapy technologies.

Repeated Results Analysis for Middleware Regression Benchmarking

Czech Academy of Sciences Publication Activity Database

Bulej, Lubomír; Kalibera, T.; Tůma, P.

2005-01-01

Roč. 60, - (2005), s. 345-358 ISSN 0166-5316 R&D Projects: GA ČR GA102/03/0672 Institutional research plan: CEZ:AV0Z10300504 Keywords : middleware benchmarking * regression benchmarking * regression testing Subject RIV: JD - Computer Applications, Robotics Impact factor: 0.756, year: 2005

Linear Regression with a Randomly Censored Covariate: Application to an Alzheimer's Study.

Science.gov (United States)

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

2017-01-01

The association between maternal age of onset of dementia and amyloid deposition (measured by in vivo positron emission tomography (PET) imaging) in cognitively normal older offspring is of interest. In a regression model for amyloid, special methods are required due to the random right censoring of the covariate of maternal age of onset of dementia. Prior literature has proposed methods to address the problem of censoring due to assay limit of detection, but not random censoring. We propose imputation methods and a survival regression method that do not require parametric assumptions about the distribution of the censored covariate. Existing imputation methods address missing covariates, but not right censored covariates. In simulation studies, we compare these methods to the simple, but inefficient complete case analysis, and to thresholding approaches. We apply the methods to the Alzheimer's study.

Bayesian Analysis for Penalized Spline Regression Using WinBUGS

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Ciprian M. Crainiceanu

2005-09-01

Full Text Available Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed model software for smoothing. Thus, software originally developed for Bayesian analysis of mixed models can be used for penalized spline regression. Bayesian inference for nonparametric models enjoys the flexibility of nonparametric models and the exact inference provided by the Bayesian inferential machinery. This paper provides a simple, yet comprehensive, set of programs for the implementation of nonparametric Bayesian analysis in WinBUGS. Good mixing properties of the MCMC chains are obtained by using low-rank thin-plate splines, while simulation times per iteration are reduced employing WinBUGS specific computational tricks.

Marital status and survival in patients with rectal cancer: An analysis of the Surveillance, Epidemiology and End Results (SEER) database.

Science.gov (United States)

Wang, Xiangyang; Cao, Weilan; Zheng, Chenguo; Hu, Wanle; Liu, Changbao

2018-06-01

Marital status has been validated as an independent prognostic factor for survival in several cancer types, but is controversial in rectal cancer (RC). The objective of this study was to investigate the impact of marital status on the survival outcomes of patients with RC. We extracted data of 27,498 eligible patients diagnosed with RC between 2004 and 2009 from the Surveillance, Epidemiology and End Results (SEER) database. Patients were categorized into married, never married, divorced/separated and widowed groups.We used Chi-square tests to compare characteristics of patients with different marital status.Rectal cancer specific survival was compared using the Kaplan-Meier method,and multivariate Cox regression analyses was used to analyze the survival outcome risk factors in different marital status. The widowed group had the highest percentage of elderly patients and women,higher proportion of adenocarcinomas, and more stage I/II in tumor stage (P married group (76.7% VS 85.4%). Compared with the married patients, the never married (HR 1.40), widowed (HR 1.61,) and divorced/separated patients (HR 1.16) had an increased overall 5-year mortality. A further analysis showed that widowed patients had an increased overall 5-year cause-specific survival(CSS) compared with married patients at stage I(HR 1.92),stage II (HR 1.65),stage III (HR 1.73),and stage IV (HR 1.38). Our study showed marriage was associated with better outcomes of RC patients, but unmarried RC patients, especially widowed patients,are at greater risk of cancer specific mortality. Copyright © 2018 Elsevier Ltd. All rights reserved.

Forecasting municipal solid waste generation using prognostic tools and regression analysis.

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Ghinea, Cristina; Drăgoi, Elena Niculina; Comăniţă, Elena-Diana; Gavrilescu, Marius; Câmpean, Teofil; Curteanu, Silvia; Gavrilescu, Maria

2016-11-01

For an adequate planning of waste management systems the accurate forecast of waste generation is an essential step, since various factors can affect waste trends. The application of predictive and prognosis models are useful tools, as reliable support for decision making processes. In this paper some indicators such as: number of residents, population age, urban life expectancy, total municipal solid waste were used as input variables in prognostic models in order to predict the amount of solid waste fractions. We applied Waste Prognostic Tool, regression analysis and time series analysis to forecast municipal solid waste generation and composition by considering the Iasi Romania case study. Regression equations were determined for six solid waste fractions (paper, plastic, metal, glass, biodegradable and other waste). Accuracy Measures were calculated and the results showed that S-curve trend model is the most suitable for municipal solid waste (MSW) prediction. Copyright © 2016 Elsevier Ltd. All rights reserved.

Development of Compressive Failure Strength for Composite Laminate Using Regression Analysis Method

Energy Technology Data Exchange (ETDEWEB)

Lee, Myoung Keon [Agency for Defense Development, Daejeon (Korea, Republic of); Lee, Jeong Won; Yoon, Dong Hyun; Kim, Jae Hoon [Chungnam Nat’l Univ., Daejeon (Korea, Republic of)

2016-10-15

This paper provides the compressive failure strength value of composite laminate developed by using regression analysis method. Composite material in this document is a Carbon/Epoxy unidirection(UD) tape prepreg(Cycom G40-800/5276-1) cured at 350°F(177°C). The operating temperature is –60°F~+200°F(-55°C - +95°C). A total of 56 compression tests were conducted on specimens from eight (8) distinct laminates that were laid up by standard angle layers (0°, +45°, –45° and 90°). The ASTM-D-6484 standard was used for test method. The regression analysis was performed with the response variable being the laminate ultimate fracture strength and the regressor variables being two ply orientations (0° and ±45°)

Development of Compressive Failure Strength for Composite Laminate Using Regression Analysis Method

International Nuclear Information System (INIS)

Lee, Myoung Keon; Lee, Jeong Won; Yoon, Dong Hyun; Kim, Jae Hoon

2016-01-01

This paper provides the compressive failure strength value of composite laminate developed by using regression analysis method. Composite material in this document is a Carbon/Epoxy unidirection(UD) tape prepreg(Cycom G40-800/5276-1) cured at 350°F(177°C). The operating temperature is –60°F~+200°F(-55°C - +95°C). A total of 56 compression tests were conducted on specimens from eight (8) distinct laminates that were laid up by standard angle layers (0°, +45°, –45° and 90°). The ASTM-D-6484 standard was used for test method. The regression analysis was performed with the response variable being the laminate ultimate fracture strength and the regressor variables being two ply orientations (0° and ±45°)

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…

Clinical Nomogram for Predicting Survival Outcomes in Early Mucinous Breast Cancer.

Directory of Open Access Journals (Sweden)

Jianfei Fu

regression model, as a more rational model, is recommended for use in the survival analysis of patients with MBC in the future.

CADDIS Volume 4. Data Analysis: PECBO Appendix - R Scripts for Non-Parametric Regressions

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Script for computing nonparametric regression analysis. Overview of using scripts to infer environmental conditions from biological observations, statistically estimating species-environment relationships, statistical scripts.

Bernstein - Von Mises theorem and its application in survival analysis

Czech Academy of Sciences Publication Activity Database

Timková, Jana

2010-01-01

Roč. 22, č. 3 (2010), s. 115-122 ISSN 1210-8022. [16. letní škola JČMF Robust 2010. Králíky, 30.01.2010-05.02.2010] R&D Projects: GA AV ČR(CZ) IAA101120604 Institutional research plan: CEZ:AV0Z10750506 Keywords : Cox model * bayesian asymptotics * survival function Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2010/SI/timkova-bernstein - von mises theorem and its application in survival analysis.pdf

Aneurysmal subarachnoid hemorrhage prognostic decision-making algorithm using classification and regression tree analysis.

Science.gov (United States)

Lo, Benjamin W Y; Fukuda, Hitoshi; Angle, Mark; Teitelbaum, Jeanne; Macdonald, R Loch; Farrokhyar, Forough; Thabane, Lehana; Levine, Mitchell A H

2016-01-01

Classification and regression tree analysis involves the creation of a decision tree by recursive partitioning of a dataset into more homogeneous subgroups. Thus far, there is scarce literature on using this technique to create clinical prediction tools for aneurysmal subarachnoid hemorrhage (SAH). The classification and regression tree analysis technique was applied to the multicenter Tirilazad database (3551 patients) in order to create the decision-making algorithm. In order to elucidate prognostic subgroups in aneurysmal SAH, neurologic, systemic, and demographic factors were taken into account. The dependent variable used for analysis was the dichotomized Glasgow Outcome Score at 3 months. Classification and regression tree analysis revealed seven prognostic subgroups. Neurological grade, occurrence of post-admission stroke, occurrence of post-admission fever, and age represented the explanatory nodes of this decision tree. Split sample validation revealed classification accuracy of 79% for the training dataset and 77% for the testing dataset. In addition, the occurrence of fever at 1-week post-aneurysmal SAH is associated with increased odds of post-admission stroke (odds ratio: 1.83, 95% confidence interval: 1.56-2.45, P tree was generated, which serves as a prediction tool to guide bedside prognostication and clinical treatment decision making. This prognostic decision-making algorithm also shed light on the complex interactions between a number of risk factors in determining outcome after aneurysmal SAH.

Survival Function Analysis of Planet Size Distribution

OpenAIRE

Zeng, Li; Jacobsen, Stein B.; Sasselov, Dimitar D.; Vanderburg, Andrew

2018-01-01

Applying the survival function analysis to the planet radius distribution of the Kepler exoplanet candidates, we have identified two natural divisions of planet radius at 4 Earth radii and 10 Earth radii. These divisions place constraints on planet formation and interior structure model. The division at 4 Earth radii separates small exoplanets from large exoplanets above. When combined with the recently-discovered radius gap at 2 Earth radii, it supports the treatment of planets 2-4 Earth rad...

Survival analysis with functional covariates for partial follow-up studies.

Science.gov (United States)

Fang, Hong-Bin; Wu, Tong Tong; Rapoport, Aaron P; Tan, Ming

2016-12-01

Predictive or prognostic analysis plays an increasingly important role in the era of personalized medicine to identify subsets of patients whom the treatment may benefit the most. Although various time-dependent covariate models are available, such models require that covariates be followed in the whole follow-up period. This article studies a new class of functional survival models where the covariates are only monitored in a time interval that is shorter than the whole follow-up period. This paper is motivated by the analysis of a longitudinal study on advanced myeloma patients who received stem cell transplants and T cell infusions after the transplants. The absolute lymphocyte cell counts were collected serially during hospitalization. Those patients are still followed up if they are alive after hospitalization, while their absolute lymphocyte cell counts cannot be measured after that. Another complication is that absolute lymphocyte cell counts are sparsely and irregularly measured. The conventional method using Cox model with time-varying covariates is not applicable because of the different lengths of observation periods. Analysis based on each single observation obviously underutilizes available information and, more seriously, may yield misleading results. This so-called partial follow-up study design represents increasingly common predictive modeling problem where we have serial multiple biomarkers up to a certain time point, which is shorter than the total length of follow-up. We therefore propose a solution to the partial follow-up design. The new method combines functional principal components analysis and survival analysis with selection of those functional covariates. It also has the advantage of handling sparse and irregularly measured longitudinal observations of covariates and measurement errors. Our analysis based on functional principal components reveals that it is the patterns of the trajectories of absolute lymphocyte cell counts, instead of

Factors associated with improved survival among older colorectal cancer patients in the US: a population-based analysis

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Earle Craig C

2009-07-01

Full Text Available Abstract Background The purpose of this study was to estimate the relative impact of changes in demographics, stage at detection, treatment mix, and medical technology on 5-year survival among older colorectal cancer (CRC patients. Methods We selected older patients diagnosed with CRC between 1992 and 2000 from the SEER-Medicare database and followed them through 2005. Trends in demographic characteristics, stage at detection and initial treatment mix were evaluated descriptively. Separate multivariate logistic regression models for colon (CC and rectal cancer (RC patients were estimated to isolate the independent effects of these factors along with technological change (proxied by cohort year on 5-year survival. Results Our sample included 37,808 CC and 13,619 RC patients (combined mean ± SD age: 77.2 ± 7.0 years; 55% female; 87% white. In recent years, more CC patients were diagnosed at Stage I and fewer at Stages II and IV, and more RC patients were diagnosed at Stage I and fewer at Stages II and III. CC and RC patients diagnosed in later years were slightly older with somewhat better Charlson scores and were more likely to be female, from the Northeast, and from areas with higher average education levels. Surgery alone was more common in later years for CC patients while combined surgery, chemotherapy, and radiotherapy was more common for RC patients. Between 1992 and 2000, 5-year observed survival improved from 43.0% to 46.3% for CC patients and from 39.4% to 42.2% for RC patients. Multivariate logistic regressions indicate that patients diagnosed in 2000 had significantly greater odds of 5-year survival than those diagnosed in 1992 (OR: 1.35 for CC, 1.38 for RC. Our decomposition suggests that early detection had little impact on survival; rather, technological improvements (e.g., new medical technologies or more effective use of existing technologies and changing demographics were responsible for the largest share of the change in 5

Inferring gene expression dynamics via functional regression analysis

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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.

Improved survival after an out-of-hospital cardiac arrest using new guidelines

DEFF Research Database (Denmark)

Steinmetz, Jacob; Barnung, S.; Nielsen, S.L.

2008-01-01

BACKGROUND: An out-of-hospital cardiac arrest (OHCA) is associated with a poor prognosis. We hypothesized that the implementations of 2005 European Resuscitation Council resuscitation guidelines were associated with improved 30-day survival after OHCA. METHODS: We prospectively recorded data on all....... Treatment after implementation was confirmed as a significant predictor of better 30-day survival in a logistic regression analysis. CONCLUSION: The implementation of new resuscitation guidelines was associated with improved 30-day survival after OHCA Udgivelsesdato: 2008/8...... patients with OHCA treated by the Mobile Emergency Care Unit of Copenhagen in two periods: 1 June 2004 until 31 August 2005 (before implementation) and 1 January 2006 until 31 March 2007 (after implementation), separated by a 4-month period in which the above-mentioned change took place. RESULTS: We found...

Application of survival analysis methodology to the quantitative analysis of LC-MS proteomics data

KAUST Repository

Tekwe, C. D.; Carroll, R. J.; Dabney, A. R.

2012-01-01

positive, skewed and often left-censored, we propose using survival methodology to carry out differential expression analysis of proteins. Various standard statistical techniques including non-parametric tests such as the Kolmogorov-Smirnov and Wilcoxon

Use of generalized ordered logistic regression for the analysis of multidrug resistance data.

Science.gov (United States)

Agga, Getahun E; Scott, H Morgan

2015-10-01

Statistical analysis of antimicrobial resistance data largely focuses on individual antimicrobial's binary outcome (susceptible or resistant). However, bacteria are becoming increasingly multidrug resistant (MDR). Statistical analysis of MDR data is mostly descriptive often with tabular or graphical presentations. Here we report the applicability of generalized ordinal logistic regression model for the analysis of MDR data. A total of 1,152 Escherichia coli, isolated from the feces of weaned pigs experimentally supplemented with chlortetracycline (CTC) and copper, were tested for susceptibilities against 15 antimicrobials and were binary classified into resistant or susceptible. The 15 antimicrobial agents tested were grouped into eight different antimicrobial classes. We defined MDR as the number of antimicrobial classes to which E. coli isolates were resistant ranging from 0 to 8. Proportionality of the odds assumption of the ordinal logistic regression model was violated only for the effect of treatment period (pre-treatment, during-treatment and post-treatment); but not for the effect of CTC or copper supplementation. Subsequently, a partially constrained generalized ordinal logistic model was built that allows for the effect of treatment period to vary while constraining the effects of treatment (CTC and copper supplementation) to be constant across the levels of MDR classes. Copper (Proportional Odds Ratio [Prop OR]=1.03; 95% CI=0.73-1.47) and CTC (Prop OR=1.1; 95% CI=0.78-1.56) supplementation were not significantly associated with the level of MDR adjusted for the effect of treatment period. MDR generally declined over the trial period. In conclusion, generalized ordered logistic regression can be used for the analysis of ordinal data such as MDR data when the proportionality assumptions for ordered logistic regression are violated. Published by Elsevier B.V.

Evaluation of clinical and histopathologic prognostic factors for survival in canine osteosarcoma of the extracranial flat and irregular bones.

Science.gov (United States)

Kruse, M A; Holmes, E S; Balko, J A; Fernandez, S; Brown, D C; Goldschmidt, M H

2013-07-01

Osteosarcoma is the most common bone tumor in dogs. However, current literature focuses primarily on appendicular osteosarcoma. This study examined the prognostic value of histological and clinical factors in flat and irregular bone osteosarcomas and hypothesized that clinical factors would have a significant association with survival time while histological factors would not. All osteosarcoma biopsy samples of the vertebra, rib, sternum, scapula, or pelvis were reviewed while survival information and clinical data were obtained from medical records, veterinarians, and owners. Forty-six dogs were included in the analysis of histopathological variables and 27 dogs with complete clinical data were included in the analysis of clinical variables. In the histopathologic cox regression model, there was no significant association between any histologic feature of osteosarcoma, including grade, and survival time. In the clinical cox regression model, there was a significant association between the location of the tumor and survival time as well as between the percent elevation of alkaline phosphatase (ALP) above normal and survival time. Controlling for ALP elevation, dogs with osteosarcoma located in the scapula had a significantly greater hazard for death (2.8) compared to dogs with tumors in other locations. Controlling for tumor location, every 100% increase in ALP from normal increased the hazard for death by 1.7. For canine osteosarcomas of the flat and irregular bones, histopathological features, including grade do not appear to be rigorous predictors of survival. Clinical variables such as increased ALP levels and tumor location in the scapula were associated with decreased survival times.

Regression analysis of growth responses to water depth in three wetland plant species

DEFF Research Database (Denmark)

Sorrell, Brian K; Tanner, Chris C; Brix, Hans

2012-01-01

depths from 0 – 0.5 m. Morphological and growth responses to depth were followed for 54 days before harvest, and then analysed by repeated measures analysis of covariance, and non-linear and quantile regression analysis (QRA), to compare flooding tolerances. Principal results Growth responses to depth...

A SOCIOLOGICAL ANALYSIS OF THE CHILDBEARING COEFFICIENT IN THE ALTAI REGION BASED ON METHOD OF FUZZY LINEAR REGRESSION

Directory of Open Access Journals (Sweden)

Sergei Vladimirovich Varaksin

2017-06-01

Full Text Available Purpose. Construction of a mathematical model of the dynamics of childbearing change in the Altai region in 2000–2016, analysis of the dynamics of changes in birth rates for multiple age categories of women of childbearing age. Methodology. A auxiliary analysis element is the construction of linear mathematical models of the dynamics of childbearing by using fuzzy linear regression method based on fuzzy numbers. Fuzzy linear regression is considered as an alternative to standard statistical linear regression for short time series and unknown distribution law. The parameters of fuzzy linear and standard statistical regressions for childbearing time series were defined with using the built in language MatLab algorithm. Method of fuzzy linear regression is not used in sociological researches yet. Results. There are made the conclusions about the socio-demographic changes in society, the high efficiency of the demographic policy of the leadership of the region and the country, and the applicability of the method of fuzzy linear regression for sociological analysis.

Hepatitis B virus mutation may play a role in hepatocellular carcinoma recurrence: A systematic review and meta-regression analysis.

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Zhou, Hua-ying; Luo, Yue; Chen, Wen-dong; Gong, Guo-zhong

2015-06-01

A number of studies have confirmed that antiviral therapy with nucleotide analogs (NAs) can improve the prognosis of hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) after curative therapy. However, what factors affected the prognosis of HBV-HCC after removal of the primary tumor and inhibition of HBV replication? A meta-regression analysis was conducted to explore the prognostic factor for this subgroup of patients. MEDLINE, EMBASE, Web of Science, and Cochrane library were searched from January 1995 to February 2014 for clinical trials evaluating the effect of NAs on the prognosis of HBV-HCC after curative therapy. Data were extracted for host, viral, and intervention information. Single-arm meta-analysis was performed to assess overall survival (OS) rates and HCC recurrence. Meta-regression analysis was carried out to explore risk factors for 1-year OS rate and HCC recurrence for HBV-HCC patients after curative therapy and antiviral therapy. Fourteen observational studies with 1284 patients met the inclusion criteria. Influential factors for prognosis of HCC were mainly baseline HBeAg positivity, cirrhotic stage, advanced Tumor-Node-Metastasis (TNM) stage, macrovascular invasion, and antiviral agent type. The 1-year OS rate decreased by more than four times (coefficient -4.45, P<0.001) and the 1-year HCC recurrence increased by more than one time (coefficient 1.20, P=0.003) when lamivudine was chosen for HCC after curative therapy, relative to entecavir for HCC. HBV mutation may play a role in HCC recurrence. Entecavir or tenofovir, a high genetic barrier to resistance, should be recommended for HBV-HCC patients. © 2015 The Authors. Journal of Gastroenterology and Hepatology published by Journal of Gastroenterology and Hepatology Foundation and Wiley Publishing Asia Pty Ltd.

Survival rates and predictors of survival among colorectal cancer patients in a Malaysian tertiary hospital.

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Magaji, Bello Arkilla; Moy, Foong Ming; Roslani, April Camilla; Law, Chee Wei

2017-05-18

Colorectal cancer is the third most commonly diagnosed malignancy and the fourth leading cause of cancer-related death globally. It is the second most common cancer among both males and females in Malaysia. The economic burden of colorectal cancer is likely to increase over time owing to its current trend and aging population. Cancer survival analysis is an essential indicator for early detection and improvement in cancer treatment. However, there was a scarcity of studies concerning survival of colorectal cancer patients as well as its predictors. Therefore, we aimed to determine the 1-, 3- and 5-year survival rates, compare survival rates among ethnic groups and determine the predictors of survival among colorectal cancer patients. This was an ambidirectional cohort study conducted at the University Malaya Medical Centre (UMMC) in Kuala Lumpur, Malaysia. All Malaysian citizens or permanent residents with histologically confirmed diagnosis of colorectal cancer seen at UMMC from 1 January 2001 to 31 December 2010 were included in the study. Demographic and clinical characteristics were extracted from the medical records. Patients were followed-up until death or censored at the end of the study (31st December 2010). Censored patients' vital status (whether alive or dead) were cross checked with the National Registration Department. Survival analyses at 1-, 3- and 5-year intervals were performed using the Kaplan-Meier method. Log-rank test was used to compare the survival rates, while Cox proportional hazard regression analysis was carried out to determine the predictors of 5-year colorectal cancer survival. Among 1212 patients, the median survival for colorectal, colon and rectal cancers were 42.0, 42.0 and 41.0 months respectively; while the 1-, 3-, and 5-year relative survival rates ranged from 73.8 to 76.0%, 52.1 to 53.7% and 40.4 to 45.4% respectively. The Chinese patients had the lowest 5-year survival compared to Malay and Indian patients. Based on the 814

The Poor Survival among Pulmonary Tuberculosis Patients in Chiapas, Mexico: The Case of Los Altos Region

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Nájera-Ortiz, J. C.; Sánchez-Pérez, H. J.; Ochoa-Díaz-López, H.; Leal-Fernández, G.; Navarro-Giné, A.

2012-01-01

Objective. To analyse survival in patients with pulmonary tuberculosis (PTB) and factors associated with such survival. Design. Study of a cohort of patients aged over 14 years diagnosed with PTB from January 1, 1998 to July 31, 2005. During 2004–2006 a home visit was made to each patient and, during 2008-2009, they were visited again. During these visits a follow-up interview was administered; when the patient had died, a verbal autopsy was conducted with family members. Statistical analysis consisted of survival tests, Kaplan-Meier log-rank test and Cox regression. Results. Of 305 studied patients, 68 had died due to PTB by the time of the first evaluation, 237 were followed-up for a second evaluation, and 10 of them had died of PTB. According to the Cox regression, age (over 45 years) and treatment duration (under six months) were associated with a poorer survival. When treatment duration was excluded, the association between poorer survival with age persisted, whereas with having been treated via DOTS strategy, was barely significant. Conclusions. In the studied area it is necessary that patients receive a complete treatment scheme, and to give priority to patients aged over 45 years. PMID:22701170

The Poor Survival among Pulmonary Tuberculosis Patients in Chiapas, Mexico: The Case of Los Altos Region

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J. C. Nájera-Ortiz

2012-01-01

Full Text Available Objective. To analyse survival in patients with pulmonary tuberculosis (PTB and factors associated with such survival. Design. Study of a cohort of patients aged over 14 years diagnosed with PTB from January 1, 1998 to July 31, 2005. During 2004–2006 a home visit was made to each patient and, during 2008-2009, they were visited again. During these visits a follow-up interview was administered; when the patient had died, a verbal autopsy was conducted with family members. Statistical analysis consisted of survival tests, Kaplan-Meier log-rank test and Cox regression. Results. Of 305 studied patients, 68 had died due to PTB by the time of the first evaluation, 237 were followed-up for a second evaluation, and 10 of them had died of PTB. According to the Cox regression, age (over 45 years and treatment duration (under six months were associated with a poorer survival. When treatment duration was excluded, the association between poorer survival with age persisted, whereas with having been treated via DOTS strategy, was barely significant. Conclusions. In the studied area it is necessary that patients receive a complete treatment scheme, and to give priority to patients aged over 45 years.

Multiple Logistic Regression Analysis of Cigarette Use among High School Students

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Adwere-Boamah, Joseph

2011-01-01

A binary logistic regression analysis was performed to predict high school students' cigarette smoking behavior from selected predictors from 2009 CDC Youth Risk Behavior Surveillance Survey. The specific target student behavior of interest was frequent cigarette use. Five predictor variables included in the model were: a) race, b) frequency of…

Analysis of survival for patients with chronic kidney disease primarily related to renal cancer surgery.

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Wu, Jitao; Suk-Ouichai, Chalairat; Dong, Wen; Antonio, Elvis Caraballo; Derweesh, Ithaar H; Lane, Brian R; Demirjian, Sevag; Li, Jianbo; Campbell, Steven C

2018-01-01

To evaluate predictors of long-term survival for patients with chronic kidney disease primarily due to surgery (CKD-S). Patients with CKD-S have generally good survival that approximates patients who do not have CKD even after renal cancer surgery (RCS), yet there may be heterogeneity within this cohort. From 1997 to 2008, 4 246 patients underwent RCS at our centre. The median (interquartile range [IQR]) follow-up was 9.4 (7.3-11.0) years. New baseline glomerular filtration rate (GFR) was defined as highest GFR between nadir and 6 weeks after RCS. We retrospectively evaluated three cohorts: no-CKD (new baseline GFR of ≥60 mL/min/1.73 m 2 ); CKD-S (new baseline GFR of cancer-related survival (NRCRS) for the CKD-S cohort. Kaplan-Meier analysis assessed the longitudinal impact of new baseline GFR (45-60 mL/min/1.73 m 2 vs <45 mL/min/1.73 m 2 ) and Cox regression evaluated relative impact of preoperative GFR, new baseline GFR, and relevant demographics/comorbidities. Of the 4 246 patients who underwent RCS, 931 had CKD-S and 1 113 had CKD-M/S, whilst 2 202 had no-CKD even after RCS. Partial/radical nephrectomy (PN/RN) was performed in 54%/46% of the patients, respectively. For CKD-S, 641 patients had a new baseline GFR of 45-60 mL/min/1.73 m 2 and 290 had a new baseline GFR of <45 mL/min/1.73 m 2 . Kaplan-Meier analysis showed significantly reduced NRCRS for patients with CKD-S with a GFR of <45 mL/min/1.73 m 2 compared to those with no-CKD or CKD-S with a GFR of 45-60 mL/min/1.73 m 2 (both P ≤ 0.004), and competing risk analysis confirmed this (P < 0.001). Age, gender, heart disease, and new baseline GFR were all associated independently with NRCRS for patients with CKD-S (all P ≤ 0.02). Our data suggest that CKD-S is heterogeneous, and patients with a reduced new baseline GFR have compromised survival, particularly if <45 mL/min/1.73 m 2 . Our findings may have implications regarding choice of PN/RN in patients at risk of developing

[11C]Choline PET/CT predicts survival in hormone-naive prostate cancer patients with biochemical failure after radical prostatectomy

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Giovacchini, Giampiero; Incerti, Elena; Mapelli, Paola; Gianolli, Luigi; Picchio, Maria; Kirienko, Margarita; Briganti, Alberto; Gandaglia, Giorgio; Montorsi, Francesco

2015-01-01

Over the last decade, PET/CT with radiolabelled choline has been shown to be useful for restaging patients with prostate cancer (PCa) who develop biochemical failure. The limitations of most clinical studies have been poor validation of [ 11 C]choline PET/CT-positive findings and lack of survival analysis. The aim of this study was to assess whether [ 11 C]choline PET/CT can predict survival in hormone-naive PCa patients with biochemical failure. This retrospective study included 302 hormone-naive PCa patients treated with radical prostatectomy who underwent [ 11 C]choline PET/CT from 1 December 2004 to 31 July 2007 because of biochemical failure (prostate-specific antigen, PSA, >0.2 ng/mL). Median PSA was 1.02 ng/mL. PCa-specific survival was estimated using Kaplan-Meier curves. Cox regression analysis was used to evaluate the association between clinicopathological variables and PCa-specific survival. The coefficients of the covariates included in the Cox regression analysis were used to develop a novel nomogram. Median follow-up was 7.2 years (1.4 - 18.9 years). [ 11 C]Choline PET/CT was positive in 101 of 302 patients (33 %). Median PCa-specific survival after prostatectomy was 14.9 years (95 % CI 9.7 - 20.1 years) in patients with positive [ 11 C]choline PET/CT. Median survival was not achieved in patients with negative [ 11 C]choline PET/CT. The 15-year PCa-specific survival probability was 42.4 % (95 % CI 31.7 - 53.1 %) in patients with positive [ 11 C]choline PET/CT and 95.5 % (95 % CI 93.5 - 97.5 %) in patients with negative [ 11 C]choline PET/CT. In multivariate analysis, [ 11 C]choline PET/CT (hazard ratio 6.36, 95 % CI 2.14 - 18.94, P < 0.001) and Gleason score >7 (hazard ratio 3.11, 95 % CI 1.11 - 8.66, P = 0.030) predicted PCa-specific survival. An internally validated nomogram predicted 15-year PCa-specific survival probability with an accuracy of 80 %. Positive [ 11 C]choline PET/CT after biochemical failure predicts PCa-specific survival in hormone

THE PROGNOSIS OF RUSSIAN DEFENSE INDUSTRY DEVELOPMENT IMPLEMENTED THROUGH REGRESSION ANALYSIS

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L.M. Kapustina

2007-03-01

Full Text Available The article illustrates the results of investigation the major internal and external factors which influence the development of the defense industry, as well as the results of regression analysis which quantitatively displays the factorial contribution in the growth rate of Russian defense industry. On the basis of calculated regression dependences the authors fulfilled the medium-term prognosis of defense industry. Optimistic and inertial versions of defense product growth rate for the period up to 2009 are based on scenario conditions in Russian economy worked out by the Ministry of economy and development. In conclusion authors point out which factors and conditions have the largest impact on successful and stable operation of Russian defense industry.

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

The median hazard ratio: a useful measure of variance and general contextual effects in multilevel survival analysis.

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Austin, Peter C; Wagner, Philippe; Merlo, Juan

2017-03-15

Multilevel data occurs frequently in many research areas like health services research and epidemiology. A suitable way to analyze such data is through the use of multilevel regression models (MLRM). MLRM incorporate cluster-specific random effects which allow one to partition the total individual variance into between-cluster variation and between-individual variation. Statistically, MLRM account for the dependency of the data within clusters and provide correct estimates of uncertainty around regression coefficients. Substantively, the magnitude of the effect of clustering provides a measure of the General Contextual Effect (GCE). When outcomes are binary, the GCE can also be quantified by measures of heterogeneity like the Median Odds Ratio (MOR) calculated from a multilevel logistic regression model. Time-to-event outcomes within a multilevel structure occur commonly in epidemiological and medical research. However, the Median Hazard Ratio (MHR) that corresponds to the MOR in multilevel (i.e., 'frailty') Cox proportional hazards regression is rarely used. Analogously to the MOR, the MHR is the median relative change in the hazard of the occurrence of the outcome when comparing identical subjects from two randomly selected different clusters that are ordered by risk. We illustrate the application and interpretation of the MHR in a case study analyzing the hazard of mortality in patients hospitalized for acute myocardial infarction at hospitals in Ontario, Canada. We provide R code for computing the MHR. The MHR is a useful and intuitive measure for expressing cluster heterogeneity in the outcome and, thereby, estimating general contextual effects in multilevel survival analysis. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Noninvasive spectral imaging of skin chromophores based on multiple regression analysis aided by Monte Carlo simulation

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Nishidate, Izumi; Wiswadarma, Aditya; Hase, Yota; Tanaka, Noriyuki; Maeda, Takaaki; Niizeki, Kyuichi; Aizu, Yoshihisa

2011-08-01

In order to visualize melanin and blood concentrations and oxygen saturation in human skin tissue, a simple imaging technique based on multispectral diffuse reflectance images acquired at six wavelengths (500, 520, 540, 560, 580 and 600nm) was developed. The technique utilizes multiple regression analysis aided by Monte Carlo simulation for diffuse reflectance spectra. Using the absorbance spectrum as a response variable and the extinction coefficients of melanin, oxygenated hemoglobin, and deoxygenated hemoglobin as predictor variables, multiple regression analysis provides regression coefficients. Concentrations of melanin and total blood are then determined from the regression coefficients using conversion vectors that are deduced numerically in advance, while oxygen saturation is obtained directly from the regression coefficients. Experiments with a tissue-like agar gel phantom validated the method. In vivo experiments with human skin of the human hand during upper limb occlusion and of the inner forearm exposed to UV irradiation demonstrated the ability of the method to evaluate physiological reactions of human skin tissue.

Multiple Regression Analysis of Unconfined Compression Strength of Mine Tailings Matrices

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Mahmood Ali A.

2017-01-01

Full Text Available As part of a novel approach of sustainable development of mine tailings, experimental and numerical analysis is carried out on newly formulated tailings matrices. Several physical characteristic tests are carried out including the unconfined compression strength test to ascertain the integrity of these matrices when subjected to loading. The current paper attempts a multiple regression analysis of the unconfined compressive strength test results of these matrices to investigate the most pertinent factors affecting their strength. Results of this analysis showed that the suggested equation is reasonably applicable to the range of binder combinations used.

The Regression Analysis of Individual Financial Performance: Evidence from Croatia

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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...

KRAS polymorphisms are associated with survival of CRC in Chinese population.

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Dai, Qiong; Wei, Hui Lian; Huang, Juan; Zhou, Tie Jun; Chai, Li; Yang, Zhi-Hui

2016-04-01

rs12245, rs12587, rs9266, rs1137282, rs61764370, and rs712 of KRAS oncogene are characterized in the 3'UTR. The study highlights the important role of these polymorphisms playing in the susceptibility, oxaliplatin-based chemotherapy sensitivity, progression, and prognosis of CRC. Improved multiplex ligation detection reaction (iMLDR) technique is used for genotyping. An unconditional logistic regression model was used to estimate the association of certain polymorphism and CRC risk. The Kaplan-Meier method, log-rank test, and Cox regression model were used to evaluate the effects of polymorphisms on survival analysis. Results demonstrated that TT genotype and T allele of rs712 were associated with the increased risk of CRC; the patients with GG genotype and G allele of rs61764370 had a shorter survival and a higher risk of relapse or metastasis of CRC. Our studies supported the conclusions that rs61764370 and rs712 polymorphisms of the KRAS are functional and it may play an important role in the development of CRC and oxaliplatin-based chemotherapy efficiency and prognosis of CRC.

Geographic remoteness, area-level socioeconomic disadvantage and inequalities in colorectal cancer survival in Queensland: a multilevel analysis

Science.gov (United States)

2013-01-01

Background To explore the impact of geographical remoteness and area-level socioeconomic disadvantage on colorectal cancer (CRC) survival. Methods Multilevel logistic regression and Markov chain Monte Carlo simulations were used to analyze geographical variations in five-year all-cause and CRC-specific survival across 478 regions in Queensland Australia for 22,727 CRC cases aged 20–84 years diagnosed from 1997–2007. Results Area-level disadvantage and geographic remoteness were independently associated with CRC survival. After full multivariate adjustment (both levels), patients from remote (odds Ratio [OR]: 1.24, 95%CrI: 1.07-1.42) and more disadvantaged quintiles (OR = 1.12, 1.15, 1.20, 1.23 for Quintiles 4, 3, 2 and 1 respectively) had lower CRC-specific survival than major cities and least disadvantaged areas. Similar associations were found for all-cause survival. Area disadvantage accounted for a substantial amount of the all-cause variation between areas. Conclusions We have demonstrated that the area-level inequalities in survival of colorectal cancer patients cannot be explained by the measured individual-level characteristics of the patients or their cancer and remain after adjusting for cancer stage. Further research is urgently needed to clarify the factors that underlie the survival differences, including the importance of geographical differences in clinical management of CRC. PMID:24152961

A systematic review and meta-regression analysis of mivacurium for tracheal intubation

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Vanlinthout, L.E.H.; Mesfin, S.H.; Hens, N.; Vanacker, B.F.; Robertson, E.N.; Booij, L.H.D.J.

2014-01-01

We systematically reviewed factors associated with intubation conditions in randomised controlled trials of mivacurium, using random-effects meta-regression analysis. We included 29 studies of 1050 healthy participants. Four factors explained 72.9% of the variation in the probability of excellent

Effect of smoking on survival of patients with hepatocellular carcinoma.

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Kolly, Philippe; Knöpfli, Marina; Dufour, Jean-François

2017-11-01

Lifestyle factors such as smoking, obesity and physical activity have gained interest in the field of hepatocellular carcinoma. These factors play a significant role in the development of hepatocellular carcinoma. Several studies revealed the impact of tobacco consumption on the development of hepatocellular carcinoma and its synergistic effects with viral etiologies (hepatitis B and C). The effects of smoking on survival in patients with a diagnosed hepatocellular carcinoma have not yet been investigated in a Western cohort where hepatitis C infection is a major risk factor. Using data from a prospective cohort of patients with hepatocellular carcinoma who were followed at the University Hospital of Bern, Switzerland, survival was compared by Kaplan-Meier analysis in smokers and nonsmokers, and multivariate Cox regression was applied to control for confounding variables. Of 238 eligible hepatocellular carcinoma patients, 64 were smokers at the time of inclusion and 174 were nonsmokers. Smokers had a significant worse overall survival than nonsmokers (hazard ratio 1.77, 95% confidence interval: 1.22-2.58, P=.003). Analysis of patients according to their underlying liver disease, revealed that smoking, and not nonsmoking, affected survival of hepatitis B virus and C virus-infected patients only. In this subgroup, smoking was an independent predictor for survival (hazard ratio 2.99, 95% confidence interval: 1.7-5.23, Phepatocellular carcinoma. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Impact of Interstitial Pneumonia on the Survival and Risk Factors Analysis of Patients with Hematological Malignancy

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Wei-Liang Chen

2013-01-01

Full Text Available Background. The emergence of interstitial pneumonia (IP in patients with hematological malignancy (HM is becoming a challenging scenario in current practice. However, detailed characterization and investigation of outcomes and risk factors on survival have not been addressed. Methods. We conducted a retrospective study of 42,584 cancer patients covering the period between 1996 and 2008 using the institutional cancer registry system. Among 816 HM patients, 61 patients with IP were recognized. The clinical features, laboratory results, and histological types were studied to determine the impact of IP on survival and identify the profile of prognostic factors. Results. HM patients with IP showed a significant worse survival than those without IP in the 5-year overall survival (P=0.027. The overall survival showed no significant difference between infectious pneumonia and noninfectious interstitial pneumonia (IIP versus nIIP (P=0.323. In a multivariate Cox regression model, leukocyte and platelet count were associated with increased risk of death. Conclusions. The occurrence of IP in HM patients is associated with increased mortality. Of interest, nIIP is a prognostic indicator in patients with lymphoma but not in patients with leukemia. However, aggressive management of IP in patients with HM is strongly advised, and further prospective survey is warranted.

Trends in Testicular Cancer Survival: A Large Population-based Analysis.

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Sui, Wilson; Morrow, David C; Bermejo, Carlos E; Hellenthal, Nicholas J

2015-06-01

To determine whether discrepancies in testicular cancer outcomes between Caucasians and non-Caucasians are changing over time. Although testicular cancer is more common in Caucasians, studies have shown that other races have worse outcomes. Using the Surveillance, Epidemiology, and End Results registry, we identified 29,803 patients diagnosed with histologically confirmed testicular cancer between 1983 and 2011. Of these, 12,650 patients (42%) had 10-year follow-up data. We stratified the patients by age group, stage, race, and year of diagnosis and assessed 10-year overall and cancer-specific survival in each cohort. Cox proportional hazard models were used to determine the relative contributions of each stratum to cancer-specific survival. Predicted overall 10-year survival of Caucasian patients with testicular cancer increased slightly from 88% to 89% over the period studied, whereas predicted cancer-specific 10-year survival dropped slightly from 94% to 93%. In contrast, non-Caucasian men demonstrated larger changes in 10-year overall (84%-86%) and cancer-specific (88%-91%) survival. On univariate analysis, race was significantly associated with testicular cancer death, with non-Caucasian men being 1.69 times more likely to die of testicular cancer than Caucasians (hazard ratio, 1.33-2.16; 95% confidence interval, testicular cancer. These data show a convergence in cancer-specific survival between racial groups over time, suggesting that diagnostic and treatment discrepancies may be improving for non-Caucasians. Copyright © 2015 Elsevier Inc. All rights reserved.

No rationale for 1 variable per 10 events criterion for binary logistic regression analysis.

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van Smeden, Maarten; de Groot, Joris A H; Moons, Karel G M; Collins, Gary S; Altman, Douglas G; Eijkemans, Marinus J C; Reitsma, Johannes B

2016-11-24

Ten events per variable (EPV) is a widely advocated minimal criterion for sample size considerations in logistic regression analysis. Of three previous simulation studies that examined this minimal EPV criterion only one supports the use of a minimum of 10 EPV. In this paper, we examine the reasons for substantial differences between these extensive simulation studies. The current study uses Monte Carlo simulations to evaluate small sample bias, coverage of confidence intervals and mean square error of logit coefficients. Logistic regression models fitted by maximum likelihood and a modified estimation procedure, known as Firth's correction, are compared. The results show that besides EPV, the problems associated with low EPV depend on other factors such as the total sample size. It is also demonstrated that simulation results can be dominated by even a few simulated data sets for which the prediction of the outcome by the covariates is perfect ('separation'). We reveal that different approaches for identifying and handling separation leads to substantially different simulation results. We further show that Firth's correction can be used to improve the accuracy of regression coefficients and alleviate the problems associated with separation. The current evidence supporting EPV rules for binary logistic regression is weak. Given our findings, there is an urgent need for new research to provide guidance for supporting sample size considerations for binary logistic regression analysis.

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

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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.

Logistic Regression: Concept and Application

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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…

Impact of reconstruction methods and pathological factors on survival after pancreaticoduodenectomy

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Salah Binziad

2013-01-01

Full Text Available Background: Surgery remains the mainstay of therapy for pancreatic head (PH and periampullary carcinoma (PC and provides the only chance of cure. Improvements of surgical technique, increased surgical experience and advances in anesthesia, intensive care and parenteral nutrition have substantially decreased surgical complications and increased survival. We evaluate the effects of reconstruction type, complications and pathological factors on survival and quality of life. Materials and Methods: This is a prospective study to evaluate the impact of various reconstruction methods of the pancreatic remnant after pancreaticoduodenectomy and the pathological characteristics of PC patients over 3.5 years. Patient characteristics and descriptive analysis in the three variable methods either with or without stent were compared with Chi-square test. Multivariate analysis was performed with the logistic regression analysis test and multinomial logistic regression analysis test. Survival rate was analyzed by use Kaplan-Meier test. Results: Forty-one consecutive patients with PC were enrolled. There were 23 men (56.1% and 18 women (43.9%, with a median age of 56 years (16 to 70 years. There were 24 cases of PH cancer, eight cases of PC, four cases of distal CBD cancer and five cases of duodenal carcinoma. Nine patients underwent duct-to-mucosa pancreatico jejunostomy (PJ, 17 patients underwent telescoping pancreatico jejunostomy (PJ and 15 patients pancreaticogastrostomy (PG. The pancreatic duct was stented in 30 patients while in 11 patients, the duct was not stented. The PJ duct-to-mucosa caused significantly less leakage, but longer operative and reconstructive times. Telescoping PJ was associated with the shortest hospital stay. There were 5 postoperative mortalities, while postoperative morbidities included pancreatic fistula-6 patients, delayed gastric emptying in-11, GI fistula-3, wound infection-12, burst abdomen-6 and pulmonary infection-2. Factors

Reduced in-hospital survival rates of out-of-hospital cardiac arrest victims with obstructive pulmonary disease

DEFF Research Database (Denmark)

Blom, M T; Warnier, M J; Bardai, A

2013-01-01

) had comparable survival to ER (75% vs. 78%, OR 0.9 [95% CI: 0.6-1.3]) and to hospital admission (56% vs. 57%, OR 1.0 [0.7-1.4]). However, survival to hospital discharge was significantly lower among OPD patients (21% vs. 33%, OR 0.6 [0.4-0.9]). Multivariate regression analysis among patients who were...... with obstructive pulmonary disease (OPD) have a lower survival rate after OHCA than non-OPD patients. METHODS: We performed a community-based cohort study of 1172 patients with non-traumatic OHCA with ECG-documented VT/VF between 2005 and 2008. We compared survival to emergency room (ER), to hospital admission...... admitted to hospital (OPD: n=100, no OPD: n=561) revealed that OPD was an independent determinant of reduced 30-day survival rate (39% vs. 59%, adjusted OR 0.6 [0.4-1.0, p=0.035]). CONCLUSION: OPD-patients had lower survival rates after OHCA than non-OPD patients. Survival to ER and to hospital admission...

High serum uric acid concentration predicts poor survival in patients with breast cancer.

Science.gov (United States)

Yue, Cai-Feng; Feng, Pin-Ning; Yao, Zhen-Rong; Yu, Xue-Gao; Lin, Wen-Bin; Qian, Yuan-Min; Guo, Yun-Miao; Li, Lai-Sheng; Liu, Min

2017-10-01

Uric acid is a product of purine metabolism. Recently, uric acid has gained much attraction in cancer. In this study, we aim to investigate the clinicopathological and prognostic significance of serum uric acid concentration in breast cancer patients. A total of 443 female patients with histopathologically diagnosed breast cancer were included. After a mean follow-up time of 56months, survival was analysed using the Kaplan-Meier method. To further evaluate the prognostic significance of uric acid concentrations, univariate and multivariate Cox regression analyses were applied. Of the clinicopathological parameters, uric acid concentration was associated with age, body mass index, ER status and PR status. Univariate analysis identified that patients with increased uric acid concentration had a significantly inferior overall survival (HR 2.13, 95% CI 1.15-3.94, p=0.016). In multivariate analysis, we found that high uric acid concentration is an independent prognostic factor predicting death, but insufficient to predict local relapse or distant metastasis. Kaplan-Meier analysis indicated that high uric acid concentration is related to the poor overall survival (p=0.013). High uric acid concentration predicts poor survival in patients with breast cancer, and might serve as a potential marker for appropriate management of breast cancer patients. Copyright © 2017 Elsevier B.V. All rights reserved.

A multiple regression analysis for accurate background subtraction in 99Tcm-DTPA renography

International Nuclear Information System (INIS)

Middleton, G.W.; Thomson, W.H.; Davies, I.H.; Morgan, A.

1989-01-01

A technique for accurate background subtraction in 99 Tc m -DTPA renography is described. The technique is based on a multiple regression analysis of the renal curves and separate heart and soft tissue curves which together represent background activity. It is compared, in over 100 renograms, with a previously described linear regression technique. Results show that the method provides accurate background subtraction, even in very poorly functioning kidneys, thus enabling relative renal filtration and excretion to be accurately estimated. (author)

Multilevel survival analysis of health inequalities in life expectancy

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Merlo Juan

2009-08-01

Full Text Available Abstract Background The health status of individuals is determined by multiple factors operating at both micro and macro levels and the interactive effects of them. Measures of health inequalities should reflect such determinants explicitly through sources of levels and combining mean differences at group levels and the variation of individuals, for the benefits of decision making and intervention planning. Measures derived recently from marginal models such as beta-binomial and frailty survival, address this issue to some extent, but are limited in handling data with complex structures. Beta-binomial models were also limited in relation to measuring inequalities of life expectancy (LE directly. Methods We propose a multilevel survival model analysis that estimates life expectancy based on survival time with censored data. The model explicitly disentangles total health inequalities in terms of variance components of life expectancy compared to the source of variation at the level of individuals in households and parishes and so on, and estimates group differences of inequalities at the same time. Adjusted distributions of life expectancy by gender and by household socioeconomic level are calculated. Relative and absolute health inequality indices are derived based on model estimates. The model based analysis is illustrated on a large Swedish cohort of 22,680 men and 26,474 women aged 65–69 in 1970 and followed up for 30 years. Model based inequality measures are compared to the conventional calculations. Results Much variation of life expectancy is observed at individual and household levels. Contextual effects at Parish and Municipality level are negligible. Women have longer life expectancy than men and lower inequality. There is marked inequality by the level of household socioeconomic status measured by the median life expectancy in each socio-economic group and the variation in life expectancy within each group. Conclusion Multilevel

Development of an empirical model of turbine efficiency using the Taylor expansion and regression analysis

International Nuclear Information System (INIS)

Fang, Xiande; Xu, Yu

2011-01-01

The empirical model of turbine efficiency is necessary for the control- and/or diagnosis-oriented simulation and useful for the simulation and analysis of dynamic performances of the turbine equipment and systems, such as air cycle refrigeration systems, power plants, turbine engines, and turbochargers. Existing empirical models of turbine efficiency are insufficient because there is no suitable form available for air cycle refrigeration turbines. This work performs a critical review of empirical models (called mean value models in some literature) of turbine efficiency and develops an empirical model in the desired form for air cycle refrigeration, the dominant cooling approach in aircraft environmental control systems. The Taylor series and regression analysis are used to build the model, with the Taylor series being used to expand functions with the polytropic exponent and the regression analysis to finalize the model. The measured data of a turbocharger turbine and two air cycle refrigeration turbines are used for the regression analysis. The proposed model is compact and able to present the turbine efficiency map. Its predictions agree with the measured data very well, with the corrected coefficient of determination R c 2 ≥ 0.96 and the mean absolute percentage deviation = 1.19% for the three turbines. -- Highlights: → Performed a critical review of empirical models of turbine efficiency. → Developed an empirical model in the desired form for air cycle refrigeration, using the Taylor expansion and regression analysis. → Verified the method for developing the empirical model. → Verified the model.

In vitro chemo-sensitivity assay guided chemotherapy is associated with prolonged overall survival in cancer patients.

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Udelnow, Andrej; Schönfęlder, Manfred; Würl, Peter; Halloul, Zuhir; Meyer, Frank; Lippert, Hans; Mroczkowski, Paweł

2013-06-01

The overall survival (OS) of patients suffering From various tumour entities was correlated with the results of in vitro-chemosensitivity assay (CSA) of the in vivo applied drugs. Tumour specimen (n=611) were dissected in 514 patients and incubated for primary tumour cell culture. The histocytological regression assay was performed 5 days after adding chemotherapeutic substances to the cell cultures. n=329 patients undergoing chemotherapy were included in the in vitro/in vivo associations. OS was assessed and in vitro response groups compared using survival analysis. Furthermore Cox-regression analysis was performed on OS including CSA, age, TNM classification and treatment course. The growth rate of the primary was 73-96% depending on tumour entity. The in-vitro response rate varied with histology and drugs (e.g. 8-18% for methotrexate and 33-83% for epirubicine). OS was significantly prolonged for patients treated with in vitro effective drugs compared to empiric therapy (log-rank-test, p=0.0435). Cox-regression revealed that application of in vitro effective drugs, residual tumour and postoperative radiotherapy determined the death risk independently. When patients were treated with drugs effective in our CSA, OS was significantly prolonged compared to empiric therapy. CSA guided chemotherapy should be compared to empiric treatment by a prospective randomized trial.

Econometric analysis of realized covariation: high frequency based covariance, regression, and correlation in financial economics

DEFF Research Database (Denmark)

Barndorff-Nielsen, Ole Eiler; Shephard, N.

2004-01-01

This paper analyses multivariate high frequency financial data using realized covariation. We provide a new asymptotic distribution theory for standard methods such as regression, correlation analysis, and covariance. It will be based on a fixed interval of time (e.g., a day or week), allowing...... the number of high frequency returns during this period to go to infinity. Our analysis allows us to study how high frequency correlations, regressions, and covariances change through time. In particular we provide confidence intervals for each of these quantities....

Health care: necessity or luxury good? A meta-regression analysis

OpenAIRE

Iordache, Ioana Raluca

2014-01-01

When estimating the influence income per capita exerts on health care expenditure, the research in the field offers mixed results. Studies employ different data, estimation techniques and models, which brings about the question whether these differences in research design play any part in explaining the heterogeneity of reported outcomes. By employing meta-regression analysis, the present paper analyzes 220 estimates of health spending income elasticity collected from 54 studies and finds tha...

Marital status independently predicts testis cancer survival--an analysis of the SEER database.

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Abern, Michael R; Dude, Annie M; Coogan, Christopher L

2012-01-01

Previous reports have shown that married men with malignancies have improved 10-year survival over unmarried men. We sought to investigate the effect of marital status on 10-year survival in a U.S. population-based cohort of men with testis cancer. We examined 30,789 cases of testis cancer reported to the Surveillance, Epidemiology, and End Results (SEER 17) database between 1973 and 2005. All staging were converted to the 1997 AJCC TNM system. Patients less than 18 years of age at time of diagnosis were excluded. A subgroup analysis of patients with stages I or II non-seminomatous germ cell tumors (NSGCT) was performed. Univariate analysis using t-tests and χ(2) tests compared characteristics of patients separated by marital status. Multivariate analysis was performed using a Cox proportional hazard model to generate Kaplan-Meier survival curves, with all-cause and cancer-specific mortality as the primary endpoints. 20,245 cases met the inclusion criteria. Married men were more likely to be older (38.9 vs. 31.4 years), Caucasian (94.4% vs. 92.1%), stage I (73.1% vs. 61.4%), and have seminoma as the tumor histology (57.3% vs. 43.4%). On multivariate analysis, married status (HR 0.58, P married status (HR 0.60, P married and unmarried men (44.8% vs. 43.4%, P = 0.33). Marital status is an independent predictor of improved overall and cancer-specific survival in men with testis cancer. In men with stages I or II NSGCT, RPLND is an additional predictor of improved overall survival. Marital status does not appear to influence whether men undergo RPLND. Copyright © 2012 Elsevier Inc. All rights reserved.

Progression-free survival as a potential surrogate for overall survival in metastatic breast cancer

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Beauchemin C

2014-06-01

Full Text Available Catherine Beauchemin,1 Dan Cooper,2 Marie-Ève Lapierre,1 Louise Yelle,3 Jean Lachaine11Université de Montréal, Faculté de pharmacie, Montreal, 2Institut national d'excellence en santé et en services sociaux (INESSS, 3Centre Hospitalier de l'Université de Montréal – Hôpital Notre-Dame, Département de médecine, Université de Montréal, Montreal, QC, CanadaBackground: Progression-free survival (PFS and time to progression (TTP are frequently used to establish the clinical efficacy of anti-cancer drugs. However, the surrogacy of PFS/TTP for overall survival (OS remains a matter of uncertainty in metastatic breast cancer (mBC. This study assessed the relationship between PFS/TTP and OS in mBC using a trial-based approach.Methods: We conducted a systematic literature review according to the PICO method: 'Population' consisted of women with mBC; 'Interventions' and 'Comparators' were standard treatments for mBC or best supportive care; 'Outcomes' of interest were median PFS/TTP and OS. We first performed a correlation analysis between median PFS/TTP and OS, and then conducted subgroup analyses to explore possible reasons for heterogeneity. Then, we assessed the relationship between the treatment effect on PFS/TTP and OS. The treatment effect on PFS/TTP and OS was quantified by the absolute difference of median values. We also conducted linear regression analysis to predict the effects of a new anti-cancer drug on OS on the basis of its effects on PFS/TTP.Results: A total of 5,041 studies were identified, and 144 fulfilled the eligibility criteria. There was a statistically significant relationship between median PFS/TTP and OS across included trials (r=0.428; P<0.01. Correlation coefficient for the treatment effect on PFS/TTP and OS was estimated at 0.427 (P<0.01. The obtained linear regression equation was ΔOS =−0.088 (95% confidence interval [CI] −1.347–1.172 + 1.753 (95% CI 1.307–2.198 × ΔPFS (R2=0.86.Conclusion: Results of

Polymorphisms of LIG4, BTBD2, HMGA2, and RTEL1 genes involved in the double-strand break repair pathway predict glioblastoma survival.

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Liu, Yanhong; Shete, Sanjay; Etzel, Carol J; Scheurer, Michael; Alexiou, George; Armstrong, Georgina; Tsavachidis, Spyros; Liang, Fu-Wen; Gilbert, Mark; Aldape, Ken; Armstrong, Terri; Houlston, Richard; Hosking, Fay; Robertson, Lindsay; Xiao, Yuanyuan; Wiencke, John; Wrensch, Margaret; Andersson, Ulrika; Melin, Beatrice S; Bondy, Melissa

2010-05-10

Glioblastoma (GBM) is the most common and aggressive type of glioma and has the poorest survival. However, a small percentage of patients with GBM survive well beyond the established median. Therefore, identifying the genetic variants that influence this small number of unusually long-term survivors may provide important insight into tumor biology and treatment. Among 590 patients with primary GBM, we evaluated associations of survival with the 100 top-ranking glioma susceptibility single nucleotide polymorphisms from our previous genome-wide association study using Cox regression models. We also compared differences in genetic variation between short-term survivors (STS; or= 36 months), and explored classification and regression tree analysis for survival data. We tested results using two independent series totaling 543 GBMs. We identified LIG4 rs7325927 and BTBD2 rs11670188 as predictors of STS in GBM and CCDC26 rs10464870 and rs891835, HMGA2 rs1563834, and RTEL1 rs2297440 as predictors of LTS. Further survival tree analysis revealed that patients >or= 50 years old with LIG4 rs7325927 (V) had the worst survival (median survival time, 1.2 years) and exhibited the highest risk of death (hazard ratio, 17.53; 95% CI, 4.27 to 71.97) compared with younger patients with combined RTEL1 rs2297440 (V) and HMGA2 rs1563834 (V) genotypes (median survival time, 7.8 years). Polymorphisms in the LIG4, BTBD2, HMGA2, and RTEL1 genes, which are involved in the double-strand break repair pathway, are associated with GBM survival.

Distance Based Root Cause Analysis and Change Impact Analysis of Performance Regressions

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Junzan Zhou

2015-01-01

Full Text Available Performance regression testing is applied to uncover both performance and functional problems of software releases. A performance problem revealed by performance testing can be high response time, low throughput, or even being out of service. Mature performance testing process helps systematically detect software performance problems. However, it is difficult to identify the root cause and evaluate the potential change impact. In this paper, we present an approach leveraging server side logs for identifying root causes of performance problems. Firstly, server side logs are used to recover call tree of each business transaction. We define a novel distance based metric computed from call trees for root cause analysis and apply inverted index from methods to business transactions for change impact analysis. Empirical studies show that our approach can effectively and efficiently help developers diagnose root cause of performance problems.

Volumetric and MGMT parameters in glioblastoma patients: Survival analysis

International Nuclear Information System (INIS)

Iliadis, Georgios; Kotoula, Vassiliki; Chatzisotiriou, Athanasios; Televantou, Despina; Eleftheraki, Anastasia G; Lambaki, Sofia; Misailidou, Despina; Selviaridis, Panagiotis; Fountzilas, George

2012-01-01

In this study several tumor-related volumes were assessed by means of a computer-based application and a survival analysis was conducted to evaluate the prognostic significance of pre- and postoperative volumetric data in patients harboring glioblastomas. In addition, MGMT (O 6 -methylguanine methyltransferase) related parameters were compared with those of volumetry in order to observe possible relevance of this molecule in tumor development. We prospectively analyzed 65 patients suffering from glioblastoma (GBM) who underwent radiotherapy with concomitant adjuvant temozolomide. For the purpose of volumetry T1 and T2-weighted magnetic resonance (MR) sequences were used, acquired both pre- and postoperatively (pre-radiochemotherapy). The volumes measured on preoperative MR images were necrosis, enhancing tumor and edema (including the tumor) and on postoperative ones, net-enhancing tumor. Age, sex, performance status (PS) and type of operation were also included in the multivariate analysis. MGMT was assessed for promoter methylation with Multiplex Ligation-dependent Probe Amplification (MLPA), for RNA expression with real time PCR, and for protein expression with immunohistochemistry in a total of 44 cases with available histologic material. In the multivariate analysis a negative impact was shown for pre-radiochemotherapy net-enhancing tumor on the overall survival (OS) (p = 0.023) and for preoperative necrosis on progression-free survival (PFS) (p = 0.030). Furthermore, the multivariate analysis confirmed the importance of PS in PFS and OS of patients. MGMT promoter methylation was observed in 13/23 (43.5%) evaluable tumors; complete methylation was observed in 3/13 methylated tumors only. High rate of MGMT protein positivity (> 20% positive neoplastic nuclei) was inversely associated with pre-operative tumor necrosis (p = 0.021). Our findings implicate that volumetric parameters may have a significant role in the prognosis of GBM patients. Furthermore

Sparse multivariate factor analysis regression models and its applications to integrative genomics analysis.

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Zhou, Yan; Wang, Pei; Wang, Xianlong; Zhu, Ji; Song, Peter X-K

2017-01-01

The multivariate regression model is a useful tool to explore complex associations between two kinds of molecular markers, which enables the understanding of the biological pathways underlying disease etiology. For a set of correlated response variables, accounting for such dependency can increase statistical power. Motivated by integrative genomic data analyses, we propose a new methodology-sparse multivariate factor analysis regression model (smFARM), in which correlations of response variables are assumed to follow a factor analysis model with latent factors. This proposed method not only allows us to address the challenge that the number of association parameters is larger than the sample size, but also to adjust for unobserved genetic and/or nongenetic factors that potentially conceal the underlying response-predictor associations. The proposed smFARM is implemented by the EM algorithm and the blockwise coordinate descent algorithm. The proposed methodology is evaluated and compared to the existing methods through extensive simulation studies. Our results show that accounting for latent factors through the proposed smFARM can improve sensitivity of signal detection and accuracy of sparse association map estimation. We illustrate smFARM by two integrative genomics analysis examples, a breast cancer dataset, and an ovarian cancer dataset, to assess the relationship between DNA copy numbers and gene expression arrays to understand genetic regulatory patterns relevant to the disease. We identify two trans-hub regions: one in cytoband 17q12 whose amplification influences the RNA expression levels of important breast cancer genes, and the other in cytoband 9q21.32-33, which is associated with chemoresistance in ovarian cancer. © 2016 WILEY PERIODICALS, INC.

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

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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.

OASIS 2: online application for survival analysis 2 with features for the analysis of maximal lifespan and healthspan in aging research.

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Han, Seong Kyu; Lee, Dongyeop; Lee, Heetak; Kim, Donghyo; Son, Heehwa G; Yang, Jae-Seong; Lee, Seung-Jae V; Kim, Sanguk

2016-08-30

Online application for survival analysis (OASIS) has served as a popular and convenient platform for the statistical analysis of various survival data, particularly in the field of aging research. With the recent advances in the fields of aging research that deal with complex survival data, we noticed a need for updates to the current version of OASIS. Here, we report OASIS 2 (http://sbi.postech.ac.kr/oasis2), which provides extended statistical tools for survival data and an enhanced user interface. In particular, OASIS 2 enables the statistical comparison of maximal lifespans, which is potentially useful for determining key factors that limit the lifespan of a population. Furthermore, OASIS 2 provides statistical and graphical tools that compare values in different conditions and times. That feature is useful for comparing age-associated changes in physiological activities, which can be used as indicators of "healthspan." We believe that OASIS 2 will serve as a standard platform for survival analysis with advanced and user-friendly statistical tools for experimental biologists in the field of aging research.

[Medulloblastoma: improved survival in recent decades. Unicentric experience].

Science.gov (United States)

Igual Estellés, Lucía; Berlanga Charriel, Pablo; Cañete Nieto, Adela

2017-01-01

The aim of the study is to analyse variations in the treatment of medulloblastoma, the most common childhood brain tumour, and its impact on survival over the past two decades, as well as its clinical and pathological features. Survival analysis of all patients under 14 years old diagnosed with medulloblastoma between January 1990 and December 2013 in a Paediatric Oncology Unit. Sixty-three patients were diagnosed and treated for medulloblastoma, with a median follow-up of 5.1 years (range 0.65-21.7 years). The overall survival (OS) at 3 and 5 years was 66±13% and 55±14%, respectively. The OS at 5 years was 44%±25% in patients diagnosed in the 1990's, showing an increase to 70%±23% (p=0.032) since 2000. Clinical prognosis factors were included in the logistic regression model: age (p=0.008), presence of metastases and/or residual tumour (p=0.007), and receiving chemotherapy with radiotherapy after surgery (p=0.008). Statistically significant differences were observed for all of them. In our institution there has been a significant increase in medulloblastoma survival in the last decades. Multivariate analysis showed that this improvement was not related to the date of diagnosis, but with the introduction of chemotherapy in adjuvant treatment. This study confirmed that clinical factors significantly associated with worse outcome were age and presence of metastases at diagnosis. Copyright © 2016 Asociación Española de Pediatría. Publicado por Elsevier España, S.L.U. All rights reserved.

Parametric and semiparametric models with applications to reliability, survival analysis, and quality of life

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Nikulin, M; Mesbah, M; Limnios, N

2004-01-01

Parametric and semiparametric models are tools with a wide range of applications to reliability, survival analysis, and quality of life. This self-contained volume examines these tools in survey articles written by experts currently working on the development and evaluation of models and methods. While a number of chapters deal with general theory, several explore more specific connections and recent results in "real-world" reliability theory, survival analysis, and related fields.

No rationale for 1 variable per 10 events criterion for binary logistic regression analysis

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Maarten van Smeden

2016-11-01

Full Text Available Abstract Background Ten events per variable (EPV is a widely advocated minimal criterion for sample size considerations in logistic regression analysis. Of three previous simulation studies that examined this minimal EPV criterion only one supports the use of a minimum of 10 EPV. In this paper, we examine the reasons for substantial differences between these extensive simulation studies. Methods The current study uses Monte Carlo simulations to evaluate small sample bias, coverage of confidence intervals and mean square error of logit coefficients. Logistic regression models fitted by maximum likelihood and a modified estimation procedure, known as Firth’s correction, are compared. Results The results show that besides EPV, the problems associated with low EPV depend on other factors such as the total sample size. It is also demonstrated that simulation results can be dominated by even a few simulated data sets for which the prediction of the outcome by the covariates is perfect (‘separation’. We reveal that different approaches for identifying and handling separation leads to substantially different simulation results. We further show that Firth’s correction can be used to improve the accuracy of regression coefficients and alleviate the problems associated with separation. Conclusions The current evidence supporting EPV rules for binary logistic regression is weak. Given our findings, there is an urgent need for new research to provide guidance for supporting sample size considerations for binary logistic regression analysis.

Drawing Nomograms with R: applications to categorical outcome and survival data.

Science.gov (United States)

Zhang, Zhongheng; Kattan, Michael W

2017-05-01

Outcome prediction is a major task in clinical medicine. The standard approach to this work is to collect a variety of predictors and build a model of appropriate type. The model is a mathematical equation that connects the outcome of interest with the predictors. A new patient with given clinical characteristics can be predicted for outcome with this model. However, the equation describing the relationship between predictors and outcome is often complex and the computation requires software for practical use. There is another method called nomogram which is a graphical calculating device allowing an approximate graphical computation of a mathematical function. In this article, we describe how to draw nomograms for various outcomes with nomogram() function. Binary outcome is fit by logistic regression model and the outcome of interest is the probability of the event of interest. Ordinal outcome variable is also discussed. Survival analysis can be fit with parametric model to fully describe the distributions of survival time. Statistics such as the median survival time, survival probability up to a specific time point are taken as the outcome of interest.

Sparse Regression by Projection and Sparse Discriminant Analysis

KAUST Repository

Qi, Xin; Luo, Ruiyan; Carroll, Raymond J.; Zhao, Hongyu

2015-01-01

predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high-dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths

Pretreatment oral hygiene habits and survival of head and neck squamous cell carcinoma (HNSCC) patients.

Science.gov (United States)

Friemel, Juliane; Foraita, Ronja; Günther, Kathrin; Heibeck, Mathias; Günther, Frauke; Pflueger, Maren; Pohlabeln, Hermann; Behrens, Thomas; Bullerdiek, Jörn; Nimzyk, Rolf; Ahrens, Wolfgang

2016-03-11

The survival time of patients with head and neck squamous cell carcinoma (HNSCC) is related to health behavior, such as tobacco smoking and alcohol consumption. Poor oral health (OH), dental care (DC) and the frequent use of mouthwash have been shown to represent independent risk factors for head and neck cancerogenesis, but their impact on the survival of HNSCC patients has not been systematically investigated. Two hundred seventy-six incident HNSCC cases recruited for the ARCAGE study were followed through a period of 6-10 years. Interview-based information on wearing of dentures, gum bleeding, teeth brushing, use of floss and dentist visits were grouped into weighted composite scores, i.e. oral health (OH) and dental care (DH). Use of mouthwash was assessed as frequency per day. Also obtained were other types of health behavior, such as smoking, alcohol drinking and diet, appreciated as both confounding and study variables. Endpoints were progression-free survival, overall survival and tumor-specific survival. Prognostic values were estimated using Kaplan-Meier analysis and Cox proportional hazards regression models. A good dental care score, summarizing annual dental visits, daily teeth cleaning and use of floss was associated with longer overall survival time (p = .001). The results of the Cox regression models similarly suggested a higher risk of tumor progression and shortened overall survival in patients with poor dental care, but the results lost their statistical significance after other types of health behavior had been controlled for. Frequent use of mouthwash (≥ 2 times/day) significantly increased the risk of tumor-specific death (HR = 2.26; CI = 1.19-4.32). Alcohol consumption and tobacco smoking were dose-dependently associated with tumor progression and shorter overall survival. Frequent mouthwash use of ≥ 2 times/day seems to elevate the risk of tumor-specific death in HNSCC patients. Good dental care scores are associated with longer overall

Surgery for left ventricular aneurysm: early and late survival after simple linear repair and endoventricular patch plasty.

Science.gov (United States)

Lundblad, Runar; Abdelnoor, Michel; Svennevig, Jan Ludvig

2004-09-01

Simple linear resection and endoventricular patch plasty are alternative techniques to repair postinfarction left ventricular aneurysm. The aim of the study was to compare these 2 methods with regard to early mortality and long-term survival. We retrospectively reviewed 159 patients undergoing operations between 1989 and 2003. The epidemiologic design was of an exposed (simple linear repair, n = 74) versus nonexposed (endoventricular patch plasty, n = 85) cohort with 2 endpoints: early mortality and long-term survival. The crude effect of aneurysm repair technique versus endpoint was estimated by odds ratio, rate ratio, or relative risk and their 95% confidence intervals. Stratification analysis by using the Mantel-Haenszel method was done to quantify confounders and pinpoint effect modifiers. Adjustment for multiconfounders was performed by using logistic regression and Cox regression analysis. Survival curves were analyzed with the Breslow test and the log-rank test. Early mortality was 8.2% for all patients, 13.5% after linear repair and 3.5% after endoventricular patch plasty. When adjusted for multiconfounders, the risk of early mortality was significantly higher after simple linear repair than after endoventricular patch plasty (odds ratio, 4.4; 95% confidence interval, 1.1-17.8). Mean follow-up was 5.8 +/- 3.8 years (range, 0-14.0 years). Overall 5-year cumulative survival was 78%, 70.1% after linear repair and 91.4% after endoventricular patch plasty. The risk of total mortality was significantly higher after linear repair than after endoventricular patch plasty when controlled for multiconfounders (relative risk, 4.5; 95% confidence interval, 2.0-9.7). Linear repair dominated early in the series and patch plasty dominated later, giving a possible learning-curve bias in favor of patch plasty that could not be adjusted for in the regression analysis. Postinfarction left ventricular aneurysm can be repaired with satisfactory early and late results. Surgical

Statistical methods and regression analysis of stratospheric ozone and meteorological variables in Isfahan

Science.gov (United States)

Hassanzadeh, S.; Hosseinibalam, F.; Omidvari, M.

2008-04-01

Data of seven meteorological variables (relative humidity, wet temperature, dry temperature, maximum temperature, minimum temperature, ground temperature and sun radiation time) and ozone values have been used for statistical analysis. Meteorological variables and ozone values were analyzed using both multiple linear regression and principal component methods. Data for the period 1999-2004 are analyzed jointly using both methods. For all periods, temperature dependent variables were highly correlated, but were all negatively correlated with relative humidity. Multiple regression analysis was used to fit the meteorological variables using the meteorological variables as predictors. A variable selection method based on high loading of varimax rotated principal components was used to obtain subsets of the predictor variables to be included in the linear regression model of the meteorological variables. In 1999, 2001 and 2002 one of the meteorological variables was weakly influenced predominantly by the ozone concentrations. However, the model did not predict that the meteorological variables for the year 2000 were not influenced predominantly by the ozone concentrations that point to variation in sun radiation. This could be due to other factors that were not explicitly considered in this study.

Geographical variations in the use of cancer treatments are associated with survival of lung cancer patients

DEFF Research Database (Denmark)

Møller, Henrik; Coupland, Victoria H; Tataru, Daniela

2018-01-01

INTRODUCTION: Lung cancer outcomes in England are inferior to comparable countries. Patient or disease characteristics, healthcare-seeking behaviour, diagnostic pathways, and oncology service provision may contribute. We aimed to quantify associations between geographic variations in treatment...... and survival of patients in England. METHODS: We retrieved detailed cancer registration data to analyse the variation in survival of 176,225 lung cancer patients, diagnosed 2010-2014. We used Kaplan-Meier analysis and Cox proportional hazards regression to investigate survival in the two-year period following...... diagnosis. RESULTS: Survival improved over the period studied. The use of active treatment varied between geographical areas, with inter-quintile ranges of 9%-17% for surgical resection, 4%-13% for radical radiotherapy, and 22%-35% for chemotherapy. At 2 years, there were 188 potentially avoidable deaths...

Multivariate regression analysis for determining short-term values of radon and its decay products from filter measurements

International Nuclear Information System (INIS)

Kraut, W.; Schwarz, W.; Wilhelm, A.

1994-01-01

A multivariate regression analysis is applied to decay measurements of α-resp. β-filter activcity. Activity concentrations for Po-218, Pb-214 and Bi-214, resp. for the Rn-222 equilibrium equivalent concentration are obtained explicitly. The regression analysis takes into account properly the variances of the measured count rates and their influence on the resulting activity concentrations. (orig.) [de

Mechanisms and mediation in survival analysis: towards an integrated analytical framework

Directory of Open Access Journals (Sweden)

Jonathan Pratschke

2016-02-01

Full Text Available Abstract Background A wide-ranging debate has taken place in recent years on mediation analysis and causal modelling, raising profound theoretical, philosophical and methodological questions. The authors build on the results of these discussions to work towards an integrated approach to the analysis of research questions that situate survival outcomes in relation to complex causal pathways with multiple mediators. The background to this contribution is the increasingly urgent need for policy-relevant research on the nature of inequalities in health and healthcare. Methods The authors begin by summarising debates on causal inference, mediated effects and statistical models, showing that these three strands of research have powerful synergies. They review a range of approaches which seek to extend existing survival models to obtain valid estimates of mediation effects. They then argue for an alternative strategy, which involves integrating survival outcomes within Structural Equation Models via the discrete-time survival model. This approach can provide an integrated framework for studying mediation effects in relation to survival outcomes, an issue of great relevance in applied health research. The authors provide an example of how these techniques can be used to explore whether the social class position of patients has a significant indirect effect on the hazard of death from colon cancer. Results The results suggest that the indirect effects of social class on survival are substantial and negative (-0.23 overall. In addition to the substantial direct effect of this variable (-0.60, its indirect effects account for more than one quarter of the total effect. The two main pathways for this indirect effect, via emergency admission (-0.12, on the one hand, and hospital caseload, on the other, (-0.10 are of similar size. Conclusions The discrete-time survival model provides an attractive way of integrating time-to-event data within the field of

Mechanisms and mediation in survival analysis: towards an integrated analytical framework.

Science.gov (United States)

Pratschke, Jonathan; Haase, Trutz; Comber, Harry; Sharp, Linda; de Camargo Cancela, Marianna; Johnson, Howard

2016-02-29

A wide-ranging debate has taken place in recent years on mediation analysis and causal modelling, raising profound theoretical, philosophical and methodological questions. The authors build on the results of these discussions to work towards an integrated approach to the analysis of research questions that situate survival outcomes in relation to complex causal pathways with multiple mediators. The background to this contribution is the increasingly urgent need for policy-relevant research on the nature of inequalities in health and healthcare. The authors begin by summarising debates on causal inference, mediated effects and statistical models, showing that these three strands of research have powerful synergies. They review a range of approaches which seek to extend existing survival models to obtain valid estimates of mediation effects. They then argue for an alternative strategy, which involves integrating survival outcomes within Structural Equation Models via the discrete-time survival model. This approach can provide an integrated framework for studying mediation effects in relation to survival outcomes, an issue of great relevance in applied health research. The authors provide an example of how these techniques can be used to explore whether the social class position of patients has a significant indirect effect on the hazard of death from colon cancer. The results suggest that the indirect effects of social class on survival are substantial and negative (-0.23 overall). In addition to the substantial direct effect of this variable (-0.60), its indirect effects account for more than one quarter of the total effect. The two main pathways for this indirect effect, via emergency admission (-0.12), on the one hand, and hospital caseload, on the other, (-0.10) are of similar size. The discrete-time survival model provides an attractive way of integrating time-to-event data within the field of Structural Equation Modelling. The authors demonstrate the efficacy

An Econometric Analysis of Modulated Realised Covariance, Regression and Correlation in Noisy Diffusion Models

DEFF Research Database (Denmark)

Kinnebrock, Silja; Podolskij, Mark

This paper introduces a new estimator to measure the ex-post covariation between high-frequency financial time series under market microstructure noise. We provide an asymptotic limit theory (including feasible central limit theorems) for standard methods such as regression, correlation analysis...... process can be relaxed and how our method can be applied to non-synchronous observations. We also present an empirical study of how high-frequency correlations, regressions and covariances change through time....

A menu-driven software package of Bayesian nonparametric (and parametric) mixed models for regression analysis and density estimation.

Science.gov (United States)

Karabatsos, George

2017-02-01

Most of applied statistics involves regression analysis of data. In practice, it is important to specify a regression model that has minimal assumptions which are not violated by data, to ensure that statistical inferences from the model are informative and not misleading. This paper presents a stand-alone and menu-driven software package, Bayesian Regression: Nonparametric and Parametric Models, constructed from MATLAB Compiler. Currently, this package gives the user a choice from 83 Bayesian models for data analysis. They include 47 Bayesian nonparametric (BNP) infinite-mixture regression models; 5 BNP infinite-mixture models for density estimation; and 31 normal random effects models (HLMs), including normal linear models. Each of the 78 regression models handles either a continuous, binary, or ordinal dependent variable, and can handle multi-level (grouped) data. All 83 Bayesian models can handle the analysis of weighted observations (e.g., for meta-analysis), and the analysis of left-censored, right-censored, and/or interval-censored data. Each BNP infinite-mixture model has a mixture distribution assigned one of various BNP prior distributions, including priors defined by either the Dirichlet process, Pitman-Yor process (including the normalized stable process), beta (two-parameter) process, normalized inverse-Gaussian process, geometric weights prior, dependent Dirichlet process, or the dependent infinite-probits prior. The software user can mouse-click to select a Bayesian model and perform data analysis via Markov chain Monte Carlo (MCMC) sampling. After the sampling completes, the software automatically opens text output that reports MCMC-based estimates of the model's posterior distribution and model predictive fit to the data. Additional text and/or graphical output can be generated by mouse-clicking other menu options. This includes output of MCMC convergence analyses, and estimates of the model's posterior predictive distribution, for selected

Analysis of prognostic factors for survival in patients with primary spinal chordoma using the SEER Registry from 1973 to 2014.

Science.gov (United States)

Pan, Yue; Lu, Lingyun; Chen, Junquan; Zhong, Yong; Dai, Zhehao

2018-04-06

Spinal chordomas are rare primary osseous tumors that arise from the remnants of the notochord. They are commonly considered slow-growing, locally invasive neoplasms with little tendency to metastasize, but the high recurrent rate of spinal chordomas may seriously affect the survival rate and quality of life of patients. The aim of the study is to describe the epidemiological data and determine the prognostic factors for decreased survival in patients with primary spinal chordoma. The Surveillance, Epidemiology, and End Results (SEER) Registry database, a US population-based cancer registry database, was used to identify all patients diagnosed with primary spinal chordoma from 1973 to 2014. We utilized Kaplan-Meier method and Cox proportional hazards regression analysis to evaluate the association between patients overall survival and relevant characteristics, including age, gender, race, disease stage, treatment methods, primary tumor site, marital status, and urban county background. In the data set between 1973 and 2014, a total of 808 patients were identified with primary spinal chordoma. The overall rate of distant metastatic cases in our cohort was only 7.7%. Spinal chordoma was more common occurred in men (62.6%) than women (37.3%). Majority of neoplasms were found in the White (87.9%), while the incidence of the Black is relatively infrequent (3.3%). Three hundred fifty-seven spinal chordomas (44.2%) were located in the vertebral column, while 451 patients' tumor (55.8%) was located in the sacrum or pelvis. Age ≥ 60 years (HR = 2.72; 95%CI, 1.71 to 2.89), distant metastasis (HR = 2.16; 95%CI, 1.54 to 3.02), and non-surgical therapy (HR = 2.14; 95%CI, 1.72 to 2.69) were independent risk factors for survival reduction in analysis. Survival did not significantly differ as a factor of tumor site (vertebrae vs sacrum/pelvis) for primary spinal chordoma (HR = 0.93, P = 0.16). Race (P = 0.52), gender (P = 0.11), marital status (P = 0.94), and

Regression analysis of mixed recurrent-event and panel-count data.

Science.gov (United States)

Zhu, Liang; Tong, Xinwei; Sun, Jianguo; Chen, Manhua; Srivastava, Deo Kumar; Leisenring, Wendy; Robison, Leslie L

2014-07-01

In event history studies concerning recurrent events, two types of data have been extensively discussed. One is recurrent-event data (Cook and Lawless, 2007. The Analysis of Recurrent Event Data. New York: Springer), and the other is panel-count data (Zhao and others, 2010. Nonparametric inference based on panel-count data. Test 20: , 1-42). In the former case, all study subjects are monitored continuously; thus, complete information is available for the underlying recurrent-event processes of interest. In the latter case, study subjects are monitored periodically; thus, only incomplete information is available for the processes of interest. In reality, however, a third type of data could occur in which some study subjects are monitored continuously, but others are monitored periodically. When this occurs, we have mixed recurrent-event and panel-count data. This paper discusses regression analysis of such mixed data and presents two estimation procedures for the problem. One is a maximum likelihood estimation procedure, and the other is an estimating equation procedure. The asymptotic properties of both resulting estimators of regression parameters are established. Also, the methods are applied to a set of mixed recurrent-event and panel-count data that arose from a Childhood Cancer Survivor Study and motivated this investigation. © The Author 2014. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

[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.

Percutaneous radiofrequency ablation for early hepatocellular carcinoma: Risk factors for survival

Science.gov (United States)

Kikuchi, Luciana; Menezes, Marcos; Chagas, Aline L; Tani, Claudia M; Alencar, Regiane SSM; Diniz, Marcio A; Alves, Venâncio AF; D’Albuquerque, Luiz Augusto Carneiro; Carrilho, Flair José

2014-01-01

(led to death). The 1- and 2-year overall survival rates were 82% and 71%, respectively. Sex, tumor size, initial response, and recurrence status influenced survival, but did not reach the threshold of statistical significance. Child-Pugh class and the model for end-stage liver disease score were identified as predictors of survival by simple Cox regression, but only Child-Pugh class showed a statistically significant association to survival in multiple Cox regression analysis (HR = 15; 95%CI: 3-76 mo; P = 0.001). The 1- and 2-year cumulative disease-free survival rates were 65% and 36%, respectively. CONCLUSION: RFA is an effective therapy for local tumor control of early HCC, and patients with preserved liver function are the best candidates. PMID:24587635

Penalized estimation for competing risks regression with applications to high-dimensional covariates

DEFF Research Database (Denmark)

Ambrogi, Federico; Scheike, Thomas H.

2016-01-01

of competing events. The direct binomial regression model of Scheike and others (2008. Predicting cumulative incidence probability by direct binomial regression. Biometrika 95: (1), 205-220) is reformulated in a penalized framework to possibly fit a sparse regression model. The developed approach is easily...... Research 19: (1), 29-51), the research regarding competing risks is less developed (Binder and others, 2009. Boosting for high-dimensional time-to-event data with competing risks. Bioinformatics 25: (7), 890-896). The aim of this work is to consider how to do penalized regression in the presence...... implementable using existing high-performance software to do penalized regression. Results from simulation studies are presented together with an application to genomic data when the endpoint is progression-free survival. An R function is provided to perform regularized competing risks regression according...

Forecasting Model for IPTV Service in Korea Using Bootstrap Ridge Regression Analysis

Science.gov (United States)

Lee, Byoung Chul; Kee, Seho; Kim, Jae Bum; Kim, Yun Bae

The telecom firms in Korea are taking new step to prepare for the next generation of convergence services, IPTV. In this paper we described our analysis on the effective method for demand forecasting about IPTV broadcasting. We have tried according to 3 types of scenarios based on some aspects of IPTV potential market and made a comparison among the results. The forecasting method used in this paper is the multi generation substitution model with bootstrap ridge regression analysis.

Marital status integration and suicide: A meta-analysis and meta-regression.

Science.gov (United States)

Kyung-Sook, Woo; SangSoo, Shin; Sangjin, Shin; Young-Jeon, Shin

2018-01-01

Marital status is an index of the phenomenon of social integration within social structures and has long been identified as an important predictor suicide. However, previous meta-analyses have focused only on a particular marital status, or not sufficiently explored moderators. A meta-analysis of observational studies was conducted to explore the relationships between marital status and suicide and to understand the important moderating factors in this association. Electronic databases were searched to identify studies conducted between January 1, 2000 and June 30, 2016. We performed a meta-analysis, subgroup analysis, and meta-regression of 170 suicide risk estimates from 36 publications. Using random effects model with adjustment for covariates, the study found that the suicide risk for non-married versus married was OR = 1.92 (95% CI: 1.75-2.12). The suicide risk was higher for non-married individuals aged analysis by gender, non-married men exhibited a greater risk of suicide than their married counterparts in all sub-analyses, but women aged 65 years or older showed no significant association between marital status and suicide. The suicide risk in divorced individuals was higher than for non-married individuals in both men and women. The meta-regression showed that gender, age, and sample size affected between-study variation. The results of the study indicated that non-married individuals have an aggregate higher suicide risk than married ones. In addition, gender and age were confirmed as important moderating factors in the relationship between marital status and suicide. Copyright © 2017 Elsevier Ltd. All rights reserved.

Dedifferentiated chondrosarcoma: A survival analysis of 159 cases from the SEER database (2001-2011).

Science.gov (United States)

Strotman, Patrick K; Reif, Taylor J; Kliethermes, Stephanie A; Sandhu, Jasmin K; Nystrom, Lukas M

2017-08-01

Dedifferentiated chondrosarcoma is a rare malignancy with reported 5-year overall survival rates ranging from 7% to 24%. The purpose of this investigation is to determine the overall survival of dedifferentiated chondrosarcoma in a modern patient series and how it is impacted by patient demographics, tumor characteristics, and surgical treatment factors. This is a retrospective review of the Surveillance, Epidemiology, and End Results (SEER) database from 2001 to 2011. Kaplan Meier analyses were used for overall and disease-specific survival. Univariable and multivariable cox regression models were used to identify prognostic factors. Five year overall- and disease-specific survival was 18% (95% CI: 12-26%) and 28% (95% CI: 18-37%), respectively. Individuals with extremity tumors had a worse prognosis than individuals with a primary tumor in the chest wall or axial skeleton (HR 0.20, 95% CI: 0.07-0.56; P = 0.002 and HR 0.60, 95% CI: 0.36-0.99; P = 0.04, respectively). Patients with AJCC stage III or IV disease (HR 2.51, 95% CI: 1.50-4.20; P = 0.001), tumors larger than 8 cm (HR 2.17, 95% CI: 1.11-4.27; P = 0.046), metastatic disease at diagnosis (HR 3.25, 95% CI: 1.98-5.33; P chondrosarcoma is poor with a 5-year overall survival of 18%. Patients with a primary tumor located in the chest wall had a better prognosis. Tumors larger than 8 cm, presence of metastases at diagnosis, and treatment without surgical resection were significant predictors of mortality. © 2017 Wiley Periodicals, Inc.

The time dependent association of adrenaline administration and survival from out-of-hospital cardiac arrest.

Science.gov (United States)

Ewy, Gordon A; Bobrow, Bentley J; Chikani, Vatsal; Sanders, Arthur B; Otto, Charles W; Spaite, Daniel W; Kern, Karl B

2015-11-01

Recommended for decades, the therapeutic value of adrenaline (epinephrine) in the resuscitation of patients with out-of-hospital cardiac arrest (OHCA) is controversial. To investigate the possible time-dependent outcomes associated with adrenaline administration by Emergency Medical Services personnel (EMS). A retrospective analysis of prospectively collected data from a near statewide cardiac resuscitation database between 1 January 2005 and 30 November 2013. Multivariable logistic regression was used to analyze the effect of the time interval between EMS dispatch and the initial dose of adrenaline on survival. The primary endpoints were survival to hospital discharge and favourable neurologic outcome. Data from 3469 patients with witnessed OHCA were analyzed. Their mean age was 66.3 years and 69% were male. An initially shockable rhythm was present in 41.8% of patients. Based on a multivariable logistic regression model with initial adrenaline administration time interval (AATI) from EMS dispatch as the covariate, survival was greatest when adrenaline was administered very early but decreased rapidly with increasing (AATI); odds ratio 0.94 (95% Confidence Interval (CI) 0.92-0.97). The AATI had no significant effect on good neurological outcome (OR=0.96, 95% CI=0.90-1.02). In patients with OHCA, survival to hospital discharge was greater in those treated early with adrenaline by EMS especially in the subset of patients with a shockable rhythm. However survival rapidly decreased with increasing adrenaline administration time intervals (AATI). Copyright © 2015 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Driven Factors Analysis of China’s Irrigation Water Use Efficiency by Stepwise Regression and Principal Component Analysis

Directory of Open Access Journals (Sweden)

Renfu Jia

2016-01-01

Full Text Available This paper introduces an integrated approach to find out the major factors influencing efficiency of irrigation water use in China. It combines multiple stepwise regression (MSR and principal component analysis (PCA to obtain more realistic results. In real world case studies, classical linear regression model often involves too many explanatory variables and the linear correlation issue among variables cannot be eliminated. Linearly correlated variables will cause the invalidity of the factor analysis results. To overcome this issue and reduce the number of the variables, PCA technique has been used combining with MSR. As such, the irrigation water use status in China was analyzed to find out the five major factors that have significant impacts on irrigation water use efficiency. To illustrate the performance of the proposed approach, the calculation based on real data was conducted and the results were shown in this paper.

A regression analysis of the effect of energy use in agriculture

International Nuclear Information System (INIS)

Karkacier, Osman; Gokalp Goktolga, Z.; Cicek, Adnan

2006-01-01

This study investigates the impacts of energy use on productivity of Turkey's agriculture. It reports the results of a regression analysis of the relationship between energy use and agricultural productivity. The study is based on the analysis of the yearbook data for the period 1971-2003. Agricultural productivity was specified as a function of its energy consumption (TOE) and gross additions of fixed assets during the year. Least square (LS) was employed to estimate equation parameters. The data of this study comes from the State Institute of Statistics (SIS) and The Ministry of Energy of Turkey

The relationship between survival of Columbia River fall chinook salmon and in-river environmental factors -- Analysis of historic data for juvenile and adult salmonid production: Phase 2. Final report

International Nuclear Information System (INIS)

Skalski, J.R.; Townsend, R.L.; Donnelly, R.F.; Hilborn, R.W.

1996-12-01

This project analyzes in greater detail the coded-wire-tag (CWT) returns of Priest Rapids Hatchery fall chinook for the years 1976--1989 initially begun by Hilborn et al. (1993a). These additional analyses were prompted by suggestions made by peer reviews of the initial draft report. The initial draft and the peer review comments are included in this final report (Appendices A and B). The statistical analyses paired Priest Rapids stock with potential downriver reference stocks to isolate in-river survival rates. Thirty-three potential reference stocks were initially examined for similar ocean recovery rates; the five stocks with the most similar recovery patterns (i.e., Bonneville Brights, Cowlitz, Gray's River, Tanner Creek, and Washougal) to the Priest Rapids stock were used in the subsequent analysis of in-river survival. Three alternate forms of multiple regression models were used to investigate the relationship between predicted in-river survival and ambient conditions. Analyses were conducted with and without attempts to adjust for smolt transportation at McNary Dam. Independent variables examined in the analysis included river flows, temperature, turbidity, and spill along with the total biomass of hatchery releases in the Columbia-Snake River Basin

Factors affecting survival of women diagnosed with breast cancer in El-Minia Governorate, Egypt.

Science.gov (United States)

Seedhom, Amany Edward; Kamal, Nashwa Nabil

2011-07-01

This study was conducted to determine breast cancer survival time and the association between breast cancer survival and socio-demographic and pathologic factors among women, in El-Minia, Egypt. While there has been much researches regarding prognostic factors for breast cancer but the majority of these studies were from developed countries. El-Minia has a population of approximately 4 million. To date, no research has been performed to determine breast cancer survival and the factors affecting it in El-minia. This retrospective study used data obtained from the cancer registry in the National Institute of Oncology in El-Minia and included 1207 women diagnosed with first primary breast cancer between 1(st) January 2005 and 31(st) December 2009 and followed to 30(th) June 2010. The association between survival and sociodemographic and pathological factors and distant metastasis at diagnosis, and treatment options was investigated using unifactorial chi-square test and multi-factorial (Cox regression) analyses. Kaplan-Meier analysis was used to compare survival time among different groups. Median survival time was 83.8 ± 3.2. Cox regression showed that high vs low educational level (Hazard ratio (HR)= 0.35, 95% CI; 0.27-0.46), metastases to bone (HR = 3.22, 95% CI: 1.71-6.05), metastases to lung (HR= 2.314, 95% CI: 1.225-4.373), tumor size (≤ 2 cm vs ≥ 5 cm: HR = 1.4, 95% CI: 1.1-1.8) and number of involved nodes (1 vs > 10 HR = 5.21, 95%CI: 3.1-9.01) were significantly related to survival. The results showed the need to develop screening programs and standardized treatment regimens in a tax-funded health care system.

Estimation of Unemployment Duration in Botoşani County Using Survival Analysis

Directory of Open Access Journals (Sweden)

Darabă Gabriel

2017-01-01

Full Text Available In this paper we aim at estimating the unemployment duration in Botosani County in order tostudy the impact of individual characteristics (gender, age, place of residence, unemploymentbenefit, etc. on the length of unemployment spells. We use Cox regression model to measure theeffects of gender, age, residential environment, etc. on the hazard rate of leaving unemploymentandKaplan-Meier estimator to compare survival probabilities among different categories ofunemployed persons. The study is carried out on a sample of 200 unemployment spellsregisteredwith the Employment Agency of Botoşani County from January 2012 to December 2015. Theresults reveal that place of residence, unemployment benefit and unemployed category have asignificant impact on unemployment spells.

Pre-therapeutic factors for predicting survival after radioembolization: a single-center experience in 389 patients

International Nuclear Information System (INIS)

Paprottka, K.J.; Schoeppe, F.; Ingrisch, M.; Ruebenthaler, J.; Sommer, N.N.; Paprottka, P.M.; Toni, E. de; Ilhan, H.; Zacherl, M.; Todica, A.

2017-01-01

To determine pre-therapeutic predictive factors for overall survival (OS) after yttrium (Y)-90 radioembolization (RE). We retrospectively analyzed the pre-therapeutic characteristics (sex, age, tumor entity, hepatic tumor burden, extrahepatic disease [EHD] and liver function [with focus on bilirubin and cholinesterase level]) of 389 consecutive patients with various refractory liver-dominant tumors (hepatocellular carcinoma [HCC], cholangiocarcinoma [CCC], neuroendocrine tumor [NET], colorectal cancer [CRC] and metastatic breast cancer [MBC]), who received Y-90 radioembolization for predicting survival. Predictive factors were selected by univariate Cox regression analysis and subsequently tested by multivariate analysis for predicting patient survival. The median OS was 356 days (95% CI 285-427 days). Stable disease was observed in 132 patients, an objective response in 71 (one of which was complete remission) and progressive disease in 122. The best survival rate was observed in patients with NET, and the worst in patients with MBC. In the univariate analyses, extrahepatic disease (P < 0.001), large tumor burden (P = 0.001), high bilirubin levels (>1.9 mg/dL, P < 0.001) and low cholinesterase levels (CHE <4.62 U/I, P < 0.001) at baseline were significantly associated with poor survival. Tumor entity, tumor burden, extrahepatic disease and CHE were confirmed in the multivariate analysis as independent predictors of survival. Sex, applied RE dose and age had no significant influence on OS. Pre-therapeutic baseline bilirubin and CHE levels, extrahepatic disease and hepatic tumor burden are associated with patient survival after RE. Such parameters may be used to improve patient selection for RE of primary or metastatic liver tumors. (orig.)

Pre-therapeutic factors for predicting survival after radioembolization: a single-center experience in 389 patients

Energy Technology Data Exchange (ETDEWEB)

Paprottka, K.J.; Schoeppe, F.; Ingrisch, M.; Ruebenthaler, J.; Sommer, N.N.; Paprottka, P.M. [LMU - University of Munich, Department of Clinical Radiology, Munich (Germany); Toni, E. de [LMU - University of Munich, Department of Hepatology, Munich (Germany); Ilhan, H.; Zacherl, M.; Todica, A. [LMU - University of Munich, Department of Nuclear Medicine, Munich (Germany)

2017-07-15

To determine pre-therapeutic predictive factors for overall survival (OS) after yttrium (Y)-90 radioembolization (RE). We retrospectively analyzed the pre-therapeutic characteristics (sex, age, tumor entity, hepatic tumor burden, extrahepatic disease [EHD] and liver function [with focus on bilirubin and cholinesterase level]) of 389 consecutive patients with various refractory liver-dominant tumors (hepatocellular carcinoma [HCC], cholangiocarcinoma [CCC], neuroendocrine tumor [NET], colorectal cancer [CRC] and metastatic breast cancer [MBC]), who received Y-90 radioembolization for predicting survival. Predictive factors were selected by univariate Cox regression analysis and subsequently tested by multivariate analysis for predicting patient survival. The median OS was 356 days (95% CI 285-427 days). Stable disease was observed in 132 patients, an objective response in 71 (one of which was complete remission) and progressive disease in 122. The best survival rate was observed in patients with NET, and the worst in patients with MBC. In the univariate analyses, extrahepatic disease (P < 0.001), large tumor burden (P = 0.001), high bilirubin levels (>1.9 mg/dL, P < 0.001) and low cholinesterase levels (CHE <4.62 U/I, P < 0.001) at baseline were significantly associated with poor survival. Tumor entity, tumor burden, extrahepatic disease and CHE were confirmed in the multivariate analysis as independent predictors of survival. Sex, applied RE dose and age had no significant influence on OS. Pre-therapeutic baseline bilirubin and CHE levels, extrahepatic disease and hepatic tumor burden are associated with patient survival after RE. Such parameters may be used to improve patient selection for RE of primary or metastatic liver tumors. (orig.)

Trends in colorectal cancer survival in northern Denmark: 1985-2004.

Science.gov (United States)

Iversen, L H; Nørgaard, M; Jepsen, P; Jacobsen, J; Christensen, M M; Gandrup, P; Madsen, M R; Laurberg, S; Wogelius, P; Sørensen, H T

2007-03-01

The prognosis for colorectal cancer (CRC) is less favourable in Denmark than in neighbouring countries. To improve cancer treatment in Denmark, a National Cancer Plan was proposed in 2000. We conducted this population-based study to monitor recent trends in CRC survival and mortality in four Danish counties. We used hospital discharge registry data for the period January 1985-March 2004 in the counties of north Jutland, Ringkjøbing, Viborg and Aarhus. We computed crude survival and used Cox proportional hazards regression analysis to compare mortality over time, adjusted for age and gender. A total of 19,515 CRC patients were identified and linked with the Central Office of Civil Registration to ascertain survival through January 2005. From 1985 to 2004, 1-year and 5-year survival improved both for patients with colon and rectal cancer. From 1995-1999 to 2000-2004, overall 1-year survival of 65% for colon cancer did not improve, and some age groups experienced a decreasing 1-year survival probability. For rectal cancer, overall 1-year survival increased from 71% in 1995-1999 to 74% in 2000-2004. Using 1985-1989 as reference period, 30-day mortality did not decrease after implementation of the National Cancer Plan in 2000, neither for patients with colon nor rectal cancer. However, 1-year mortality for patients with rectal cancer did decline after its implementation. Survival and mortality from colon and rectal cancer improved before the National Cancer Plan was proposed; after its implementation, however, improvement has been observed for rectal cancer only.

ESC guidelines adherence is associated with improved survival in patients from the Norwegian Heart Failure Registry.

Science.gov (United States)

De Blois, Jonathan; Fagerland, Morten Wang; Grundtvig, Morten; Semb, Anne Grete; Gullestad, Lars; Westheim, Arne; Hole, Torstein; Atar, Dan; Agewall, Stefan

2015-01-01

To assess the adherence to heart failure (HF) guidelines for angiotensin-converting enzyme-I (ACE-I), angiotensin II receptor blockers (ARB), and β-blockers and the possible association of ACE-I or ARB, β-blockers, and statins with survival in the large contemporary Norwegian Heart Failure Registry. The study included 5761 outpatients who were diagnosed with HF of any aetiology (mean left ventricular ejection fraction 32% ± 11%) from January 2000 to January 2010 and followed up until death or February 2010. Adherence to treatment according to the guidelines was high. Cox regression analysis to identify risk factors for all-cause mortality, after adjustment for many factors, showed that ACE-I ≥ 50% of target dose, use of beta-blockers, and statins were significantly related to improved survival (P = 0.003, P < 0.001, and P < 0.001, respectively). Propensity scoring showed the same benefit for these variables. Both multivariable and propensity scoring analyses showed survival benefits with β-blockers, statins, and adequate doses of ACE-I in this contemporary HF cohort. This study stresses the importance of guidelines adherence, even in the context of high levels of adherence to guidelines. Moreover, respecting the recommended target doses of ACE-I appears to have a crucial role in survival improvement and, in the multivariate Cox regression analysis, ARB treatment was not significantly associated with a lower all-cause mortality. Published on behalf of the European Society of Cardiology. All rights reserved. ©The Author 2015. For permissions please email: journals.permissions@oup.com.

Prediction of hearing outcomes by multiple regression analysis in patients with idiopathic sudden sensorineural hearing loss.

Science.gov (United States)

Suzuki, Hideaki; Tabata, Takahisa; Koizumi, Hiroki; Hohchi, Nobusuke; Takeuchi, Shoko; Kitamura, Takuro; Fujino, Yoshihisa; Ohbuchi, Toyoaki

2014-12-01

This study aimed to create a multiple regression model for predicting hearing outcomes of idiopathic sudden sensorineural hearing loss (ISSNHL). The participants were 205 consecutive patients (205 ears) with ISSNHL (hearing level ≥ 40 dB, interval between onset and treatment ≤ 30 days). They received systemic steroid administration combined with intratympanic steroid injection. Data were examined by simple and multiple regression analyses. Three hearing indices (percentage hearing improvement, hearing gain, and posttreatment hearing level [HLpost]) and 7 prognostic factors (age, days from onset to treatment, initial hearing level, initial hearing level at low frequencies, initial hearing level at high frequencies, presence of vertigo, and contralateral hearing level) were included in the multiple regression analysis as dependent and explanatory variables, respectively. In the simple regression analysis, the percentage hearing improvement, hearing gain, and HLpost showed significant correlation with 2, 5, and 6 of the 7 prognostic factors, respectively. The multiple correlation coefficients were 0.396, 0.503, and 0.714 for the percentage hearing improvement, hearing gain, and HLpost, respectively. Predicted values of HLpost calculated by the multiple regression equation were reliable with 70% probability with a 40-dB-width prediction interval. Prediction of HLpost by the multiple regression model may be useful to estimate the hearing prognosis of ISSNHL. © The Author(s) 2014.

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)

[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.

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.

Finding determinants of audit delay by pooled OLS regression analysis

OpenAIRE

Vuko, Tina; Čular, Marko

2014-01-01

The aim of this paper is to investigate determinants of audit delay. Audit delay is measured as the length of time (i.e. the number of calendar days) from the fiscal year-end to the audit report date. It is important to understand factors that influence audit delay since it directly affects the timeliness of financial reporting. The research is conducted on a sample of Croatian listed companies, covering the period of four years (from 2008 to 2011). We use pooled OLS regression analysis, mode...