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…

A logistic regression estimating function for spatial Gibbs point processes

DEFF Research Database (Denmark)

Baddeley, Adrian; Coeurjolly, Jean-François; Rubak, Ege

We propose a computationally efficient logistic regression estimating function for spatial Gibbs point processes. The sample points for the logistic regression consist of the observed point pattern together with a random pattern of dummy points. The estimating function is closely related to the p......We propose a computationally efficient logistic regression estimating function for spatial Gibbs point processes. The sample points for the logistic regression consist of the observed point pattern together with a random pattern of dummy points. The estimating function is closely related...

Targeting: Logistic Regression, Special Cases and Extensions

Directory of Open Access Journals (Sweden)

Helmut Schaeben

2014-12-01

Full Text Available Logistic regression is a classical linear model for logit-transformed conditional probabilities of a binary target variable. It recovers the true conditional probabilities if the joint distribution of predictors and the target is of log-linear form. Weights-of-evidence is an ordinary logistic regression with parameters equal to the differences of the weights of evidence if all predictor variables are discrete and conditionally independent given the target variable. The hypothesis of conditional independence can be tested in terms of log-linear models. If the assumption of conditional independence is violated, the application of weights-of-evidence does not only corrupt the predicted conditional probabilities, but also their rank transform. Logistic regression models, including the interaction terms, can account for the lack of conditional independence, appropriate interaction terms compensate exactly for violations of conditional independence. Multilayer artificial neural nets may be seen as nested regression-like models, with some sigmoidal activation function. Most often, the logistic function is used as the activation function. If the net topology, i.e., its control, is sufficiently versatile to mimic interaction terms, artificial neural nets are able to account for violations of conditional independence and yield very similar results. Weights-of-evidence cannot reasonably include interaction terms; subsequent modifications of the weights, as often suggested, cannot emulate the effect of interaction terms.

Logistic regression for risk factor modelling in stuttering research.

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Reed, Phil; Wu, Yaqionq

2013-06-01

To outline the uses of logistic regression and other statistical methods for risk factor analysis in the context of research on stuttering. The principles underlying the application of a logistic regression are illustrated, and the types of questions to which such a technique has been applied in the stuttering field are outlined. The assumptions and limitations of the technique are discussed with respect to existing stuttering research, and with respect to formulating appropriate research strategies to accommodate these considerations. Finally, some alternatives to the approach are briefly discussed. The way the statistical procedures are employed are demonstrated with some hypothetical data. Research into several practical issues concerning stuttering could benefit if risk factor modelling were used. Important examples are early diagnosis, prognosis (whether a child will recover or persist) and assessment of treatment outcome. After reading this article you will: (a) Summarize the situations in which logistic regression can be applied to a range of issues about stuttering; (b) Follow the steps in performing a logistic regression analysis; (c) Describe the assumptions of the logistic regression technique and the precautions that need to be checked when it is employed; (d) Be able to summarize its advantages over other techniques like estimation of group differences and simple regression. Copyright © 2012 Elsevier Inc. All rights reserved.

Sample size determination for logistic regression on a logit-normal distribution.

Science.gov (United States)

Kim, Seongho; Heath, Elisabeth; Heilbrun, Lance

2017-06-01

Although the sample size for simple logistic regression can be readily determined using currently available methods, the sample size calculation for multiple logistic regression requires some additional information, such as the coefficient of determination ([Formula: see text]) of a covariate of interest with other covariates, which is often unavailable in practice. The response variable of logistic regression follows a logit-normal distribution which can be generated from a logistic transformation of a normal distribution. Using this property of logistic regression, we propose new methods of determining the sample size for simple and multiple logistic regressions using a normal transformation of outcome measures. Simulation studies and a motivating example show several advantages of the proposed methods over the existing methods: (i) no need for [Formula: see text] for multiple logistic regression, (ii) available interim or group-sequential designs, and (iii) much smaller required sample size.

Parameters Estimation of Geographically Weighted Ordinal Logistic Regression (GWOLR) Model

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Zuhdi, Shaifudin; Retno Sari Saputro, Dewi; Widyaningsih, Purnami

2017-06-01

A regression model is the representation of relationship between independent variable and dependent variable. The dependent variable has categories used in the logistic regression model to calculate odds on. The logistic regression model for dependent variable has levels in the logistics regression model is ordinal. GWOLR model is an ordinal logistic regression model influenced the geographical location of the observation site. Parameters estimation in the model needed to determine the value of a population based on sample. The purpose of this research is to parameters estimation of GWOLR model using R software. Parameter estimation uses the data amount of dengue fever patients in Semarang City. Observation units used are 144 villages in Semarang City. The results of research get GWOLR model locally for each village and to know probability of number dengue fever patient categories.

Interpreting parameters in the logistic regression model with random effects

DEFF Research Database (Denmark)

Larsen, Klaus; Petersen, Jørgen Holm; Budtz-Jørgensen, Esben

2000-01-01

interpretation, interval odds ratio, logistic regression, median odds ratio, normally distributed random effects......interpretation, interval odds ratio, logistic regression, median odds ratio, normally distributed random effects...

Advanced colorectal neoplasia risk stratification by penalized logistic regression.

Science.gov (United States)

Lin, Yunzhi; Yu, Menggang; Wang, Sijian; Chappell, Richard; Imperiale, Thomas F

2016-08-01

Colorectal cancer is the second leading cause of death from cancer in the United States. To facilitate the efficiency of colorectal cancer screening, there is a need to stratify risk for colorectal cancer among the 90% of US residents who are considered "average risk." In this article, we investigate such risk stratification rules for advanced colorectal neoplasia (colorectal cancer and advanced, precancerous polyps). We use a recently completed large cohort study of subjects who underwent a first screening colonoscopy. Logistic regression models have been used in the literature to estimate the risk of advanced colorectal neoplasia based on quantifiable risk factors. However, logistic regression may be prone to overfitting and instability in variable selection. Since most of the risk factors in our study have several categories, it was tempting to collapse these categories into fewer risk groups. We propose a penalized logistic regression method that automatically and simultaneously selects variables, groups categories, and estimates their coefficients by penalizing the [Formula: see text]-norm of both the coefficients and their differences. Hence, it encourages sparsity in the categories, i.e. grouping of the categories, and sparsity in the variables, i.e. variable selection. We apply the penalized logistic regression method to our data. The important variables are selected, with close categories simultaneously grouped, by penalized regression models with and without the interactions terms. The models are validated with 10-fold cross-validation. The receiver operating characteristic curves of the penalized regression models dominate the receiver operating characteristic curve of naive logistic regressions, indicating a superior discriminative performance. © The Author(s) 2013.

Big Five Personality Traits of Cybercrime Victims.

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van de Weijer, Steve G A; Leukfeldt, E Rutger

2017-07-01

The prevalence of cybercrime has increased rapidly over the last decades and has become part of the everyday life of citizens. It is, therefore, of great importance to gain more knowledge on the factors related to an increased or decreased likelihood of becoming a cybercrime victim. The current study adds to the existing body of knowledge using a large representative sample of Dutch individuals (N = 3,648) to study the relationship between cybercrime victimization and the key traits from the Big Five model of personality (i.e., extraversion, agreeableness, conscientiousness, emotional stability, and openness to experience). First, multinomial logistic regression analyses were used to examine the associations between the personality traits and three victim groups, that is, cybercrime victims versus nonvictims, traditional crime victims versus nonvictims, and cybercrime victims versus traditional crime victims. Next, logistic regression analyses were performed to predict victimization of cyber-dependent crimes (i.e., hacking and virus infection) and cyber-enabled crimes (i.e., online intimidation, online consumer fraud, and theft from bank account). The analyses show that personality traits are not specifically associated with cybercrime victimization, but rather with victimization in general. Only those with higher scores on emotional stability were less likely to become a victim of cybercrime than traditional crime. Furthermore, the results indicate that there are little differences between personality traits related to victimization of cyber-enabled and cyber-dependent crimes. Only individuals with higher scores on openness to experience have higher odds of becoming a victim of cyber-enabled crimes.

Logistic Regression Modeling of Diminishing Manufacturing Sources for Integrated Circuits

National Research Council Canada - National Science Library

Gravier, Michael

1999-01-01

.... The research identified logistic regression as a powerful tool for analysis of DMSMS and further developed twenty models attempting to identify the "best" way to model and predict DMSMS using logistic regression...

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

On logistic regression analysis of dichotomized responses.

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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.

A Methodology for Generating Placement Rules that Utilizes Logistic Regression

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Wurtz, Keith

2008-01-01

The purpose of this article is to provide the necessary tools for institutional researchers to conduct a logistic regression analysis and interpret the results. Aspects of the logistic regression procedure that are necessary to evaluate models are presented and discussed with an emphasis on cutoff values and choosing the appropriate number of…

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

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

2007-09-01

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

The crux of the method: assumptions in ordinary least squares and logistic regression.

Science.gov (United States)

Long, Rebecca G

2008-10-01

Logistic regression has increasingly become the tool of choice when analyzing data with a binary dependent variable. While resources relating to the technique are widely available, clear discussions of why logistic regression should be used in place of ordinary least squares regression are difficult to find. The current paper compares and contrasts the assumptions of ordinary least squares with those of logistic regression and explains why logistic regression's looser assumptions make it adept at handling violations of the more important assumptions in ordinary least squares.

An Entropy-Based Measure for Assessing Fuzziness in Logistic Regression

Science.gov (United States)

Weiss, Brandi A.; Dardick, William

2016-01-01

This article introduces an entropy-based measure of data-model fit that can be used to assess the quality of logistic regression models. Entropy has previously been used in mixture-modeling to quantify how well individuals are classified into latent classes. The current study proposes the use of entropy for logistic regression models to quantify…

Victimization by Bullying and Physical Symptoms among South Korean Schoolchildren

Science.gov (United States)

Lee, Ji Hyeon

2018-01-01

This study examined the relationship between victimization by bullying and physical symptoms among South Korean school children. Data were analyzed from a nationally representative sample of 2006 schoolchildren across South Korea aged 9-17 years. Multiple logistic regression analysis was used to estimate the associations between victimization by…

Steganalysis using logistic regression

Science.gov (United States)

Lubenko, Ivans; Ker, Andrew D.

2011-02-01

We advocate Logistic Regression (LR) as an alternative to the Support Vector Machine (SVM) classifiers commonly used in steganalysis. LR offers more information than traditional SVM methods - it estimates class probabilities as well as providing a simple classification - and can be adapted more easily and efficiently for multiclass problems. Like SVM, LR can be kernelised for nonlinear classification, and it shows comparable classification accuracy to SVM methods. This work is a case study, comparing accuracy and speed of SVM and LR classifiers in detection of LSB Matching and other related spatial-domain image steganography, through the state-of-art 686-dimensional SPAM feature set, in three image sets.

SEPARATION PHENOMENA LOGISTIC REGRESSION

Directory of Open Access Journals (Sweden)

Ikaro Daniel de Carvalho Barreto

2014-03-01

Full Text Available This paper proposes an application of concepts about the maximum likelihood estimation of the binomial logistic regression model to the separation phenomena. It generates bias in the estimation and provides different interpretations of the estimates on the different statistical tests (Wald, Likelihood Ratio and Score and provides different estimates on the different iterative methods (Newton-Raphson and Fisher Score. It also presents an example that demonstrates the direct implications for the validation of the model and validation of variables, the implications for estimates of odds ratios and confidence intervals, generated from the Wald statistics. Furthermore, we present, briefly, the Firth correction to circumvent the phenomena of separation.

Estimating the exceedance probability of rain rate by logistic regression

Science.gov (United States)

Chiu, Long S.; Kedem, Benjamin

1990-01-01

Recent studies have shown that the fraction of an area with rain intensity above a fixed threshold is highly correlated with the area-averaged rain rate. To estimate the fractional rainy area, a logistic regression model, which estimates the conditional probability that rain rate over an area exceeds a fixed threshold given the values of related covariates, is developed. The problem of dependency in the data in the estimation procedure is bypassed by the method of partial likelihood. Analyses of simulated scanning multichannel microwave radiometer and observed electrically scanning microwave radiometer data during the Global Atlantic Tropical Experiment period show that the use of logistic regression in pixel classification is superior to multiple regression in predicting whether rain rate at each pixel exceeds a given threshold, even in the presence of noisy data. The potential of the logistic regression technique in satellite rain rate estimation is discussed.

The effect of high leverage points on the logistic ridge regression estimator having multicollinearity

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Ariffin, Syaiba Balqish; Midi, Habshah

2014-06-01

This article is concerned with the performance of logistic ridge regression estimation technique in the presence of multicollinearity and high leverage points. In logistic regression, multicollinearity exists among predictors and in the information matrix. The maximum likelihood estimator suffers a huge setback in the presence of multicollinearity which cause regression estimates to have unduly large standard errors. To remedy this problem, a logistic ridge regression estimator is put forward. It is evident that the logistic ridge regression estimator outperforms the maximum likelihood approach for handling multicollinearity. The effect of high leverage points are then investigated on the performance of the logistic ridge regression estimator through real data set and simulation study. The findings signify that logistic ridge regression estimator fails to provide better parameter estimates in the presence of both high leverage points and multicollinearity.

Intermediate and advanced topics in multilevel logistic regression analysis.

Science.gov (United States)

Austin, Peter C; Merlo, Juan

2017-09-10

Multilevel data occur frequently in health services, population and public health, and epidemiologic research. In such research, binary outcomes are common. Multilevel logistic regression models allow one to account for the clustering of subjects within clusters of higher-level units when estimating the effect of subject and cluster characteristics on subject outcomes. A search of the PubMed database demonstrated that the use of multilevel or hierarchical regression models is increasing rapidly. However, our impression is that many analysts simply use multilevel regression models to account for the nuisance of within-cluster homogeneity that is induced by clustering. In this article, we describe a suite of analyses that can complement the fitting of multilevel logistic regression models. These ancillary analyses permit analysts to estimate the marginal or population-average effect of covariates measured at the subject and cluster level, in contrast to the within-cluster or cluster-specific effects arising from the original multilevel logistic regression model. We describe the interval odds ratio and the proportion of opposed odds ratios, which are summary measures of effect for cluster-level covariates. We describe the variance partition coefficient and the median odds ratio which are measures of components of variance and heterogeneity in outcomes. These measures allow one to quantify the magnitude of the general contextual effect. We describe an R 2 measure that allows analysts to quantify the proportion of variation explained by different multilevel logistic regression models. We illustrate the application and interpretation of these measures by analyzing mortality in patients hospitalized with a diagnosis of acute myocardial infarction. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

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

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

2014-03-01

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

Geographically Weighted Logistic Regression Applied to Credit Scoring Models

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Pedro Henrique Melo Albuquerque

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

A Solution to Separation and Multicollinearity in Multiple Logistic Regression.

Science.gov (United States)

Shen, Jianzhao; Gao, Sujuan

2008-10-01

In dementia screening tests, item selection for shortening an existing screening test can be achieved using multiple logistic regression. However, maximum likelihood estimates for such logistic regression models often experience serious bias or even non-existence because of separation and multicollinearity problems resulting from a large number of highly correlated items. Firth (1993, Biometrika, 80(1), 27-38) proposed a penalized likelihood estimator for generalized linear models and it was shown to reduce bias and the non-existence problems. The ridge regression has been used in logistic regression to stabilize the estimates in cases of multicollinearity. However, neither solves the problems for each other. In this paper, we propose a double penalized maximum likelihood estimator combining Firth's penalized likelihood equation with a ridge parameter. We present a simulation study evaluating the empirical performance of the double penalized likelihood estimator in small to moderate sample sizes. We demonstrate the proposed approach using a current screening data from a community-based dementia study.

Adolescent sexual victimization

DEFF Research Database (Denmark)

Bramsen, Rikke Holm; Lasgaard, Mathias; Koss, Mary P

2012-01-01

at baseline and first time APSV during a 6-month period. Data analysis was a binary logistic regression analysis. Number of sexual partners and displaying sexual risk behaviors significantly predicted subsequent first time peer-on-peer sexual victimization, whereas a history of child sexual abuse, early......The present study set out to investigate predictors of first time adolescent peer-on-peer sexual victimization (APSV) among 238 female Grade 9 students from 30 schools in Denmark. A prospective research design was utilized to examine the relationship among five potential predictors as measured...... sexual onset and failing to signal sexual boundaries did not. The present study identifies specific risk factors for first time sexual victimization that are potentially changeable. Thus, the results may inform prevention initiatives targeting initial experiences of APSV....

Fuzzy multinomial logistic regression analysis: A multi-objective programming approach

Science.gov (United States)

Abdalla, Hesham A.; El-Sayed, Amany A.; Hamed, Ramadan

2017-05-01

Parameter estimation for multinomial logistic regression is usually based on maximizing the likelihood function. For large well-balanced datasets, Maximum Likelihood (ML) estimation is a satisfactory approach. Unfortunately, ML can fail completely or at least produce poor results in terms of estimated probabilities and confidence intervals of parameters, specially for small datasets. In this study, a new approach based on fuzzy concepts is proposed to estimate parameters of the multinomial logistic regression. The study assumes that the parameters of multinomial logistic regression are fuzzy. Based on the extension principle stated by Zadeh and Bárdossy's proposition, a multi-objective programming approach is suggested to estimate these fuzzy parameters. A simulation study is used to evaluate the performance of the new approach versus Maximum likelihood (ML) approach. Results show that the new proposed model outperforms ML in cases of small datasets.

Model building strategy for logistic regression: purposeful selection.

Science.gov (United States)

Zhang, Zhongheng

2016-03-01

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

Immigrants as crime victims: Experiences of personal nonfatal victimization.

Science.gov (United States)

Wheeler, Krista; Zhao, Weiyan; Kelleher, Kelly; Stallones, Lorann; Xiang, Huiyun

2010-04-01

Immigrants to the United States are disproportionately victims of homicide mortality in and outside the workplace. Examining their experiences with nonfatal victimization may be helpful in understanding immigrant vulnerability to violence. We compared the annual prevalence of nonfatal personal victimization experienced by immigrant and US-born adults by sociodemographics, employment, occupation, industry, smoking, alcohol and drug use using data from Wave 1 National Epidemiologic Survey on Alcohol and Related Conditions. The prevalence of victimization among immigrants was comparable to that among US-born adults [3.84% (95% CI: 3.18-4.63) vs. 4.10% (95% CI: 3.77-4.44)]. Lower percentages of victimization experienced by immigrants were seen among the unmarried, those age 30-44 years, and among residents of central city areas as compared to those groups among the US-born. For immigrants entering the US as youth, the victimization prevalence declines with greater years of residency in US. Multivariate logistic regression models suggest that, the odds of victimization was significantly associated with age, family income, marital status, central city residency, smoking, and drug use while employment status was not a significant factor. Immigrant workers with farming/forestry occupations might face a higher risk of being victims of violence than their US-born counterparts. The prevalence of victimization among immigrants was comparable to that among US-born adults. Employment status and industry/occupation overall were not significant risk factors for becoming victims of violence. (c) 2010 Wiley-Liss, Inc.

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

Science.gov (United States)

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

2017-04-01

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

Logistic regression a self-learning text

CERN Document Server

Kleinbaum, David G

1994-01-01

This textbook provides students and professionals in the health sciences with a presentation of the use of logistic regression in research. The text is self-contained, and designed to be used both in class or as a tool for self-study. It arises from the author's many years of experience teaching this material and the notes on which it is based have been extensively used throughout the world.

Predicting Social Trust with Binary Logistic Regression

Science.gov (United States)

Adwere-Boamah, Joseph; Hufstedler, Shirley

2015-01-01

This study used binary logistic regression to predict social trust with five demographic variables from a national sample of adult individuals who participated in The General Social Survey (GSS) in 2012. The five predictor variables were respondents' highest degree earned, race, sex, general happiness and the importance of personally assisting…

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

Science.gov (United States)

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

When the Cop Is the Victim: A Test of Target Congruence Theory on Intimate Partner Violence Victimization Experienced by Police Officers.

Science.gov (United States)

Zavala, Egbert

2017-05-01

This study analyzed data from the Police Stress and Domestic Violence in Police Families in Baltimore, Maryland, 1997-1999 ( N = 753) to examine propositions derived from target congruence theory in the context of intimate partner violence (IPV) victimization experienced by police officers. Specifically, this study tested the influence of target vulnerability, target gratifiability, and target antagonism on IPV victimization. Results from logistic regression models showed that all three theoretical constructs positively and significantly predicted IPV victimization. Results, as well as the study's limitations and directions for future research, are discussed.

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

Science.gov (United States)

Vaeth, Michael; Skovlund, Eva

2004-06-15

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

Computing group cardinality constraint solutions for logistic regression problems.

Science.gov (United States)

Zhang, Yong; Kwon, Dongjin; Pohl, Kilian M

2017-01-01

We derive an algorithm to directly solve logistic regression based on cardinality constraint, group sparsity and use it to classify intra-subject MRI sequences (e.g. cine MRIs) of healthy from diseased subjects. Group cardinality constraint models are often applied to medical images in order to avoid overfitting of the classifier to the training data. Solutions within these models are generally determined by relaxing the cardinality constraint to a weighted feature selection scheme. However, these solutions relate to the original sparse problem only under specific assumptions, which generally do not hold for medical image applications. In addition, inferring clinical meaning from features weighted by a classifier is an ongoing topic of discussion. Avoiding weighing features, we propose to directly solve the group cardinality constraint logistic regression problem by generalizing the Penalty Decomposition method. To do so, we assume that an intra-subject series of images represents repeated samples of the same disease patterns. We model this assumption by combining series of measurements created by a feature across time into a single group. Our algorithm then derives a solution within that model by decoupling the minimization of the logistic regression function from enforcing the group sparsity constraint. The minimum to the smooth and convex logistic regression problem is determined via gradient descent while we derive a closed form solution for finding a sparse approximation of that minimum. We apply our method to cine MRI of 38 healthy controls and 44 adult patients that received reconstructive surgery of Tetralogy of Fallot (TOF) during infancy. Our method correctly identifies regions impacted by TOF and generally obtains statistically significant higher classification accuracy than alternative solutions to this model, i.e., ones relaxing group cardinality constraints. Copyright © 2016 Elsevier B.V. All rights reserved.

What Are the Odds of that? A Primer on Understanding Logistic Regression

Science.gov (United States)

Huang, Francis L.; Moon, Tonya R.

2013-01-01

The purpose of this Methodological Brief is to present a brief primer on logistic regression, a commonly used technique when modeling dichotomous outcomes. Using data from the National Education Longitudinal Study of 1988 (NELS:88), logistic regression techniques were used to investigate student-level variables in eighth grade (i.e., enrolled in a…

Determining factors influencing survival of breast cancer by fuzzy logistic regression model.

Science.gov (United States)

Nikbakht, Roya; Bahrampour, Abbas

2017-01-01

Fuzzy logistic regression model can be used for determining influential factors of disease. This study explores the important factors of actual predictive survival factors of breast cancer's patients. We used breast cancer data which collected by cancer registry of Kerman University of Medical Sciences during the period of 2000-2007. The variables such as morphology, grade, age, and treatments (surgery, radiotherapy, and chemotherapy) were applied in the fuzzy logistic regression model. Performance of model was determined in terms of mean degree of membership (MDM). The study results showed that almost 41% of patients were in neoplasm and malignant group and more than two-third of them were still alive after 5-year follow-up. Based on the fuzzy logistic model, the most important factors influencing survival were chemotherapy, morphology, and radiotherapy, respectively. Furthermore, the MDM criteria show that the fuzzy logistic regression have a good fit on the data (MDM = 0.86). Fuzzy logistic regression model showed that chemotherapy is more important than radiotherapy in survival of patients with breast cancer. In addition, another ability of this model is calculating possibilistic odds of survival in cancer patients. The results of this study can be applied in clinical research. Furthermore, there are few studies which applied the fuzzy logistic models. Furthermore, we recommend using this model in various research areas.

Score Normalization using Logistic Regression with Expected Parameters

NARCIS (Netherlands)

Aly, Robin

State-of-the-art score normalization methods use generative models that rely on sometimes unrealistic assumptions. We propose a novel parameter estimation method for score normalization based on logistic regression. Experiments on the Gov2 and CluewebA collection indicate that our method is

Modeling Governance KB with CATPCA to Overcome Multicollinearity in the Logistic Regression

Science.gov (United States)

Khikmah, L.; Wijayanto, H.; Syafitri, U. D.

2017-04-01

The problem often encounters in logistic regression modeling are multicollinearity problems. Data that have multicollinearity between explanatory variables with the result in the estimation of parameters to be bias. Besides, the multicollinearity will result in error in the classification. In general, to overcome multicollinearity in regression used stepwise regression. They are also another method to overcome multicollinearity which involves all variable for prediction. That is Principal Component Analysis (PCA). However, classical PCA in only for numeric data. Its data are categorical, one method to solve the problems is Categorical Principal Component Analysis (CATPCA). Data were used in this research were a part of data Demographic and Population Survey Indonesia (IDHS) 2012. This research focuses on the characteristic of women of using the contraceptive methods. Classification results evaluated using Area Under Curve (AUC) values. The higher the AUC value, the better. Based on AUC values, the classification of the contraceptive method using stepwise method (58.66%) is better than the logistic regression model (57.39%) and CATPCA (57.39%). Evaluation of the results of logistic regression using sensitivity, shows the opposite where CATPCA method (99.79%) is better than logistic regression method (92.43%) and stepwise (92.05%). Therefore in this study focuses on major class classification (using a contraceptive method), then the selected model is CATPCA because it can raise the level of the major class model accuracy.

Logistic regression modelling: procedures and pitfalls in developing and interpreting prediction models

Directory of Open Access Journals (Sweden)

Nataša Šarlija

2017-01-01

Full Text Available This study sheds light on the most common issues related to applying logistic regression in prediction models for company growth. The purpose of the paper is 1 to provide a detailed demonstration of the steps in developing a growth prediction model based on logistic regression analysis, 2 to discuss common pitfalls and methodological errors in developing a model, and 3 to provide solutions and possible ways of overcoming these issues. Special attention is devoted to the question of satisfying logistic regression assumptions, selecting and defining dependent and independent variables, using classification tables and ROC curves, for reporting model strength, interpreting odds ratios as effect measures and evaluating performance of the prediction model. Development of a logistic regression model in this paper focuses on a prediction model of company growth. The analysis is based on predominantly financial data from a sample of 1471 small and medium-sized Croatian companies active between 2009 and 2014. The financial data is presented in the form of financial ratios divided into nine main groups depicting following areas of business: liquidity, leverage, activity, profitability, research and development, investing and export. The growth prediction model indicates aspects of a business critical for achieving high growth. In that respect, the contribution of this paper is twofold. First, methodological, in terms of pointing out pitfalls and potential solutions in logistic regression modelling, and secondly, theoretical, in terms of identifying factors responsible for high growth of small and medium-sized companies.

Classifying machinery condition using oil samples and binary logistic regression

Science.gov (United States)

Phillips, J.; Cripps, E.; Lau, John W.; Hodkiewicz, M. R.

2015-08-01

The era of big data has resulted in an explosion of condition monitoring information. The result is an increasing motivation to automate the costly and time consuming human elements involved in the classification of machine health. When working with industry it is important to build an understanding and hence some trust in the classification scheme for those who use the analysis to initiate maintenance tasks. Typically "black box" approaches such as artificial neural networks (ANN) and support vector machines (SVM) can be difficult to provide ease of interpretability. In contrast, this paper argues that logistic regression offers easy interpretability to industry experts, providing insight to the drivers of the human classification process and to the ramifications of potential misclassification. Of course, accuracy is of foremost importance in any automated classification scheme, so we also provide a comparative study based on predictive performance of logistic regression, ANN and SVM. A real world oil analysis data set from engines on mining trucks is presented and using cross-validation we demonstrate that logistic regression out-performs the ANN and SVM approaches in terms of prediction for healthy/not healthy engines.

A Bayesian goodness of fit test and semiparametric generalization of logistic regression with measurement data.

Science.gov (United States)

Schörgendorfer, Angela; Branscum, Adam J; Hanson, Timothy E

2013-06-01

Logistic regression is a popular tool for risk analysis in medical and population health science. With continuous response data, it is common to create a dichotomous outcome for logistic regression analysis by specifying a threshold for positivity. Fitting a linear regression to the nondichotomized response variable assuming a logistic sampling model for the data has been empirically shown to yield more efficient estimates of odds ratios than ordinary logistic regression of the dichotomized endpoint. We illustrate that risk inference is not robust to departures from the parametric logistic distribution. Moreover, the model assumption of proportional odds is generally not satisfied when the condition of a logistic distribution for the data is violated, leading to biased inference from a parametric logistic analysis. We develop novel Bayesian semiparametric methodology for testing goodness of fit of parametric logistic regression with continuous measurement data. The testing procedures hold for any cutoff threshold and our approach simultaneously provides the ability to perform semiparametric risk estimation. Bayes factors are calculated using the Savage-Dickey ratio for testing the null hypothesis of logistic regression versus a semiparametric generalization. We propose a fully Bayesian and a computationally efficient empirical Bayesian approach to testing, and we present methods for semiparametric estimation of risks, relative risks, and odds ratios when parametric logistic regression fails. Theoretical results establish the consistency of the empirical Bayes test. Results from simulated data show that the proposed approach provides accurate inference irrespective of whether parametric assumptions hold or not. Evaluation of risk factors for obesity shows that different inferences are derived from an analysis of a real data set when deviations from a logistic distribution are permissible in a flexible semiparametric framework. © 2013, The International Biometric

Diagnosis of cranial hemangioma: Comparison between logistic regression analysis and neuronal network

International Nuclear Information System (INIS)

Arana, E.; Marti-Bonmati, L.; Bautista, D.; Paredes, R.

1998-01-01

To study the utility of logistic regression and the neuronal network in the diagnosis of cranial hemangiomas. Fifteen patients presenting hemangiomas were selected form a total of 167 patients with cranial lesions. All were evaluated by plain radiography and computed tomography (CT). Nineteen variables in their medical records were reviewed. Logistic regression and neuronal network models were constructed and validated by the jackknife (leave-one-out) approach. The yields of the two models were compared by means of ROC curves, using the area under the curve as parameter. Seven men and 8 women presented hemangiomas. The mean age of these patients was 38.4 (15.4 years (mea ± standard deviation). Logistic regression identified as significant variables the shape, soft tissue mass and periosteal reaction. The neuronal network lent more importance to the existence of ossified matrix, ruptured cortical vein and the mixed calcified-blastic (trabeculated) pattern. The neuronal network showed a greater yield than logistic regression (Az, 0.9409) (0.004 versus 0.7211± 0.075; p<0.001). The neuronal network discloses hidden interactions among the variables, providing a higher yield in the characterization of cranial hemangiomas and constituting a medical diagnostic acid. (Author)29 refs

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

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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).

Two-factor logistic regression in pediatric liver transplantation

Science.gov (United States)

Uzunova, Yordanka; Prodanova, Krasimira; Spasov, Lyubomir

2017-12-01

Using a two-factor logistic regression analysis an estimate is derived for the probability of absence of infections in the early postoperative period after pediatric liver transplantation. The influence of both the bilirubin level and the international normalized ratio of prothrombin time of blood coagulation at the 5th postoperative day is studied.

Adjusting for Confounding in Early Postlaunch Settings: Going Beyond Logistic Regression Models.

Science.gov (United States)

Schmidt, Amand F; Klungel, Olaf H; Groenwold, Rolf H H

2016-01-01

Postlaunch data on medical treatments can be analyzed to explore adverse events or relative effectiveness in real-life settings. These analyses are often complicated by the number of potential confounders and the possibility of model misspecification. We conducted a simulation study to compare the performance of logistic regression, propensity score, disease risk score, and stabilized inverse probability weighting methods to adjust for confounding. Model misspecification was induced in the independent derivation dataset. We evaluated performance using relative bias confidence interval coverage of the true effect, among other metrics. At low events per coefficient (1.0 and 0.5), the logistic regression estimates had a large relative bias (greater than -100%). Bias of the disease risk score estimates was at most 13.48% and 18.83%. For the propensity score model, this was 8.74% and >100%, respectively. At events per coefficient of 1.0 and 0.5, inverse probability weighting frequently failed or reduced to a crude regression, resulting in biases of -8.49% and 24.55%. Coverage of logistic regression estimates became less than the nominal level at events per coefficient ≤5. For the disease risk score, inverse probability weighting, and propensity score, coverage became less than nominal at events per coefficient ≤2.5, ≤1.0, and ≤1.0, respectively. Bias of misspecified disease risk score models was 16.55%. In settings with low events/exposed subjects per coefficient, disease risk score methods can be useful alternatives to logistic regression models, especially when propensity score models cannot be used. Despite better performance of disease risk score methods than logistic regression and propensity score models in small events per coefficient settings, bias, and coverage still deviated from nominal.

Supporting Regularized Logistic Regression Privately and Efficiently

Science.gov (United States)

Li, Wenfa; Liu, Hongzhe; Yang, Peng; Xie, Wei

2016-01-01

As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Concerns over data privacy make it increasingly difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used statistical model while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluations on several studies validate the privacy guarantee, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc. PMID:27271738

Supporting Regularized Logistic Regression Privately and Efficiently.

Science.gov (United States)

Li, Wenfa; Liu, Hongzhe; Yang, Peng; Xie, Wei

2016-01-01

As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Concerns over data privacy make it increasingly difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used statistical model while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluations on several studies validate the privacy guarantee, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc.

Supporting Regularized Logistic Regression Privately and Efficiently.

Directory of Open Access Journals (Sweden)

Wenfa Li

Full Text Available As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Concerns over data privacy make it increasingly difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used statistical model while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluations on several studies validate the privacy guarantee, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc.

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

Science.gov (United States)

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

2015-08-01

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

Power and Sample Size Calculations for Logistic Regression Tests for Differential Item Functioning

Science.gov (United States)

Li, Zhushan

2014-01-01

Logistic regression is a popular method for detecting uniform and nonuniform differential item functioning (DIF) effects. Theoretical formulas for the power and sample size calculations are derived for likelihood ratio tests and Wald tests based on the asymptotic distribution of the maximum likelihood estimators for the logistic regression model.…

Predicting company growth using logistic regression and neural networks

Directory of Open Access Journals (Sweden)

Marijana Zekić-Sušac

2016-12-01

Full Text Available The paper aims to establish an efficient model for predicting company growth by leveraging the strengths of logistic regression and neural networks. A real dataset of Croatian companies was used which described the relevant industry sector, financial ratios, income, and assets in the input space, with a dependent binomial variable indicating whether a company had high-growth if it had annualized growth in assets by more than 20% a year over a three-year period. Due to a large number of input variables, factor analysis was performed in the pre -processing stage in order to extract the most important input components. Building an efficient model with a high classification rate and explanatory ability required application of two data mining methods: logistic regression as a parametric and neural networks as a non -parametric method. The methods were tested on the models with and without variable reduction. The classification accuracy of the models was compared using statistical tests and ROC curves. The results showed that neural networks produce a significantly higher classification accuracy in the model when incorporating all available variables. The paper further discusses the advantages and disadvantages of both approaches, i.e. logistic regression and neural networks in modelling company growth. The suggested model is potentially of benefit to investors and economic policy makers as it provides support for recognizing companies with growth potential, especially during times of economic downturn.

Landslide Hazard Mapping in Rwanda Using Logistic Regression

Science.gov (United States)

Piller, A.; Anderson, E.; Ballard, H.

2015-12-01

Landslides in the United States cause more than $1 billion in damages and 50 deaths per year (USGS 2014). Globally, figures are much more grave, yet monitoring, mapping and forecasting of these hazards are less than adequate. Seventy-five percent of the population of Rwanda earns a living from farming, mostly subsistence. Loss of farmland, housing, or life, to landslides is a very real hazard. Landslides in Rwanda have an impact at the economic, social, and environmental level. In a developing nation that faces challenges in tracking, cataloging, and predicting the numerous landslides that occur each year, satellite imagery and spatial analysis allow for remote study. We have focused on the development of a landslide inventory and a statistical methodology for assessing landslide hazards. Using logistic regression on approximately 30 test variables (i.e. slope, soil type, land cover, etc.) and a sample of over 200 landslides, we determine which variables are statistically most relevant to landslide occurrence in Rwanda. A preliminary predictive hazard map for Rwanda has been produced, using the variables selected from the logistic regression analysis.

An appraisal of convergence failures in the application of logistic regression model in published manuscripts.

Science.gov (United States)

Yusuf, O B; Bamgboye, E A; Afolabi, R F; Shodimu, M A

2014-09-01

Logistic regression model is widely used in health research for description and predictive purposes. Unfortunately, most researchers are sometimes not aware that the underlying principles of the techniques have failed when the algorithm for maximum likelihood does not converge. Young researchers particularly postgraduate students may not know why separation problem whether quasi or complete occurs, how to identify it and how to fix it. This study was designed to critically evaluate convergence issues in articles that employed logistic regression analysis published in an African Journal of Medicine and medical sciences between 2004 and 2013. Problems of quasi or complete separation were described and were illustrated with the National Demographic and Health Survey dataset. A critical evaluation of articles that employed logistic regression was conducted. A total of 581 articles was reviewed, of which 40 (6.9%) used binary logistic regression. Twenty-four (60.0%) stated the use of logistic regression model in the methodology while none of the articles assessed model fit. Only 3 (12.5%) properly described the procedures. Of the 40 that used the logistic regression model, the problem of convergence occurred in 6 (15.0%) of the articles. Logistic regression tends to be poorly reported in studies published between 2004 and 2013. Our findings showed that the procedure may not be well understood by researchers since very few described the process in their reports and may be totally unaware of the problem of convergence or how to deal with it.

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

Science.gov (United States)

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

2015-11-04

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

Multinomial logistic regression in workers' health

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

2017-11-01

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

The cross-validated AUC for MCP-logistic regression with high-dimensional data.

Science.gov (United States)

Jiang, Dingfeng; Huang, Jian; Zhang, Ying

2013-10-01

We propose a cross-validated area under the receiving operator characteristic (ROC) curve (CV-AUC) criterion for tuning parameter selection for penalized methods in sparse, high-dimensional logistic regression models. We use this criterion in combination with the minimax concave penalty (MCP) method for variable selection. The CV-AUC criterion is specifically designed for optimizing the classification performance for binary outcome data. To implement the proposed approach, we derive an efficient coordinate descent algorithm to compute the MCP-logistic regression solution surface. Simulation studies are conducted to evaluate the finite sample performance of the proposed method and its comparison with the existing methods including the Akaike information criterion (AIC), Bayesian information criterion (BIC) or Extended BIC (EBIC). The model selected based on the CV-AUC criterion tends to have a larger predictive AUC and smaller classification error than those with tuning parameters selected using the AIC, BIC or EBIC. We illustrate the application of the MCP-logistic regression with the CV-AUC criterion on three microarray datasets from the studies that attempt to identify genes related to cancers. Our simulation studies and data examples demonstrate that the CV-AUC is an attractive method for tuning parameter selection for penalized methods in high-dimensional logistic regression models.

Estimating Engineering and Manufacturing Development Cost Risk Using Logistic and Multiple Regression

National Research Council Canada - National Science Library

Bielecki, John

2003-01-01

.... Previous research has demonstrated the use of a two-step logistic and multiple regression methodology to predicting cost growth produces desirable results versus traditional single-step regression...

Rank-Optimized Logistic Matrix Regression toward Improved Matrix Data Classification.

Science.gov (United States)

Zhang, Jianguang; Jiang, Jianmin

2018-02-01

While existing logistic regression suffers from overfitting and often fails in considering structural information, we propose a novel matrix-based logistic regression to overcome the weakness. In the proposed method, 2D matrices are directly used to learn two groups of parameter vectors along each dimension without vectorization, which allows the proposed method to fully exploit the underlying structural information embedded inside the 2D matrices. Further, we add a joint [Formula: see text]-norm on two parameter matrices, which are organized by aligning each group of parameter vectors in columns. This added co-regularization term has two roles-enhancing the effect of regularization and optimizing the rank during the learning process. With our proposed fast iterative solution, we carried out extensive experiments. The results show that in comparison to both the traditional tensor-based methods and the vector-based regression methods, our proposed solution achieves better performance for matrix data classifications.

Performance and strategy comparisons of human listeners and logistic regression in discriminating underwater targets.

Science.gov (United States)

Yang, Lixue; Chen, Kean

2015-11-01

To improve the design of underwater target recognition systems based on auditory perception, this study compared human listeners with automatic classifiers. Performances measures and strategies in three discrimination experiments, including discriminations between man-made and natural targets, between ships and submarines, and among three types of ships, were used. In the experiments, the subjects were asked to assign a score to each sound based on how confident they were about the category to which it belonged, and logistic regression, which represents linear discriminative models, also completed three similar tasks by utilizing many auditory features. The results indicated that the performances of logistic regression improved as the ratio between inter- and intra-class differences became larger, whereas the performances of the human subjects were limited by their unfamiliarity with the targets. Logistic regression performed better than the human subjects in all tasks but the discrimination between man-made and natural targets, and the strategies employed by excellent human subjects were similar to that of logistic regression. Logistic regression and several human subjects demonstrated similar performances when discriminating man-made and natural targets, but in this case, their strategies were not similar. An appropriate fusion of their strategies led to further improvement in recognition accuracy.

Psychosocial Correlates of Dating Violence Victimization among Latino Youth

Science.gov (United States)

Howard, Donna E.; Beck, Kenneth; Kerr, Melissa Hallmark; Shattuck, Teresa

2005-01-01

To examine the association between physical dating violence victimization and risk and protective factors, an anonymous, cross-sectional, self-reported survey was administered to Latino youth (n = 446) residing in suburban Washington, DC. Multivariate logistic regression models were constructed, and adjusted OR and 95% CI were examined.…

Network Exposure and Homicide Victimization in an African American Community

Science.gov (United States)

Wildeman, Christopher

2014-01-01

Objectives. We estimated the association of an individual’s exposure to homicide in a social network and the risk of individual homicide victimization across a high-crime African American community. Methods. Combining 5 years of homicide and police records, we analyzed a network of 3718 high-risk individuals that was created by instances of co-offending. We used logistic regression to model the odds of being a gunshot homicide victim by individual characteristics, network position, and indirect exposure to homicide. Results. Forty-one percent of all gun homicides occurred within a network component containing less than 4% of the neighborhood’s population. Network-level indicators reduced the association between individual risk factors and homicide victimization and improved the overall prediction of individual victimization. Network exposure to homicide was strongly associated with victimization: the closer one is to a homicide victim, the greater the risk of victimization. Regression models show that exposure diminished with social distance: each social tie removed from a homicide victim decreased one’s odds of being a homicide victim by 57%. Conclusions. Risk of homicide in urban areas is even more highly concentrated than previously thought. We found that most of the risk of gun violence was concentrated in networks of identifiable individuals. Understanding these networks may improve prediction of individual homicide victimization within disadvantaged communities. PMID:24228655

Cyberbullying perpetration and victimization among middle-school students.

Science.gov (United States)

Rice, Eric; Petering, Robin; Rhoades, Harmony; Winetrobe, Hailey; Goldbach, Jeremy; Plant, Aaron; Montoya, Jorge; Kordic, Timothy

2015-03-01

We examined correlations between gender, race, sexual identity, and technology use, and patterns of cyberbullying experiences and behaviors among middle-school students. We collected a probability sample of 1285 students alongside the 2012 Youth Risk Behavior Survey in Los Angeles Unified School District middle schools. We used logistic regressions to assess the correlates of being a cyberbully perpetrator, victim, and perpetrator-victim (i.e., bidirectional cyberbullying behavior). In this sample, 6.6% reported being a cyberbully victim, 5.0% reported being a perpetrator, and 4.3% reported being a perpetrator-victim. Cyberbullying behavior frequently occurred on Facebook or via text messaging. Cyberbully perpetrators, victims, and perpetrators-victims all were more likely to report using the Internet for at least 3 hours per day. Sexual-minority students and students who texted at least 50 times per day were more likely to report cyberbullying victimization. Girls were more likely to report being perpetrators-victims. Cyberbullying interventions should account for gender and sexual identity, as well as the possible benefits of educational interventions for intensive Internet users and frequent texters.

Cyberbullying Perpetration and Victimization Among Middle-School Students

Science.gov (United States)

Rice, Eric; Rhoades, Harmony; Winetrobe, Hailey; Goldbach, Jeremy; Plant, Aaron; Montoya, Jorge; Kordic, Timothy

2015-01-01

Objectives. We examined correlations between gender, race, sexual identity, and technology use, and patterns of cyberbullying experiences and behaviors among middle-school students. Methods. We collected a probability sample of 1285 students alongside the 2012 Youth Risk Behavior Survey in Los Angeles Unified School District middle schools. We used logistic regressions to assess the correlates of being a cyberbully perpetrator, victim, and perpetrator–victim (i.e., bidirectional cyberbullying behavior). Results. In this sample, 6.6% reported being a cyberbully victim, 5.0% reported being a perpetrator, and 4.3% reported being a perpetrator–victim. Cyberbullying behavior frequently occurred on Facebook or via text messaging. Cyberbully perpetrators, victims, and perpetrators–victims all were more likely to report using the Internet for at least 3 hours per day. Sexual-minority students and students who texted at least 50 times per day were more likely to report cyberbullying victimization. Girls were more likely to report being perpetrators–victims. Conclusions. Cyberbullying interventions should account for gender and sexual identity, as well as the possible benefits of educational interventions for intensive Internet users and frequent texters. PMID:25602905

On the Usefulness of a Multilevel Logistic Regression Approach to Person-Fit Analysis

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Conijn, Judith M.; Emons, Wilco H. M.; van Assen, Marcel A. L. M.; Sijtsma, Klaas

2011-01-01

The logistic person response function (PRF) models the probability of a correct response as a function of the item locations. Reise (2000) proposed to use the slope parameter of the logistic PRF as a person-fit measure. He reformulated the logistic PRF model as a multilevel logistic regression model and estimated the PRF parameters from this…

Comparison of standard maximum likelihood classification and polytomous logistic regression used in remote sensing

Science.gov (United States)

John Hogland; Nedret Billor; Nathaniel Anderson

2013-01-01

Discriminant analysis, referred to as maximum likelihood classification within popular remote sensing software packages, is a common supervised technique used by analysts. Polytomous logistic regression (PLR), also referred to as multinomial logistic regression, is an alternative classification approach that is less restrictive, more flexible, and easy to interpret. To...

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.

On-line mixture-based alternative to logistic regression

Czech Academy of Sciences Publication Activity Database

Nagy, Ivan; Suzdaleva, Evgenia

2016-01-01

Roč. 26, č. 5 (2016), s. 417-437 ISSN 1210-0552 R&D Projects: GA ČR GA15-03564S Institutional support: RVO:67985556 Keywords : on-line modeling * on-line logistic regression * recursive mixture estimation * data dependent pointer Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.394, year: 2016 http://library.utia.cas.cz/separaty/2016/ZS/suzdaleva-0464463.pdf

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

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MILAD TAZIK

2017-11-01

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

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

International Nuclear Information System (INIS)

Wen Jinai; Yuan Liyun; Jiang Ruyi

1999-01-01

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

MODELING SNAKE MICROHABITAT FROM RADIOTELEMETRY STUDIES USING POLYTOMOUS LOGISTIC REGRESSION

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Multivariate analysis of snake microhabitat has historically used techniques that were derived under assumptions of normality and common covariance structure (e.g., discriminant function analysis, MANOVA). In this study, polytomous logistic regression (PLR which does not require ...

Parameter Estimation for Improving Association Indicators in Binary Logistic Regression

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Mahdi Bashiri

2012-02-01

Full Text Available The aim of this paper is estimation of Binary logistic regression parameters for maximizing the log-likelihood function with improved association indicators. In this paper the parameter estimation steps have been explained and then measures of association have been introduced and their calculations have been analyzed. Moreover a new related indicators based on membership degree level have been expressed. Indeed association measures demonstrate the number of success responses occurred in front of failure in certain number of Bernoulli independent experiments. In parameter estimation, existing indicators values is not sensitive to the parameter values, whereas the proposed indicators are sensitive to the estimated parameters during the iterative procedure. Therefore, proposing a new association indicator of binary logistic regression with more sensitivity to the estimated parameters in maximizing the log- likelihood in iterative procedure is innovation of this study.

New robust statistical procedures for the polytomous logistic regression models.

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Castilla, Elena; Ghosh, Abhik; Martin, Nirian; Pardo, Leandro

2018-05-17

This article derives a new family of estimators, namely the minimum density power divergence estimators, as a robust generalization of the maximum likelihood estimator for the polytomous logistic regression model. Based on these estimators, a family of Wald-type test statistics for linear hypotheses is introduced. Robustness properties of both the proposed estimators and the test statistics are theoretically studied through the classical influence function analysis. Appropriate real life examples are presented to justify the requirement of suitable robust statistical procedures in place of the likelihood based inference for the polytomous logistic regression model. The validity of the theoretical results established in the article are further confirmed empirically through suitable simulation studies. Finally, an approach for the data-driven selection of the robustness tuning parameter is proposed with empirical justifications. © 2018, The International Biometric Society.

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...

A binary logistic regression model with complex sampling design of ...

African Journals Online (AJOL)

2017-09-03

Sep 3, 2017 ... Bi-variable and multi-variable binary logistic regression model with complex sampling design was fitted. .... Data was entered into STATA-12 and analyzed using. SPSS-21. .... lack of access/too far or costs too much. 35. 1.2.

Differential item functioning analysis with ordinal logistic regression techniques. DIFdetect and difwithpar.

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Crane, Paul K; Gibbons, Laura E; Jolley, Lance; van Belle, Gerald

2006-11-01

We present an ordinal logistic regression model for identification of items with differential item functioning (DIF) and apply this model to a Mini-Mental State Examination (MMSE) dataset. We employ item response theory ability estimation in our models. Three nested ordinal logistic regression models are applied to each item. Model testing begins with examination of the statistical significance of the interaction term between ability and the group indicator, consistent with nonuniform DIF. Then we turn our attention to the coefficient of the ability term in models with and without the group term. If including the group term has a marked effect on that coefficient, we declare that it has uniform DIF. We examined DIF related to language of test administration in addition to self-reported race, Hispanic ethnicity, age, years of education, and sex. We used PARSCALE for IRT analyses and STATA for ordinal logistic regression approaches. We used an iterative technique for adjusting IRT ability estimates on the basis of DIF findings. Five items were found to have DIF related to language. These same items also had DIF related to other covariates. The ordinal logistic regression approach to DIF detection, when combined with IRT ability estimates, provides a reasonable alternative for DIF detection. There appear to be several items with significant DIF related to language of test administration in the MMSE. More attention needs to be paid to the specific criteria used to determine whether an item has DIF, not just the technique used to identify DIF.

Sample size calculation to externally validate scoring systems based on logistic regression models.

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Antonio Palazón-Bru

Full Text Available A sample size containing at least 100 events and 100 non-events has been suggested to validate a predictive model, regardless of the model being validated and that certain factors can influence calibration of the predictive model (discrimination, parameterization and incidence. Scoring systems based on binary logistic regression models are a specific type of predictive model.The aim of this study was to develop an algorithm to determine the sample size for validating a scoring system based on a binary logistic regression model and to apply it to a case study.The algorithm was based on bootstrap samples in which the area under the ROC curve, the observed event probabilities through smooth curves, and a measure to determine the lack of calibration (estimated calibration index were calculated. To illustrate its use for interested researchers, the algorithm was applied to a scoring system, based on a binary logistic regression model, to determine mortality in intensive care units.In the case study provided, the algorithm obtained a sample size with 69 events, which is lower than the value suggested in the literature.An algorithm is provided for finding the appropriate sample size to validate scoring systems based on binary logistic regression models. This could be applied to determine the sample size in other similar cases.

MENENTUKAN PROBABILITAS QUALITAS LULUSAN PROGRAM STUDI MENGGUNAKAN LOGISTIC REGRESSION

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Maxsi Ary

2016-03-01

Full Text Available Abstract – Human resources (HR is one of the success factors in the economic field, namely how to create a human resources (HR qualified and have the skills and highly competitive in the global competition. Educational level of the labor force that is still relatively low. The structure of education of the workforce is still dominated Indonesian basic education which is about 63.2%. The issue raised is to determine the probability of a program of study (whether or not to see some of the ratio of the number of graduates by the number of students per class, the amount of quota size class (large or small using logistic regression models. Data were obtained from a search result based on the amount of data the study program students and graduates in 2010 Data processing using SPSS. The results of the analysis by assessing model fit and the results will be given for each model fit. Starting with the hypothesis for assessing model fit, statistical -2LogL, Cox and Snell's R Square, Hosmer and Lemeshow's Goodness of Fit Test, and the classification table. The results of the analysis using SPSS as a tool aimed at measuring quality of graduate courses at a university, college, or academy, whether or not based on the ratio of the number of graduates and class quotas. Keywords: Quota Class, Probability, Logistic Regression Abstrak – Sumberdaya manusia (SDM adalah salah satu faktor kesuksesan dalam bidang ekonomi, yaitu bagaimana menciptakan sumber daya manusia (SDM yang berkualitas dan memiliki keterampilan serta berdaya saing tinggi dalam persaingan global. Tingkat pendidikan angkatan kerja yang ada masih relatif rendah. Struktur pendidikan angkatan kerja Indonesia masih didominasi pendidikan dasar yaitu sekitar 63,2%. Persoalan yang dikemukakan adalah menentukan probabilitas sebuah program studi (baik atau tidak dengan melihat beberapa rasio jumlah lulusan dengan jumlah mahasiswa per angkatan, ukuran besarnya kuota kelas (besar atau kecil menggunakan

Use of Logistic Regression for Forecasting Short-Term Volcanic Activity

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Mark T. Woods

2012-08-01

Full Text Available An algorithm that forecasts volcanic activity using an event tree decision making framework and logistic regression has been developed, characterized, and validated. The suite of empirical models that drive the system were derived from a sparse and geographically diverse dataset comprised of source modeling results, volcano monitoring data, and historic information from analog volcanoes. Bootstrapping techniques were applied to the training dataset to allow for the estimation of robust logistic model coefficients. Probabilities generated from the logistic models increase with positive modeling results, escalating seismicity, and rising eruption frequency. Cross validation yielded a series of receiver operating characteristic curves with areas ranging between 0.78 and 0.81, indicating that the algorithm has good forecasting capabilities. Our results suggest that the logistic models are highly transportable and can compete with, and in some cases outperform, non-transportable empirical models trained with site specific information.

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

Science.gov (United States)

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.

Race, Ethnicity, and Adolescent Violent Victimization.

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Tillyer, Marie Skubak; Tillyer, Rob

2016-07-01

The risk of adolescent violent victimization in the United States varies considerably across racial and ethnic populations; it is unknown whether the sources of risk also vary by race and ethnicity. This study examined the correlates of violent victimization for White, Black, and Hispanic youth. Data collected from 11,070 adolescents (51 % female, mean age = 15.04 years) during the first two waves of the National Longitudinal Study of Adolescent to Adult Health were used to estimate group-specific multilevel logistic regression models. The results indicate that male, violent offending, peer deviance, gang membership, and low self-control were significantly associated with increased odds of violent victimization for all groups. Some activities-including getting drunk, sneaking out, and unstructured socializing with peers-were risk factors for Black adolescents only; skipping school was a risk factor only for Hispanic adolescents. Although there are many similarities across groups, the findings suggest that minority adolescents are particularly vulnerable to violent victimization when they engage in some activities and minor forms of delinquency.

BANK FAILURE PREDICTION WITH LOGISTIC REGRESSION

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Taha Zaghdoudi

2013-04-01

Full Text Available In recent years the economic and financial world is shaken by a wave of financial crisis and resulted in violent bank fairly huge losses. Several authors have focused on the study of the crises in order to develop an early warning model. It is in the same path that our work takes its inspiration. Indeed, we have tried to develop a predictive model of Tunisian bank failures with the contribution of the binary logistic regression method. The specificity of our prediction model is that it takes into account microeconomic indicators of bank failures. The results obtained using our provisional model show that a bank's ability to repay its debt, the coefficient of banking operations, bank profitability per employee and leverage financial ratio has a negative impact on the probability of failure.

Comparing Methodologies for Developing an Early Warning System: Classification and Regression Tree Model versus Logistic Regression. REL 2015-077

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Koon, Sharon; Petscher, Yaacov

2015-01-01

The purpose of this report was to explicate the use of logistic regression and classification and regression tree (CART) analysis in the development of early warning systems. It was motivated by state education leaders' interest in maintaining high classification accuracy while simultaneously improving practitioner understanding of the rules by…

Predicting 30-day Hospital Readmission with Publicly Available Administrative Database. A Conditional Logistic Regression Modeling Approach.

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Zhu, K; Lou, Z; Zhou, J; Ballester, N; Kong, N; Parikh, P

2015-01-01

This article is part of the Focus Theme of Methods of Information in Medicine on "Big Data and Analytics in Healthcare". Hospital readmissions raise healthcare costs and cause significant distress to providers and patients. It is, therefore, of great interest to healthcare organizations to predict what patients are at risk to be readmitted to their hospitals. However, current logistic regression based risk prediction models have limited prediction power when applied to hospital administrative data. Meanwhile, although decision trees and random forests have been applied, they tend to be too complex to understand among the hospital practitioners. Explore the use of conditional logistic regression to increase the prediction accuracy. We analyzed an HCUP statewide inpatient discharge record dataset, which includes patient demographics, clinical and care utilization data from California. We extracted records of heart failure Medicare beneficiaries who had inpatient experience during an 11-month period. We corrected the data imbalance issue with under-sampling. In our study, we first applied standard logistic regression and decision tree to obtain influential variables and derive practically meaning decision rules. We then stratified the original data set accordingly and applied logistic regression on each data stratum. We further explored the effect of interacting variables in the logistic regression modeling. We conducted cross validation to assess the overall prediction performance of conditional logistic regression (CLR) and compared it with standard classification models. The developed CLR models outperformed several standard classification models (e.g., straightforward logistic regression, stepwise logistic regression, random forest, support vector machine). For example, the best CLR model improved the classification accuracy by nearly 20% over the straightforward logistic regression model. Furthermore, the developed CLR models tend to achieve better sensitivity of

Police Victimization Among Persons Who Inject Drugs Along the U.S.-Mexico Border.

Science.gov (United States)

Pinedo, Miguel; Burgos, Jose Luis; Zuniga, Maria Luisa; Perez, Ramona; Macera, Caroline A; Ojeda, Victoria D

2015-09-01

Problematic policing practices are an important driver of HIV infection among persons who inject drugs (PWID) in the U.S.-Mexico border region. This study identifies factors associated with recent (i.e., past 6 months) police victimization (e.g., extortion, physical and sexual violence) in the border city of Tijuana, Mexico. From 2011 to 2013, 733 PWID (62% male) were recruited in Tijuana and completed a structured questionnaire. Eligible participants were age 18 years or older, injected illicit drugs within the past month, and spoke Spanish or English. Multivariable logistic regression analyses identified correlates of recent experiences of police victimization (e.g., bribes, unlawful confiscation, physical and sexual violence). Overall, 56% of PWID reported a recent police victimization experience in Tijuana. In multivariable logistic regression analyses, factors independently associated with recent police victimization included recent injection of methamphetamine (adjusted odds ratio [AOR] = 1.62; 95% CI [1.18, 2.21]) and recently received injection assistance by a "hit doctor" (AOR = 1.56; 95% CI [1.03, 2.36]). Increased years lived in Tijuana (AOR = 0.98 per year; 95% CI [0.97, 0.99]) and initiating drug use at a later age (AOR = 0.96 per year; 95% CI [0.92, 0.99]) were inversely associated with recent police victimization. Physical drugusing markers may increase PWID susceptibility to police targeting and contribute to experiences of victimization. Interventions aimed at reducing police victimization events in the U.S.-Mexico border region should consider PWID's drug-using behaviors. Reducing problematic policing practices may be a crucial public health strategy to reduce HIV risk among PWID in this region.

Association between bullying victimization and substance use among college students in Spain.

Science.gov (United States)

Caravaca Sánchez, Francisco; Navarro Zaragoza, Javier; Luna Ruiz-Cabello, Aurelio; Falcón Romero, María; Luna Maldonado, Aurelio

2016-06-14

The purpose of this study is to analyze the prevalence and association between victimization and substance use among the university population in the southeast of Spain in a sample of 543 randomly selected college students (405 females and 138 males with an average age of 22.6 years). As a cross-sectional study, data was collected through an anonymous survey to assess victimization and drug use over the last 12 months. Results indicated that 62.2% of college students reported bullying victimization and 82.9% consumed some type of psychoactive substance, and found a statistically significant association between both variables measured. Additionally, logistic regression analysis confirmed the association between psychoactive substance use and different types of victimization. Our findings confirm the need for prevention to prevent this relation between victimization and substance use.

A comparison of Cox and logistic regression for use in genome-wide association studies of cohort and case-cohort design.

Science.gov (United States)

Staley, James R; Jones, Edmund; Kaptoge, Stephen; Butterworth, Adam S; Sweeting, Michael J; Wood, Angela M; Howson, Joanna M M

2017-06-01

Logistic regression is often used instead of Cox regression to analyse genome-wide association studies (GWAS) of single-nucleotide polymorphisms (SNPs) and disease outcomes with cohort and case-cohort designs, as it is less computationally expensive. Although Cox and logistic regression models have been compared previously in cohort studies, this work does not completely cover the GWAS setting nor extend to the case-cohort study design. Here, we evaluated Cox and logistic regression applied to cohort and case-cohort genetic association studies using simulated data and genetic data from the EPIC-CVD study. In the cohort setting, there was a modest improvement in power to detect SNP-disease associations using Cox regression compared with logistic regression, which increased as the disease incidence increased. In contrast, logistic regression had more power than (Prentice weighted) Cox regression in the case-cohort setting. Logistic regression yielded inflated effect estimates (assuming the hazard ratio is the underlying measure of association) for both study designs, especially for SNPs with greater effect on disease. Given logistic regression is substantially more computationally efficient than Cox regression in both settings, we propose a two-step approach to GWAS in cohort and case-cohort studies. First to analyse all SNPs with logistic regression to identify associated variants below a pre-defined P-value threshold, and second to fit Cox regression (appropriately weighted in case-cohort studies) to those identified SNPs to ensure accurate estimation of association with disease.

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

Directory of Open Access Journals (Sweden)

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.

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

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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.

Using the Logistic Regression model in supporting decisions of establishing marketing strategies

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Cristinel CONSTANTIN

2015-12-01

Full Text Available This paper is about an instrumental research regarding the using of Logistic Regression model for data analysis in marketing research. The decision makers inside different organisation need relevant information to support their decisions regarding the marketing strategies. The data provided by marketing research could be computed in various ways but the multivariate data analysis models can enhance the utility of the information. Among these models we can find the Logistic Regression model, which is used for dichotomous variables. Our research is based on explanation the utility of this model and interpretation of the resulted information in order to help practitioners and researchers to use it in their future investigations

A comparative study on entrepreneurial attitudes modeled with logistic regression and Bayes nets.

Science.gov (United States)

López Puga, Jorge; García García, Juan

2012-11-01

Entrepreneurship research is receiving increasing attention in our context, as entrepreneurs are key social agents involved in economic development. We compare the success of the dichotomic logistic regression model and the Bayes simple classifier to predict entrepreneurship, after manipulating the percentage of missing data and the level of categorization in predictors. A sample of undergraduate university students (N = 1230) completed five scales (motivation, attitude towards business creation, obstacles, deficiencies, and training needs) and we found that each of them predicted different aspects of the tendency to business creation. Additionally, our results show that the receiver operating characteristic (ROC) curve is affected by the rate of missing data in both techniques, but logistic regression seems to be more vulnerable when faced with missing data, whereas Bayes nets underperform slightly when categorization has been manipulated. Our study sheds light on the potential entrepreneur profile and we propose to use Bayesian networks as an additional alternative to overcome the weaknesses of logistic regression when missing data are present in applied research.

Bayesian logistic regression in detection of gene-steroid interaction for cancer at PDLIM5 locus.

Science.gov (United States)

Wang, Ke-Sheng; Owusu, Daniel; Pan, Yue; Xie, Changchun

2016-06-01

The PDZ and LIM domain 5 (PDLIM5) gene may play a role in cancer, bipolar disorder, major depression, alcohol dependence and schizophrenia; however, little is known about the interaction effect of steroid and PDLIM5 gene on cancer. This study examined 47 single-nucleotide polymorphisms (SNPs) within the PDLIM5 gene in the Marshfield sample with 716 cancer patients (any diagnosed cancer, excluding minor skin cancer) and 2848 noncancer controls. Multiple logistic regression model in PLINK software was used to examine the association of each SNP with cancer. Bayesian logistic regression in PROC GENMOD in SAS statistical software, ver. 9.4 was used to detect gene- steroid interactions influencing cancer. Single marker analysis using PLINK identified 12 SNPs associated with cancer (Plogistic regression in PROC GENMOD showed that both rs6532496 and rs951613 revealed strong gene-steroid interaction effects (OR=2.18, 95% CI=1.31-3.63 with P = 2.9 × 10⁻³ for rs6532496 and OR=2.07, 95% CI=1.24-3.45 with P = 5.43 × 10⁻³ for rs951613, respectively). Results from Bayesian logistic regression showed stronger interaction effects (OR=2.26, 95% CI=1.2-3.38 for rs6532496 and OR=2.14, 95% CI=1.14-3.2 for rs951613, respectively). All the 12 SNPs associated with cancer revealed significant gene-steroid interaction effects (P logistic regression and OR=2.59, 95% CI=1.4-3.97 from Bayesian logistic regression; respectively). This study provides evidence of common genetic variants within the PDLIM5 gene and interactions between PLDIM5 gene polymorphisms and steroid use influencing cancer.

The impact of perceived childhood victimization and patriarchal gender ideology on intimate partner violence (IPV) victimization among Korean immigrant women in the USA.

Science.gov (United States)

Kim, Chunrye

2017-08-01

Childhood victimization experiences are common among intimate partner violence (IPV) victims. This study examines the link between childhood physical and sexual victimization experiences and adulthood IPV among Korean immigrant women in the USA. As Korean immigrants often use physical punishment to discipline their children, and reporting sexual abuse is discouraged due to stigmatization in this community, cultural factors (e.g. patriarchal values) related to childhood victimization and IPV were also examined. Survey data from Korean immigrant women in the USA were collected. Using a case-control design, we compared 64 Korean immigrant women who have experienced IPV in the past year with 63 Korean immigrant women who have never experienced IPV in their lifetime. The findings of this study reveal that IPV victims, compared with non-victims, experienced higher childhood victimization rates. Logistic regression analysis demonstrated that childhood victimization and patriarchal gender ideology strongly predict IPV victimization among Korean immigrants. However, patriarchal values did not moderate the relationship between childhood victimization and IPV. To prevent IPV among Korean immigrant population, we need to make special efforts to prevent childhood abuse and change ingrained cultural attitudes about child physical and sexual abuse among immigrant communities through culturally sensitive programs. Copyright © 2017 Elsevier Ltd. All rights reserved.

The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams.

Science.gov (United States)

Yu, Yuanyuan; Li, Hongkai; Sun, Xiaoru; Su, Ping; Wang, Tingting; Liu, Yi; Yuan, Zhongshang; Liu, Yanxun; Xue, Fuzhong

2017-12-28

Confounders can produce spurious associations between exposure and outcome in observational studies. For majority of epidemiologists, adjusting for confounders using logistic regression model is their habitual method, though it has some problems in accuracy and precision. It is, therefore, important to highlight the problems of logistic regression and search the alternative method. Four causal diagram models were defined to summarize confounding equivalence. Both theoretical proofs and simulation studies were performed to verify whether conditioning on different confounding equivalence sets had the same bias-reducing potential and then to select the optimum adjusting strategy, in which logistic regression model and inverse probability weighting based marginal structural model (IPW-based-MSM) were compared. The "do-calculus" was used to calculate the true causal effect of exposure on outcome, then the bias and standard error were used to evaluate the performances of different strategies. Adjusting for different sets of confounding equivalence, as judged by identical Markov boundaries, produced different bias-reducing potential in the logistic regression model. For the sets satisfied G-admissibility, adjusting for the set including all the confounders reduced the equivalent bias to the one containing the parent nodes of the outcome, while the bias after adjusting for the parent nodes of exposure was not equivalent to them. In addition, all causal effect estimations through logistic regression were biased, although the estimation after adjusting for the parent nodes of exposure was nearest to the true causal effect. However, conditioning on different confounding equivalence sets had the same bias-reducing potential under IPW-based-MSM. Compared with logistic regression, the IPW-based-MSM could obtain unbiased causal effect estimation when the adjusted confounders satisfied G-admissibility and the optimal strategy was to adjust for the parent nodes of outcome, which

The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams

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Yuanyuan Yu

2017-12-01

Full Text Available Abstract Background Confounders can produce spurious associations between exposure and outcome in observational studies. For majority of epidemiologists, adjusting for confounders using logistic regression model is their habitual method, though it has some problems in accuracy and precision. It is, therefore, important to highlight the problems of logistic regression and search the alternative method. Methods Four causal diagram models were defined to summarize confounding equivalence. Both theoretical proofs and simulation studies were performed to verify whether conditioning on different confounding equivalence sets had the same bias-reducing potential and then to select the optimum adjusting strategy, in which logistic regression model and inverse probability weighting based marginal structural model (IPW-based-MSM were compared. The “do-calculus” was used to calculate the true causal effect of exposure on outcome, then the bias and standard error were used to evaluate the performances of different strategies. Results Adjusting for different sets of confounding equivalence, as judged by identical Markov boundaries, produced different bias-reducing potential in the logistic regression model. For the sets satisfied G-admissibility, adjusting for the set including all the confounders reduced the equivalent bias to the one containing the parent nodes of the outcome, while the bias after adjusting for the parent nodes of exposure was not equivalent to them. In addition, all causal effect estimations through logistic regression were biased, although the estimation after adjusting for the parent nodes of exposure was nearest to the true causal effect. However, conditioning on different confounding equivalence sets had the same bias-reducing potential under IPW-based-MSM. Compared with logistic regression, the IPW-based-MSM could obtain unbiased causal effect estimation when the adjusted confounders satisfied G-admissibility and the optimal

Disentangling the Effects of Violent Victimization, Violent Behavior, and Gun Carrying for Minority Inner-City Youth Living in Extreme Poverty

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Spano, Richard; Bolland, John

2013-01-01

Two waves of longitudinal data were used to examine the sequencing between violent victimization, violent behavior, and gun carrying in a high-poverty sample of African American youth. Multivariate logistic regression results indicated that violent victimization T1 and violent behavior T1 increased the likelihood of initiation of gun carrying T2…

Construction of risk prediction model of type 2 diabetes mellitus based on logistic regression

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Li Jian

2017-01-01

Full Text Available Objective: to construct multi factor prediction model for the individual risk of T2DM, and to explore new ideas for early warning, prevention and personalized health services for T2DM. Methods: using logistic regression techniques to screen the risk factors for T2DM and construct the risk prediction model of T2DM. Results: Male’s risk prediction model logistic regression equation: logit(P=BMI × 0.735+ vegetables × (−0.671 + age × 0.838+ diastolic pressure × 0.296+ physical activity× (−2.287 + sleep ×(−0.009 +smoking ×0.214; Female’s risk prediction model logistic regression equation: logit(P=BMI ×1.979+ vegetables× (−0.292 + age × 1.355+ diastolic pressure× 0.522+ physical activity × (−2.287 + sleep × (−0.010.The area under the ROC curve of male was 0.83, the sensitivity was 0.72, the specificity was 0.86, the area under the ROC curve of female was 0.84, the sensitivity was 0.75, the specificity was 0.90. Conclusion: This study model data is from a compared study of nested case, the risk prediction model has been established by using the more mature logistic regression techniques, and the model is higher predictive sensitivity, specificity and stability.

Logistic regression models of factors influencing the location of bioenergy and biofuels plants

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T.M. Young; R.L. Zaretzki; J.H. Perdue; F.M. Guess; X. Liu

2011-01-01

Logistic regression models were developed to identify significant factors that influence the location of existing wood-using bioenergy/biofuels plants and traditional wood-using facilities. Logistic models provided quantitative insight for variables influencing the location of woody biomass-using facilities. Availability of "thinnings to a basal area of 31.7m2/ha...

GIS-based rare events logistic regression for mineral prospectivity mapping

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Xiong, Yihui; Zuo, Renguang

2018-02-01

Mineralization is a special type of singularity event, and can be considered as a rare event, because within a specific study area the number of prospective locations (1s) are considerably fewer than the number of non-prospective locations (0s). In this study, GIS-based rare events logistic regression (RELR) was used to map the mineral prospectivity in the southwestern Fujian Province, China. An odds ratio was used to measure the relative importance of the evidence variables with respect to mineralization. The results suggest that formations, granites, and skarn alterations, followed by faults and aeromagnetic anomaly are the most important indicators for the formation of Fe-related mineralization in the study area. The prediction rate and the area under the curve (AUC) values show that areas with higher probability have a strong spatial relationship with the known mineral deposits. Comparing the results with original logistic regression (OLR) demonstrates that the GIS-based RELR performs better than OLR. The prospectivity map obtained in this study benefits the search for skarn Fe-related mineralization in the study area.

Logistic Regression Analysis of Operational Errors and Routine Operations Using Sector Characteristics

National Research Council Canada - National Science Library

Pfleiderer, Elaine M; Scroggins, Cheryl L; Manning, Carol A

2009-01-01

Two separate logistic regression analyses were conducted for low- and high-altitude sectors to determine whether a set of dynamic sector characteristics variables could reliably discriminate between operational error (OE...

Modelling of binary logistic regression for obesity among secondary students in a rural area of Kedah

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Kamaruddin, Ainur Amira; Ali, Zalila; Noor, Norlida Mohd.; Baharum, Adam; Ahmad, Wan Muhamad Amir W.

2014-07-01

Logistic regression analysis examines the influence of various factors on a dichotomous outcome by estimating the probability of the event's occurrence. Logistic regression, also called a logit model, is a statistical procedure used to model dichotomous outcomes. In the logit model the log odds of the dichotomous outcome is modeled as a linear combination of the predictor variables. The log odds ratio in logistic regression provides a description of the probabilistic relationship of the variables and the outcome. In conducting logistic regression, selection procedures are used in selecting important predictor variables, diagnostics are used to check that assumptions are valid which include independence of errors, linearity in the logit for continuous variables, absence of multicollinearity, and lack of strongly influential outliers and a test statistic is calculated to determine the aptness of the model. This study used the binary logistic regression model to investigate overweight and obesity among rural secondary school students on the basis of their demographics profile, medical history, diet and lifestyle. The results indicate that overweight and obesity of students are influenced by obesity in family and the interaction between a student's ethnicity and routine meals intake. The odds of a student being overweight and obese are higher for a student having a family history of obesity and for a non-Malay student who frequently takes routine meals as compared to a Malay student.

Prevalence and Associated Factors of Peer Victimization (Bullying among Grades 7 and 8 Middle School Students in Kuwait

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Ahmad J. Abdulsalam

2017-01-01

Full Text Available Background. Peer victimization (bullying is a universal phenomenon with detrimental effects. The aim of this study is to determine the prevalence and factors of bullying among grades 7 and 8 middle school students in Kuwait. Methods. The study is a cross-sectional study that includes a sample of 989 7th and 8th grade middle school students randomly selected from schools. The Revised Olweus Bully/Victim Questionnaire was used to measure different forms of bullying. After adjusting for confounding, logistic regression identified the significant associated factors related to bullying. Results. Prevalence of bullying was 30.2 with 95% CI 27.4 to 33.2% (3.5% bullies, 18.9% victims, 7.8% bully victims. Children with physical disabilities and one or both non-Kuwaiti parents or children with divorced/widowed parents were more prone to be victims. Most victims and bullies were found to be current smokers. Bullies were mostly in the fail/fair final school grade category, whereas victims performed better. The logistic regression showed that male gender (adjusted odds ration = 1.671, p=0.004, grade 8 student (adjusted odds ratio = 1.650, p=0.004, and student with physical disabilities (adjusted odds ratio = 1.675, p=0.003, were independently associated with bullying behavior. Conclusions. There is a need for a school-wide professional intervention program and improvement in the students’ adjustment to school environment to control bullying behavior.

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.

Violent Victimization Among Disadvantaged Young Adults Exposed to Early Family Conflict and Abuse: A 24-Year Prospective Study of the Victimization Cycle Across Gender.

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Voith, Laura A; Topitzes, James; Reynolds, Arthur J

2016-01-01

Significant associations between childhood victimization and later revictimization have materialized in previous literature; yet, the victimization cycle has been primarily explored with indicators of sexual assault, although insight into linkages between other forms of victimization remains limited. This study examined connections from family conflict exposure and physical abuse in childhood to violent crime victimization in adulthood, assessing also gender differences and neighborhood influences. Results from logistic regression and hierarchical linear modeling with data from the Chicago Longitudinal Study, a panel of 1,539 low-income, ethnic/racial minority children, unearthed a significant relation between family conflict exposure and later revictimization. Moderated by gender, these analyses showed girls exposed to frequent family conflict are particularly vulnerable to revictimization in adulthood. Exploratory analyses unveiled a potential linkage between childhood physical abuse and later revictimization for men. Neighborhood effects marginally influenced results in one instance. Public health implications are discussed.

Landslide susceptibility mapping on a global scale using the method of logistic regression

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L. Lin

2017-08-01

Full Text Available This paper proposes a statistical model for mapping global landslide susceptibility based on logistic regression. After investigating explanatory factors for landslides in the existing literature, five factors were selected for model landslide susceptibility: relative relief, extreme precipitation, lithology, ground motion and soil moisture. When building the model, 70 % of landslide and nonlandslide points were randomly selected for logistic regression, and the others were used for model validation. To evaluate the accuracy of predictive models, this paper adopts several criteria including a receiver operating characteristic (ROC curve method. Logistic regression experiments found all five factors to be significant in explaining landslide occurrence on a global scale. During the modeling process, percentage correct in confusion matrix of landslide classification was approximately 80 % and the area under the curve (AUC was nearly 0.87. During the validation process, the above statistics were about 81 % and 0.88, respectively. Such a result indicates that the model has strong robustness and stable performance. This model found that at a global scale, soil moisture can be dominant in the occurrence of landslides and topographic factor may be secondary.

Predictors of Latent Trajectory Classes of Dating Violence Victimization

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Brooks-Russell, Ashley; Foshee, Vangie; Ennett, Susan

2014-01-01

This study identified classes of developmental trajectories of physical dating violence victimization from grades 8 to 12 and examined theoretically-based risk factors that distinguished among trajectory classes. Data were from a multi-wave longitudinal study spanning 8th through 12th grade (n = 2,566; 51.9% female). Growth mixture models were used to identify trajectory classes of physical dating violence victimization separately for girls and boys. Logistic and multinomial logistic regressions were used to identify situational and target vulnerability factors associated with the trajectory classes. For girls, three trajectory classes were identified: a low/non-involved class; a moderate class where victimization increased slightly until the 10th grade and then decreased through the 12th grade; and a high class where victimization started at a higher level in the 8th grade, increased substantially until the 10th grade, and then decreased until the 12th grade. For males, two classes were identified: a low/non-involved class, and a victimized class where victimization increased slightly until the 9th grade, decreased until the 11th grade, and then increased again through the 12th grade. In bivariate analyses, almost all of the situational and target vulnerability risk factors distinguished the victimization classes from the non-involved classes. However, when all risk factors and control variables were in the model, alcohol use (a situational vulnerability) was the only factor that distinguished membership in the moderate trajectory class from the non-involved class for girls; anxiety and being victimized by peers (target vulnerability factors) were the factors that distinguished the high from the non-involved classes for the girls; and victimization by peers was the only factor distinguishing the victimized from the non-involved class for boys. These findings contribute to our understanding of the heterogeneity in physical dating violence victimization during

PARAMETRIC AND NON PARAMETRIC (MARS: MULTIVARIATE ADDITIVE REGRESSION SPLINES) LOGISTIC REGRESSIONS FOR PREDICTION OF A DICHOTOMOUS RESPONSE VARIABLE WITH AN EXAMPLE FOR PRESENCE/ABSENCE OF AMPHIBIANS

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The purpose of this report is to provide a reference manual that could be used by investigators for making informed use of logistic regression using two methods (standard logistic regression and MARS). The details for analyses of relationships between a dependent binary response ...

Evaluation of logistic regression models and effect of covariates for case-control study in RNA-Seq analysis.

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Choi, Seung Hoan; Labadorf, Adam T; Myers, Richard H; Lunetta, Kathryn L; Dupuis, Josée; DeStefano, Anita L

2017-02-06

Next generation sequencing provides a count of RNA molecules in the form of short reads, yielding discrete, often highly non-normally distributed gene expression measurements. Although Negative Binomial (NB) regression has been generally accepted in the analysis of RNA sequencing (RNA-Seq) data, its appropriateness has not been exhaustively evaluated. We explore logistic regression as an alternative method for RNA-Seq studies designed to compare cases and controls, where disease status is modeled as a function of RNA-Seq reads using simulated and Huntington disease data. We evaluate the effect of adjusting for covariates that have an unknown relationship with gene expression. Finally, we incorporate the data adaptive method in order to compare false positive rates. When the sample size is small or the expression levels of a gene are highly dispersed, the NB regression shows inflated Type-I error rates but the Classical logistic and Bayes logistic (BL) regressions are conservative. Firth's logistic (FL) regression performs well or is slightly conservative. Large sample size and low dispersion generally make Type-I error rates of all methods close to nominal alpha levels of 0.05 and 0.01. However, Type-I error rates are controlled after applying the data adaptive method. The NB, BL, and FL regressions gain increased power with large sample size, large log2 fold-change, and low dispersion. The FL regression has comparable power to NB regression. We conclude that implementing the data adaptive method appropriately controls Type-I error rates in RNA-Seq analysis. Firth's logistic regression provides a concise statistical inference process and reduces spurious associations from inaccurately estimated dispersion parameters in the negative binomial framework.

Model performance analysis and model validation in logistic regression

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Rosa Arboretti Giancristofaro

2007-10-01

Full Text Available In this paper a new model validation procedure for a logistic regression model is presented. At first, we illustrate a brief review of different techniques of model validation. Next, we define a number of properties required for a model to be considered "good", and a number of quantitative performance measures. Lastly, we describe a methodology for the assessment of the performance of a given model by using an example taken from a management study.

The role of visual markers in police victimization among structurally vulnerable persons in Tijuana, Mexico.

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Pinedo, Miguel; Burgos, Jose Luis; Ojeda, Adriana Vargas; FitzGerald, David; Ojeda, Victoria D

2015-05-01

Law enforcement can shape HIV risk behaviours and undermine strategies aimed at curbing HIV infection. Little is known about factors that increase vulnerability to police victimization in Mexico. This study identifies correlates of police or army victimization (i.e., harassment or assault) in the past 6 months among patients seeking care at a free clinic in Tijuana, Mexico. From January to May 2013, 601 patients attending a binational student-run free clinic completed an interviewer-administered questionnaire. Eligible participants were: (1) ≥18 years old; (2) seeking care at the clinic; and (3) spoke Spanish or English. Multivariate logistic regression analyses identified factors associated with police/army victimization in the past 6 months. More than one-third (38%) of participants reported victimization by police/army officials in the past 6 months in Tijuana. In multivariate logistic regression analyses, males (adjusted odds ratio (AOR): 3.68; 95% CI: 2.19-6.19), tattooed persons (AOR: 1.56; 95% CI: 1.04-2.33) and those who injected drugs in the past 6 months (AOR: 2.11; 95% CI: 1.29-3.43) were significantly more likely to report past 6-month police/army victimization. Recent feelings of rejection (AOR: 3.80; 95% CI: 2.47-5.85) and being denied employment (AOR: 2.23; 95% CI: 1.50-3.32) were also independently associated with police/army victimization. Structural interventions aimed at reducing stigma against vulnerable populations and increasing social incorporation may aid in reducing victimization events by police/army in Tijuana. Police education and training to reduce abusive policing practices may be warranted. Copyright © 2014 Elsevier B.V. All rights reserved.

Logistic regression models for polymorphic and antagonistic pleiotropic gene action on human aging and longevity

DEFF Research Database (Denmark)

Tan, Qihua; Bathum, L; Christiansen, L

2003-01-01

In this paper, we apply logistic regression models to measure genetic association with human survival for highly polymorphic and pleiotropic genes. By modelling genotype frequency as a function of age, we introduce a logistic regression model with polytomous responses to handle the polymorphic...... situation. Genotype and allele-based parameterization can be used to investigate the modes of gene action and to reduce the number of parameters, so that the power is increased while the amount of multiple testing minimized. A binomial logistic regression model with fractional polynomials is used to capture...... the age-dependent or antagonistic pleiotropic effects. The models are applied to HFE genotype data to assess the effects on human longevity by different alleles and to detect if an age-dependent effect exists. Application has shown that these methods can serve as useful tools in searching for important...

Prevalence and Determinants of Preterm Birth in Tehran, Iran: A Comparison between Logistic Regression and Decision Tree Methods.

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Amini, Payam; Maroufizadeh, Saman; Samani, Reza Omani; Hamidi, Omid; Sepidarkish, Mahdi

2017-06-01

Preterm birth (PTB) is a leading cause of neonatal death and the second biggest cause of death in children under five years of age. The objective of this study was to determine the prevalence of PTB and its associated factors using logistic regression and decision tree classification methods. This cross-sectional study was conducted on 4,415 pregnant women in Tehran, Iran, from July 6-21, 2015. Data were collected by a researcher-developed questionnaire through interviews with mothers and review of their medical records. To evaluate the accuracy of the logistic regression and decision tree methods, several indices such as sensitivity, specificity, and the area under the curve were used. The PTB rate was 5.5% in this study. The logistic regression outperformed the decision tree for the classification of PTB based on risk factors. Logistic regression showed that multiple pregnancies, mothers with preeclampsia, and those who conceived with assisted reproductive technology had an increased risk for PTB ( p logistic regression model for the classification of risk groups for PTB.

CUSUM-Logistic Regression analysis for the rapid detection of errors in clinical laboratory test results.

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Sampson, Maureen L; Gounden, Verena; van Deventer, Hendrik E; Remaley, Alan T

2016-02-01

The main drawback of the periodic analysis of quality control (QC) material is that test performance is not monitored in time periods between QC analyses, potentially leading to the reporting of faulty test results. The objective of this study was to develop a patient based QC procedure for the more timely detection of test errors. Results from a Chem-14 panel measured on the Beckman LX20 analyzer were used to develop the model. Each test result was predicted from the other 13 members of the panel by multiple regression, which resulted in correlation coefficients between the predicted and measured result of >0.7 for 8 of the 14 tests. A logistic regression model, which utilized the measured test result, the predicted test result, the day of the week and time of day, was then developed for predicting test errors. The output of the logistic regression was tallied by a daily CUSUM approach and used to predict test errors, with a fixed specificity of 90%. The mean average run length (ARL) before error detection by CUSUM-Logistic Regression (CSLR) was 20 with a mean sensitivity of 97%, which was considerably shorter than the mean ARL of 53 (sensitivity 87.5%) for a simple prediction model that only used the measured result for error detection. A CUSUM-Logistic Regression analysis of patient laboratory data can be an effective approach for the rapid and sensitive detection of clinical laboratory errors. Published by Elsevier Inc.

Classification of mislabelled microarrays using robust sparse logistic regression.

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Bootkrajang, Jakramate; Kabán, Ata

2013-04-01

Previous studies reported that labelling errors are not uncommon in microarray datasets. In such cases, the training set may become misleading, and the ability of classifiers to make reliable inferences from the data is compromised. Yet, few methods are currently available in the bioinformatics literature to deal with this problem. The few existing methods focus on data cleansing alone, without reference to classification, and their performance crucially depends on some tuning parameters. In this article, we develop a new method to detect mislabelled arrays simultaneously with learning a sparse logistic regression classifier. Our method may be seen as a label-noise robust extension of the well-known and successful Bayesian logistic regression classifier. To account for possible mislabelling, we formulate a label-flipping process as part of the classifier. The regularization parameter is automatically set using Bayesian regularization, which not only saves the computation time that cross-validation would take, but also eliminates any unwanted effects of label noise when setting the regularization parameter. Extensive experiments with both synthetic data and real microarray datasets demonstrate that our approach is able to counter the bad effects of labelling errors in terms of predictive performance, it is effective at identifying marker genes and simultaneously it detects mislabelled arrays to high accuracy. The code is available from http://cs.bham.ac.uk/∼jxb008. Supplementary data are available at Bioinformatics online.

Analysis of sparse data in logistic regression in medical research: A newer approach

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S Devika

2016-01-01

Full Text Available Background and Objective: In the analysis of dichotomous type response variable, logistic regression is usually used. However, the performance of logistic regression in the presence of sparse data is questionable. In such a situation, a common problem is the presence of high odds ratios (ORs with very wide 95% confidence interval (CI (OR: >999.999, 95% CI: 999.999. In this paper, we addressed this issue by using penalized logistic regression (PLR method. Materials and Methods: Data from case-control study on hyponatremia and hiccups conducted in Christian Medical College, Vellore, Tamil Nadu, India was used. The outcome variable was the presence/absence of hiccups and the main exposure variable was the status of hyponatremia. Simulation dataset was created with different sample sizes and with a different number of covariates. Results: A total of 23 cases and 50 controls were used for the analysis of ordinary and PLR methods. The main exposure variable hyponatremia was present in nine (39.13% of the cases and in four (8.0% of the controls. Of the 23 hiccup cases, all were males and among the controls, 46 (92.0% were males. Thus, the complete separation between gender and the disease group led into an infinite OR with 95% CI (OR: >999.999, 95% CI: 999.999 whereas there was a finite and consistent regression coefficient for gender (OR: 5.35; 95% CI: 0.42, 816.48 using PLR. After adjusting for all the confounding variables, hyponatremia entailed 7.9 (95% CI: 2.06, 38.86 times higher risk for the development of hiccups as was found using PLR whereas there was an overestimation of risk OR: 10.76 (95% CI: 2.17, 53.41 using the conventional method. Simulation experiment shows that the estimated coverage probability of this method is near the nominal level of 95% even for small sample sizes and for a large number of covariates. Conclusions: PLR is almost equal to the ordinary logistic regression when the sample size is large and is superior in small cell

Comparison of logistic regression and neural models in predicting the outcome of biopsy in breast cancer from MRI findings

International Nuclear Information System (INIS)

Abdolmaleki, P.; Yarmohammadi, M.; Gity, M.

2004-01-01

Background: We designed an algorithmic model based on regression analysis and a non-algorithmic model based on the Artificial Neural Network. Materials and methods: The ability of these models was compared together in clinical application to differentiate malignant from benign breast tumors in a study group of 161 patient's records. Each patient's record consisted of 6 subjective features extracted from MRI appearance. These findings were enclosed as features extracted for an Artificial Neural Network as well as a logistic regression model to predict biopsy outcome. After both models had been trained perfectly on samples (n=100), the validation samples (n=61) were presented to the trained network as well as the established logistic regression models. Finally, the diagnostic performance of models were compared to the that of the radiologist in terms of sensitivity, specificity and accuracy, using receiver operating characteristic curve analysis. Results: The average out put of the Artificial Neural Network yielded a perfect sensitivity (98%) and high accuracy (90%) similar to that one of an expert radiologist (96% and 92%) while specificity was smaller than that (67%) verses 80%). The output of the logistic regression model using significant features showed improvement in specificity from 60% for the logistic regression model using all features to 93% for the reduced logistic regression model, keeping the accuracy around 90%. Conclusion: Results show that Artificial Neural Network and logistic regression model prove the relationship between extracted morphological features and biopsy results. Using statistically significant variables reduced logistic regression model outperformed of Artificial Neural Network with remarkable specificity while keeping high sensitivity is achieved

Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey

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Duman, T. Y.; Can, T.; Gokceoglu, C.; Nefeslioglu, H. A.; Sonmez, H.

2006-11-01

As a result of industrialization, throughout the world, cities have been growing rapidly for the last century. One typical example of these growing cities is Istanbul, the population of which is over 10 million. Due to rapid urbanization, new areas suitable for settlement and engineering structures are necessary. The Cekmece area located west of the Istanbul metropolitan area is studied, because the landslide activity is extensive in this area. The purpose of this study is to develop a model that can be used to characterize landslide susceptibility in map form using logistic regression analysis of an extensive landslide database. A database of landslide activity was constructed using both aerial-photography and field studies. About 19.2% of the selected study area is covered by deep-seated landslides. The landslides that occur in the area are primarily located in sandstones with interbedded permeable and impermeable layers such as claystone, siltstone and mudstone. About 31.95% of the total landslide area is located at this unit. To apply logistic regression analyses, a data matrix including 37 variables was constructed. The variables used in the forwards stepwise analyses are different measures of slope, aspect, elevation, stream power index (SPI), plan curvature, profile curvature, geology, geomorphology and relative permeability of lithological units. A total of 25 variables were identified as exerting strong influence on landslide occurrence, and included by the logistic regression equation. Wald statistics values indicate that lithology, SPI and slope are more important than the other parameters in the equation. Beta coefficients of the 25 variables included the logistic regression equation provide a model for landslide susceptibility in the Cekmece area. This model is used to generate a landslide susceptibility map that correctly classified 83.8% of the landslide-prone areas.

Appropriate assessment of neighborhood effects on individual health: integrating random and fixed effects in multilevel logistic regression

DEFF Research Database (Denmark)

Larsen, Klaus; Merlo, Juan

2005-01-01

The logistic regression model is frequently used in epidemiologic studies, yielding odds ratio or relative risk interpretations. Inspired by the theory of linear normal models, the logistic regression model has been extended to allow for correlated responses by introducing random effects. However......, the model does not inherit the interpretational features of the normal model. In this paper, the authors argue that the existing measures are unsatisfactory (and some of them are even improper) when quantifying results from multilevel logistic regression analyses. The authors suggest a measure...... of heterogeneity, the median odds ratio, that quantifies cluster heterogeneity and facilitates a direct comparison between covariate effects and the magnitude of heterogeneity in terms of well-known odds ratios. Quantifying cluster-level covariates in a meaningful way is a challenge in multilevel logistic...

Robust logistic regression to narrow down the winner's curse for rare and recessive susceptibility variants.

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Kesselmeier, Miriam; Lorenzo Bermejo, Justo

2017-11-01

Logistic regression is the most common technique used for genetic case-control association studies. A disadvantage of standard maximum likelihood estimators of the genotype relative risk (GRR) is their strong dependence on outlier subjects, for example, patients diagnosed at unusually young age. Robust methods are available to constrain outlier influence, but they are scarcely used in genetic studies. This article provides a non-intimidating introduction to robust logistic regression, and investigates its benefits and limitations in genetic association studies. We applied the bounded Huber and extended the R package 'robustbase' with the re-descending Hampel functions to down-weight outlier influence. Computer simulations were carried out to assess the type I error rate, mean squared error (MSE) and statistical power according to major characteristics of the genetic study and investigated markers. Simulations were complemented with the analysis of real data. Both standard and robust estimation controlled type I error rates. Standard logistic regression showed the highest power but standard GRR estimates also showed the largest bias and MSE, in particular for associated rare and recessive variants. For illustration, a recessive variant with a true GRR=6.32 and a minor allele frequency=0.05 investigated in a 1000 case/1000 control study by standard logistic regression resulted in power=0.60 and MSE=16.5. The corresponding figures for Huber-based estimation were power=0.51 and MSE=0.53. Overall, Hampel- and Huber-based GRR estimates did not differ much. Robust logistic regression may represent a valuable alternative to standard maximum likelihood estimation when the focus lies on risk prediction rather than identification of susceptibility variants. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

[Logistic regression model of noninvasive prediction for portal hypertensive gastropathy in patients with hepatitis B associated cirrhosis].

Science.gov (United States)

Wang, Qingliang; Li, Xiaojie; Hu, Kunpeng; Zhao, Kun; Yang, Peisheng; Liu, Bo

2015-05-12

To explore the risk factors of portal hypertensive gastropathy (PHG) in patients with hepatitis B associated cirrhosis and establish a Logistic regression model of noninvasive prediction. The clinical data of 234 hospitalized patients with hepatitis B associated cirrhosis from March 2012 to March 2014 were analyzed retrospectively. The dependent variable was the occurrence of PHG while the independent variables were screened by binary Logistic analysis. Multivariate Logistic regression was used for further analysis of significant noninvasive independent variables. Logistic regression model was established and odds ratio was calculated for each factor. The accuracy, sensitivity and specificity of model were evaluated by the curve of receiver operating characteristic (ROC). According to univariate Logistic regression, the risk factors included hepatic dysfunction, albumin (ALB), bilirubin (TB), prothrombin time (PT), platelet (PLT), white blood cell (WBC), portal vein diameter, spleen index, splenic vein diameter, diameter ratio, PLT to spleen volume ratio, esophageal varices (EV) and gastric varices (GV). Multivariate analysis showed that hepatic dysfunction (X1), TB (X2), PLT (X3) and splenic vein diameter (X4) were the major occurring factors for PHG. The established regression model was Logit P=-2.667+2.186X1-2.167X2+0.725X3+0.976X4. The accuracy of model for PHG was 79.1% with a sensitivity of 77.2% and a specificity of 80.8%. Hepatic dysfunction, TB, PLT and splenic vein diameter are risk factors for PHG and the noninvasive predicted Logistic regression model was Logit P=-2.667+2.186X1-2.167X2+0.725X3+0.976X4.

Multiple Logistic Regression Analysis of Cigarette Use among High School Students

Science.gov (United States)

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…

The intermediate endpoint effect in logistic and probit regression

Science.gov (United States)

MacKinnon, DP; Lockwood, CM; Brown, CH; Wang, W; Hoffman, JM

2010-01-01

Background An intermediate endpoint is hypothesized to be in the middle of the causal sequence relating an independent variable to a dependent variable. The intermediate variable is also called a surrogate or mediating variable and the corresponding effect is called the mediated, surrogate endpoint, or intermediate endpoint effect. Clinical studies are often designed to change an intermediate or surrogate endpoint and through this intermediate change influence the ultimate endpoint. In many intermediate endpoint clinical studies the dependent variable is binary, and logistic or probit regression is used. Purpose The purpose of this study is to describe a limitation of a widely used approach to assessing intermediate endpoint effects and to propose an alternative method, based on products of coefficients, that yields more accurate results. Methods The intermediate endpoint model for a binary outcome is described for a true binary outcome and for a dichotomization of a latent continuous outcome. Plots of true values and a simulation study are used to evaluate the different methods. Results Distorted estimates of the intermediate endpoint effect and incorrect conclusions can result from the application of widely used methods to assess the intermediate endpoint effect. The same problem occurs for the proportion of an effect explained by an intermediate endpoint, which has been suggested as a useful measure for identifying intermediate endpoints. A solution to this problem is given based on the relationship between latent variable modeling and logistic or probit regression. Limitations More complicated intermediate variable models are not addressed in the study, although the methods described in the article can be extended to these more complicated models. Conclusions Researchers are encouraged to use an intermediate endpoint method based on the product of regression coefficients. A common method based on difference in coefficient methods can lead to distorted

Logistic regression against a divergent Bayesian network

Directory of Open Access Journals (Sweden)

Noel Antonio Sánchez Trujillo

2015-01-01

Full Text Available This article is a discussion about two statistical tools used for prediction and causality assessment: logistic regression and Bayesian networks. Using data of a simulated example from a study assessing factors that might predict pulmonary emphysema (where fingertip pigmentation and smoking are considered; we posed the following questions. Is pigmentation a confounding, causal or predictive factor? Is there perhaps another factor, like smoking, that confounds? Is there a synergy between pigmentation and smoking? The results, in terms of prediction, are similar with the two techniques; regarding causation, differences arise. We conclude that, in decision-making, the sum of both: a statistical tool, used with common sense, and previous evidence, taking years or even centuries to develop; is better than the automatic and exclusive use of statistical resources.

Comparison of logistic regression and artificial neural network in low back pain prediction: second national health survey.

Science.gov (United States)

Parsaeian, M; Mohammad, K; Mahmoudi, M; Zeraati, H

2012-01-01

The purpose of this investigation was to compare empirically predictive ability of an artificial neural network with a logistic regression in prediction of low back pain. Data from the second national health survey were considered in this investigation. This data includes the information of low back pain and its associated risk factors among Iranian people aged 15 years and older. Artificial neural network and logistic regression models were developed using a set of 17294 data and they were validated in a test set of 17295 data. Hosmer and Lemeshow recommendation for model selection was used in fitting the logistic regression. A three-layer perceptron with 9 inputs, 3 hidden and 1 output neurons was employed. The efficiency of two models was compared by receiver operating characteristic analysis, root mean square and -2 Loglikelihood criteria. The area under the ROC curve (SE), root mean square and -2Loglikelihood of the logistic regression was 0.752 (0.004), 0.3832 and 14769.2, respectively. The area under the ROC curve (SE), root mean square and -2Loglikelihood of the artificial neural network was 0.754 (0.004), 0.3770 and 14757.6, respectively. Based on these three criteria, artificial neural network would give better performance than logistic regression. Although, the difference is statistically significant, it does not seem to be clinically significant.

Propensity Score Estimation with Data Mining Techniques: Alternatives to Logistic Regression

Science.gov (United States)

Keller, Bryan S. B.; Kim, Jee-Seon; Steiner, Peter M.

2013-01-01

Propensity score analysis (PSA) is a methodological technique which may correct for selection bias in a quasi-experiment by modeling the selection process using observed covariates. Because logistic regression is well understood by researchers in a variety of fields and easy to implement in a number of popular software packages, it has…

Using Logistic Regression To Predict the Probability of Debris Flows Occurring in Areas Recently Burned By Wildland Fires

Science.gov (United States)

Rupert, Michael G.; Cannon, Susan H.; Gartner, Joseph E.

2003-01-01

Logistic regression was used to predict the probability of debris flows occurring in areas recently burned by wildland fires. Multiple logistic regression is conceptually similar to multiple linear regression because statistical relations between one dependent variable and several independent variables are evaluated. In logistic regression, however, the dependent variable is transformed to a binary variable (debris flow did or did not occur), and the actual probability of the debris flow occurring is statistically modeled. Data from 399 basins located within 15 wildland fires that burned during 2000-2002 in Colorado, Idaho, Montana, and New Mexico were evaluated. More than 35 independent variables describing the burn severity, geology, land surface gradient, rainfall, and soil properties were evaluated. The models were developed as follows: (1) Basins that did and did not produce debris flows were delineated from National Elevation Data using a Geographic Information System (GIS). (2) Data describing the burn severity, geology, land surface gradient, rainfall, and soil properties were determined for each basin. These data were then downloaded to a statistics software package for analysis using logistic regression. (3) Relations between the occurrence/non-occurrence of debris flows and burn severity, geology, land surface gradient, rainfall, and soil properties were evaluated and several preliminary multivariate logistic regression models were constructed. All possible combinations of independent variables were evaluated to determine which combination produced the most effective model. The multivariate model that best predicted the occurrence of debris flows was selected. (4) The multivariate logistic regression model was entered into a GIS, and a map showing the probability of debris flows was constructed. The most effective model incorporates the percentage of each basin with slope greater than 30 percent, percentage of land burned at medium and high burn severity

Integrating classification trees with local logistic regression in Intensive Care prognosis.

Science.gov (United States)

Abu-Hanna, Ameen; de Keizer, Nicolette

2003-01-01

Health care effectiveness and efficiency are under constant scrutiny especially when treatment is quite costly as in the Intensive Care (IC). Currently there are various international quality of care programs for the evaluation of IC. At the heart of such quality of care programs lie prognostic models whose prediction of patient mortality can be used as a norm to which actual mortality is compared. The current generation of prognostic models in IC are statistical parametric models based on logistic regression. Given a description of a patient at admission, these models predict the probability of his or her survival. Typically, this patient description relies on an aggregate variable, called a score, that quantifies the severity of illness of the patient. The use of a parametric model and an aggregate score form adequate means to develop models when data is relatively scarce but it introduces the risk of bias. This paper motivates and suggests a method for studying and improving the performance behavior of current state-of-the-art IC prognostic models. Our method is based on machine learning and statistical ideas and relies on exploiting information that underlies a score variable. In particular, this underlying information is used to construct a classification tree whose nodes denote patient sub-populations. For these sub-populations, local models, most notably logistic regression ones, are developed using only the total score variable. We compare the performance of this hybrid model to that of a traditional global logistic regression model. We show that the hybrid model not only provides more insight into the data but also has a better performance. We pay special attention to the precision aspect of model performance and argue why precision is more important than discrimination ability.

The use of logistic regression in modelling the distributions of bird ...

African Journals Online (AJOL)

The method of logistic regression was used to model the observed geographical distribution patterns of bird species in Swaziland in relation to a set of environmental variables. Reporting rates derived from bird atlas data are used as an index of population densities. This is justified in part by the success of the modelling ...

Alcohol Involvement in Homicide Victimization in the U.S

Science.gov (United States)

Naimi, Timothy S.; Xuan, Ziming; Cooper, Susanna E.; Coleman, Sharon M.; Hadland, Scott E.; Swahn, Monica H.; Heeren, Timothy C.

2016-01-01

Background Although the association between alcohol and homicide is well documented, there has been no recent study of alcohol involvement in homicide victimization in U.S. states. The objective of this paper was to determine the prevalence of alcohol involvement in homicide victimization and identify socio-demographic and other factors associated with alcohol involvement in homicide victimization. Methods Data from homicide victims with a reported blood alcohol content (BAC) level were analyzed from 17 states from 2010–12 using the National Violent Death Reporting System. Logistic regression was used to investigate factors associated with the odds of homicide victims having a BAC ≥0.08%. Results Among all homicide victims, 39.9% had a positive BAC including 13.7% with a BAC between 0.01%–0.79% and 26.2% of victims with a BAC ≥0.08%. Males were twice as likely as females to have a BAC ≥0.08% (29.1% vs. 15.2%; p homicide victims having a BAC ≥0.08 included male sex, American Indian/Alaska Native race, Hispanic ethnicity, history of intimate partner violence, and non-firearm homicides. Conclusions Alcohol is present in a substantial proportion of homicide victims in the U.S., with substantial variation by state, demographic and circumstantial characteristics. Future studies should explore the relationships between state-level alcohol policies and alcohol-involvement among perpetrators and victims of homicide. PMID:27676334

Feminism, status inconsistency, and women's intimate partner victimization in heterosexual relationships.

Science.gov (United States)

Franklin, Cortney A; Menaker, Tasha A

2014-07-01

This study used a random community sample of 303 women in romantic relationships to investigate the role of educational and employment status inconsistency and patriarchal family ideology as risk factors for intimate partner violence (IPV) victimization, while considering demographic factors and relationship context variables. Sequential multivariate logistic regression models demonstrated a decrease in the odds of IPV victimization for Hispanic women and women who were older as compared with their counterparts. In addition, increased relationship distress, family-of-origin violence, and employment status inconsistency significantly increased the odds of IPV. Clinical intervention strategies and future research directions are discussed. © The Author(s) 2014.

Building interpretable predictive models for pediatric hospital readmission using Tree-Lasso logistic regression.

Science.gov (United States)

Jovanovic, Milos; Radovanovic, Sandro; Vukicevic, Milan; Van Poucke, Sven; Delibasic, Boris

2016-09-01

Quantification and early identification of unplanned readmission risk have the potential to improve the quality of care during hospitalization and after discharge. However, high dimensionality, sparsity, and class imbalance of electronic health data and the complexity of risk quantification, challenge the development of accurate predictive models. Predictive models require a certain level of interpretability in order to be applicable in real settings and create actionable insights. This paper aims to develop accurate and interpretable predictive models for readmission in a general pediatric patient population, by integrating a data-driven model (sparse logistic regression) and domain knowledge based on the international classification of diseases 9th-revision clinical modification (ICD-9-CM) hierarchy of diseases. Additionally, we propose a way to quantify the interpretability of a model and inspect the stability of alternative solutions. The analysis was conducted on >66,000 pediatric hospital discharge records from California, State Inpatient Databases, Healthcare Cost and Utilization Project between 2009 and 2011. We incorporated domain knowledge based on the ICD-9-CM hierarchy in a data driven, Tree-Lasso regularized logistic regression model, providing the framework for model interpretation. This approach was compared with traditional Lasso logistic regression resulting in models that are easier to interpret by fewer high-level diagnoses, with comparable prediction accuracy. The results revealed that the use of a Tree-Lasso model was as competitive in terms of accuracy (measured by area under the receiver operating characteristic curve-AUC) as the traditional Lasso logistic regression, but integration with the ICD-9-CM hierarchy of diseases provided more interpretable models in terms of high-level diagnoses. Additionally, interpretations of models are in accordance with existing medical understanding of pediatric readmission. Best performing models have

A deeper look at two concepts of measuring gene-gene interactions: logistic regression and interaction information revisited.

Science.gov (United States)

Mielniczuk, Jan; Teisseyre, Paweł

2018-03-01

Detection of gene-gene interactions is one of the most important challenges in genome-wide case-control studies. Besides traditional logistic regression analysis, recently the entropy-based methods attracted a significant attention. Among entropy-based methods, interaction information is one of the most promising measures having many desirable properties. Although both logistic regression and interaction information have been used in several genome-wide association studies, the relationship between them has not been thoroughly investigated theoretically. The present paper attempts to fill this gap. We show that although certain connections between the two methods exist, in general they refer two different concepts of dependence and looking for interactions in those two senses leads to different approaches to interaction detection. We introduce ordering between interaction measures and specify conditions for independent and dependent genes under which interaction information is more discriminative measure than logistic regression. Moreover, we show that for so-called perfect distributions those measures are equivalent. The numerical experiments illustrate the theoretical findings indicating that interaction information and its modified version are more universal tools for detecting various types of interaction than logistic regression and linkage disequilibrium measures. © 2017 WILEY PERIODICALS, INC.

Assessing the performance of variational methods for mixed logistic regression models

Czech Academy of Sciences Publication Activity Database

Rijmen, F.; Vomlel, Jiří

2008-01-01

Roč. 78, č. 8 (2008), s. 765-779 ISSN 0094-9655 R&D Projects: GA MŠk 1M0572 Grant - others:GA MŠk(CZ) 2C06019 Institutional research plan: CEZ:AV0Z10750506 Keywords : Mixed models * Logistic regression * Variational methods * Lower bound approximation Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.353, year: 2008

A review of logistic regression models used to predict post-fire tree mortality of western North American conifers

Science.gov (United States)

Travis Woolley; David C. Shaw; Lisa M. Ganio; Stephen. Fitzgerald

2012-01-01

Logistic regression models used to predict tree mortality are critical to post-fire management, planning prescribed bums and understanding disturbance ecology. We review literature concerning post-fire mortality prediction using logistic regression models for coniferous tree species in the western USA. We include synthesis and review of: methods to develop, evaluate...

Extreme Sparse Multinomial Logistic Regression: A Fast and Robust Framework for Hyperspectral Image Classification

Science.gov (United States)

Cao, Faxian; Yang, Zhijing; Ren, Jinchang; Ling, Wing-Kuen; Zhao, Huimin; Marshall, Stephen

2017-12-01

Although the sparse multinomial logistic regression (SMLR) has provided a useful tool for sparse classification, it suffers from inefficacy in dealing with high dimensional features and manually set initial regressor values. This has significantly constrained its applications for hyperspectral image (HSI) classification. In order to tackle these two drawbacks, an extreme sparse multinomial logistic regression (ESMLR) is proposed for effective classification of HSI. First, the HSI dataset is projected to a new feature space with randomly generated weight and bias. Second, an optimization model is established by the Lagrange multiplier method and the dual principle to automatically determine a good initial regressor for SMLR via minimizing the training error and the regressor value. Furthermore, the extended multi-attribute profiles (EMAPs) are utilized for extracting both the spectral and spatial features. A combinational linear multiple features learning (MFL) method is proposed to further enhance the features extracted by ESMLR and EMAPs. Finally, the logistic regression via the variable splitting and the augmented Lagrangian (LORSAL) is adopted in the proposed framework for reducing the computational time. Experiments are conducted on two well-known HSI datasets, namely the Indian Pines dataset and the Pavia University dataset, which have shown the fast and robust performance of the proposed ESMLR framework.

Sample Size and Robustness of Inferences from Logistic Regression in the Presence of Nonlinearity and Multicollinearity

OpenAIRE

Bergtold, Jason S.; Yeager, Elizabeth A.; Featherstone, Allen M.

2011-01-01

The logistic regression models has been widely used in the social and natural sciences and results from studies using this model can have significant impact. Thus, confidence in the reliability of inferences drawn from these models is essential. The robustness of such inferences is dependent on sample size. The purpose of this study is to examine the impact of sample size on the mean estimated bias and efficiency of parameter estimation and inference for the logistic regression model. A numbe...

Logistic LASSO regression for the diagnosis of breast cancer using clinical demographic data and the BI-RADS lexicon for ultrasonography.

Science.gov (United States)

Kim, Sun Mi; Kim, Yongdai; Jeong, Kuhwan; Jeong, Heeyeong; Kim, Jiyoung

2018-01-01

The aim of this study was to compare the performance of image analysis for predicting breast cancer using two distinct regression models and to evaluate the usefulness of incorporating clinical and demographic data (CDD) into the image analysis in order to improve the diagnosis of breast cancer. This study included 139 solid masses from 139 patients who underwent a ultrasonography-guided core biopsy and had available CDD between June 2009 and April 2010. Three breast radiologists retrospectively reviewed 139 breast masses and described each lesion using the Breast Imaging Reporting and Data System (BI-RADS) lexicon. We applied and compared two regression methods-stepwise logistic (SL) regression and logistic least absolute shrinkage and selection operator (LASSO) regression-in which the BI-RADS descriptors and CDD were used as covariates. We investigated the performances of these regression methods and the agreement of radiologists in terms of test misclassification error and the area under the curve (AUC) of the tests. Logistic LASSO regression was superior (Pcomparable to the AUC with CDD (0.873 vs. 0.880, P=0.141). Logistic LASSO regression based on BI-RADS descriptors and CDD showed better performance than SL in predicting the presence of breast cancer. The use of CDD as a supplement to the BI-RADS descriptors significantly improved the prediction of breast cancer using logistic LASSO regression.

A Note on Three Statistical Tests in the Logistic Regression DIF Procedure

Science.gov (United States)

Paek, Insu

2012-01-01

Although logistic regression became one of the well-known methods in detecting differential item functioning (DIF), its three statistical tests, the Wald, likelihood ratio (LR), and score tests, which are readily available under the maximum likelihood, do not seem to be consistently distinguished in DIF literature. This paper provides a clarifying…

Comparison of IRT Likelihood Ratio Test and Logistic Regression DIF Detection Procedures

Science.gov (United States)

Atar, Burcu; Kamata, Akihito

2011-01-01

The Type I error rates and the power of IRT likelihood ratio test and cumulative logit ordinal logistic regression procedures in detecting differential item functioning (DIF) for polytomously scored items were investigated in this Monte Carlo simulation study. For this purpose, 54 simulation conditions (combinations of 3 sample sizes, 2 sample…

Future Orientation among Students Exposed to School Bullying and Cyberbullying Victimization.

Science.gov (United States)

Låftman, Sara B; Alm, Susanne; Sandahl, Julia; Modin, Bitte

2018-03-27

Future orientation can be defined as an individual's thoughts, beliefs, plans, and hopes for the future. Earlier research has shown adolescents' future orientation to predict outcomes later in life, which makes it relevant to analyze differences in future orientation among youth. The aim of the present study was to analyze if bullying victimization was associated with an increased likelihood of reporting a pessimistic future orientation among school youth. To be able to distinguish between victims and bully-victims (i.e., students who are both bullies and victims), we also took perpetration into account. The data were derived from the Stockholm School Survey performed in 2016 among ninth grade students (ages 15-16 years) ( n = 5144). Future orientation and involvement in school bullying and in cyberbullying were based on self-reports. The statistical method used was binary logistic regression. The results demonstrated that victims and bully-victims of school bullying and of cyberbullying were more likely to report a pessimistic future orientation compared with students not involved in bullying. These associations were shown also when involvement in school bullying and cyberbullying were mutually adjusted. The findings underline the importance of anti-bullying measures that target both school bullying and cyberbullying.

Logistics planning and logistics planning factors for humanitarian operations

OpenAIRE

Sullivan, Donna Marie.

1995-01-01

Due to the increasing demand on the military to conduct humanitarian operations, the need for logistics planning factors that are applicable to these operations has arisen. This thesis develops a model for humanitarian operations and employs the model to develop logistics planning factors for material consumption and a computer-assisted planning aid relating to the support of the victim population. U.S. Navy (U.S.N.) author.

Social anxiety and alcohol-related sexual victimization: A longitudinal pilot study of college women.

Science.gov (United States)

Schry, Amie R; Maddox, Brenna B; White, Susan W

2016-10-01

We sought to examine social anxiety as a risk factor for alcohol-related sexual victimization among college women. Women (Time 1: n = 574; Time 2: n = 88) who reported consuming alcohol at least once during the assessment timeframe participated. Social anxiety, alcohol use, alcohol-related consequences, and sexual victimization were assessed twice, approximately two months apart. Logistic regressions were used to examine social anxiety as a risk factor for alcohol-related sexual victimization at both time points. Longitudinally, women high in social anxiety were approximately three times more likely to endorse unwanted alcohol-related sexual experiences compared to women with low to moderate social anxiety. This study suggests social anxiety, a modifiable construct, increases risk for alcohol-related sexual victimization among college women. Implications for clinicians and risk-reduction program developers are discussed. Published by Elsevier Ltd.

Examining the offender-victim overlap among police officers: the role of social learning and job-related stress.

Science.gov (United States)

Zavala, Egbert

2013-01-01

This study uses data from the Police Stress and Domestic Violence in Police Families in Baltimore, Maryland 1997-1999 to examine the offender-victim overlap among police officers in the context of intimate partner violence (IPV). Specifically, the study examines the role of parental violence, child maltreatment, and job-related stress on perpetrating violence and victimization. Results from two logistic regression models indicate that one element of job-related stress (negative emotions) was positive and significant in predicting IPV perpetration, whereas parental violence, child maltreatment, and negative emotions were found to be positive and significant in predicting victimization. The study's limitations and future research are discussed.

The Influence of Witnessing Inter-parental Violence and Bullying Victimization in Involvement in Fighting among Adolescents: Evidence from a School-based Cross-sectional Survey in Peru.

Science.gov (United States)

Sharma, Bimala; Nam, Eun Woo; Kim, Ha Yun; Kim, Jong Koo

2016-03-01

Witnessing inter-parental violence and bullying victimization is common for many children and adolescents. This study examines the role of witnessing inter-parental violence and bullying victimization in involvement in physical fighting among Peruvian adolescents. A cross-sectional study was conducted among 1,368 randomly selected adolescents in 2015. We conducted logistic regression analyses to obtain crude and adjusted odds ratios with 95% confidence intervals for involvement in fighting among male and female adolescents. Among all adolescents, 35.8% had been involved in fighting in the last 12 months, 32.9% had been victim of verbal bullying and 37.9% had been the victim of physical bullying. Additionally, 39.2% and 27.8% of adolescents witnessed violence against their mother and father, respectively, at least once in their lives. Multivariate logistic regression analyses found that late adolescence, participation in economic activities, being the victim of verbal bullying, stress, and witnessing violence against the father among male adolescents, and self-rated academic performance and being the victim of physical or verbal bullying among female adolescents were associated with higher odds of being involved in fighting. Verbal bullying victimization and witnessing violence against the father in males and bullying victimization in females were associated with greater odds of adolescents being involved in fighting. Creating a non-violent environment at both home and school would be an effective strategy for reducing fighting among the adolescent population.

Detecting DIF in Polytomous Items Using MACS, IRT and Ordinal Logistic Regression

Science.gov (United States)

Elosua, Paula; Wells, Craig

2013-01-01

The purpose of the present study was to compare the Type I error rate and power of two model-based procedures, the mean and covariance structure model (MACS) and the item response theory (IRT), and an observed-score based procedure, ordinal logistic regression, for detecting differential item functioning (DIF) in polytomous items. A simulation…

Methods for identifying SNP interactions: a review on variations of Logic Regression, Random Forest and Bayesian logistic regression.

Science.gov (United States)

Chen, Carla Chia-Ming; Schwender, Holger; Keith, Jonathan; Nunkesser, Robin; Mengersen, Kerrie; Macrossan, Paula

2011-01-01

Due to advancements in computational ability, enhanced technology and a reduction in the price of genotyping, more data are being generated for understanding genetic associations with diseases and disorders. However, with the availability of large data sets comes the inherent challenges of new methods of statistical analysis and modeling. Considering a complex phenotype may be the effect of a combination of multiple loci, various statistical methods have been developed for identifying genetic epistasis effects. Among these methods, logic regression (LR) is an intriguing approach incorporating tree-like structures. Various methods have built on the original LR to improve different aspects of the model. In this study, we review four variations of LR, namely Logic Feature Selection, Monte Carlo Logic Regression, Genetic Programming for Association Studies, and Modified Logic Regression-Gene Expression Programming, and investigate the performance of each method using simulated and real genotype data. We contrast these with another tree-like approach, namely Random Forests, and a Bayesian logistic regression with stochastic search variable selection.

Detecting nonsense for Chinese comments based on logistic regression

Science.gov (United States)

Zhuolin, Ren; Guang, Chen; Shu, Chen

2016-07-01

To understand cyber citizens' opinion accurately from Chinese news comments, the clear definition on nonsense is present, and a detection model based on logistic regression (LR) is proposed. The detection of nonsense can be treated as a binary-classification problem. Besides of traditional lexical features, we propose three kinds of features in terms of emotion, structure and relevance. By these features, we train an LR model and demonstrate its effect in understanding Chinese news comments. We find that each of proposed features can significantly promote the result. In our experiments, we achieve a prediction accuracy of 84.3% which improves the baseline 77.3% by 7%.

Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia

Science.gov (United States)

Pradhan, Biswajeet

2010-05-01

This paper presents the results of the cross-validation of a multivariate logistic regression model using remote sensing data and GIS for landslide hazard analysis on the Penang, Cameron, and Selangor areas in Malaysia. Landslide locations in the study areas were identified by interpreting aerial photographs and satellite images, supported by field surveys. SPOT 5 and Landsat TM satellite imagery were used to map landcover and vegetation index, respectively. Maps of topography, soil type, lineaments and land cover were constructed from the spatial datasets. Ten factors which influence landslide occurrence, i.e., slope, aspect, curvature, distance from drainage, lithology, distance from lineaments, soil type, landcover, rainfall precipitation, and normalized difference vegetation index (ndvi), were extracted from the spatial database and the logistic regression coefficient of each factor was computed. Then the landslide hazard was analysed using the multivariate logistic regression coefficients derived not only from the data for the respective area but also using the logistic regression coefficients calculated from each of the other two areas (nine hazard maps in all) as a cross-validation of the model. For verification of the model, the results of the analyses were then compared with the field-verified landslide locations. Among the three cases of the application of logistic regression coefficient in the same study area, the case of Selangor based on the Selangor logistic regression coefficients showed the highest accuracy (94%), where as Penang based on the Penang coefficients showed the lowest accuracy (86%). Similarly, among the six cases from the cross application of logistic regression coefficient in other two areas, the case of Selangor based on logistic coefficient of Cameron showed highest (90%) prediction accuracy where as the case of Penang based on the Selangor logistic regression coefficients showed the lowest accuracy (79%). Qualitatively, the cross

Fitting multistate transition models with autoregressive logistic regression : Supervised exercise in intermittent claudication

NARCIS (Netherlands)

de Vries, S O; Fidler, Vaclav; Kuipers, Wietze D; Hunink, Maria G M

1998-01-01

The purpose of this study was to develop a model that predicts the outcome of supervised exercise for intermittent claudication. The authors present an example of the use of autoregressive logistic regression for modeling observed longitudinal data. Data were collected from 329 participants in a

Classification of Effective Soil Depth by Using Multinomial Logistic Regression Analysis

Science.gov (United States)

Chang, C. H.; Chan, H. C.; Chen, B. A.

2016-12-01

Classification of effective soil depth is a task of determining the slopeland utilizable limitation in Taiwan. The "Slopeland Conservation and Utilization Act" categorizes the slopeland into agriculture and husbandry land, land suitable for forestry and land for enhanced conservation according to the factors including average slope, effective soil depth, soil erosion and parental rock. However, sit investigation of the effective soil depth requires a cost-effective field work. This research aimed to classify the effective soil depth by using multinomial logistic regression with the environmental factors. The Wen-Shui Watershed located at the central Taiwan was selected as the study areas. The analysis of multinomial logistic regression is performed by the assistance of a Geographic Information Systems (GIS). The effective soil depth was categorized into four levels including deeper, deep, shallow and shallower. The environmental factors of slope, aspect, digital elevation model (DEM), curvature and normalized difference vegetation index (NDVI) were selected for classifying the soil depth. An Error Matrix was then used to assess the model accuracy. The results showed an overall accuracy of 75%. At the end, a map of effective soil depth was produced to help planners and decision makers in determining the slopeland utilizable limitation in the study areas.

Risk and Protective Factors Associated to Peer School Victimization.

Science.gov (United States)

Méndez, Inmaculada; Ruiz-Esteban, Cecilia; López-García, J J

2017-01-01

The main objective of this study is to analyze the relationship between peer school victimization and some risk and protection factors and to compare the differences by role in victimization with those of non-involved bystanders. Our participants were 1,264 secondary students ( M = 14.41, SD = 1.43) who participated voluntarily, although an informed consent was requested. A logistic regression model (LR) was used in order to identify the victim's potential risks and protective factors related to non-involved bystanders. A multiple LR and a forward stepwise LR (Wald) were used. The results showed the variables related to the victim profile were: individual features (to be male, to be at the first cycle of compulsory Secondary Education and a few challenging behaviors), school environments (i.e., school adjustment), family environment (parental styles like authoritarianism) and social environment (i.e., friends who occasionally show a positive attitude toward drug consumption and easy access to drugs, access to drugs perceived as easy, rejection by peers or lack of social acceptance and social maladjustment). The results of the study will allow tackling prevention and intervention actions in schools, families, and social environment in order to improve coexistence at school and to assist the victimized students in the classroom.

Skewed sex ratios and criminal victimization in India.

Science.gov (United States)

South, Scott J; Trent, Katherine; Bose, Sunita

2014-06-01

Although substantial research has explored the causes of India's excessively masculine population sex ratio, few studies have examined the consequences of this surplus of males. We merge individual-level data from the 2004-2005 India Human Development Survey with data from the 2001 India population census to examine the association between the district-level male-to-female sex ratio at ages 15 to 39 and self-reports of victimization by theft, breaking and entering, and assault. Multilevel logistic regression analyses reveal positive and statistically significant albeit substantively modest effects of the district-level sex ratio on all three victimization risks. We also find that higher male-to-female sex ratios are associated with the perception that young unmarried women in the local community are frequently harassed. Household-level indicators of family structure, socioeconomic status, and caste, as well as areal indicators of women's empowerment and collective efficacy, also emerge as significant predictors of self-reported criminal victimization and the perceived harassment of young women. The implications of these findings for India's growing sex ratio imbalance are discussed.

Skewed Sex Ratios and Criminal Victimization in India

Science.gov (United States)

South, Scott J.; Trent, Katherine; Bose, Sunita

2014-01-01

Although substantial research has explored the causes of India’s excessively masculine population sex ratio, few studies have examined the consequences of this surplus of males. We merge individual-level data from the 2004–2005 India Human Development Survey with data from the 2001 India population census to examine the association between the district-level male-to-female sex ratio at ages 15 to 39 and self-reports of victimization by theft, breaking and entering, and assault. Multilevel logistic regression analyses reveal positive and statistically significant albeit substantively modest effects of the district-level sex ratio on all three victimization risks. We also find that higher male-to-female sex ratios are associated with the perception that young unmarried women in the local community are frequently harassed. Household-level indicators of family structure, socioeconomic status, and caste, as well as areal indicators of women’s empowerment and collective efficacy, also emerge as significant predictors of self-reported criminal victimization and the perceived harassment of young women. The implications of these findings for India’s growing sex ratio imbalance are discussed. PMID:24682921

Detection of high GS risk group prostate tumors by diffusion tensor imaging and logistic regression modelling.

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Ertas, Gokhan

2018-07-01

To assess the value of joint evaluation of diffusion tensor imaging (DTI) measures by using logistic regression modelling to detect high GS risk group prostate tumors. Fifty tumors imaged using DTI on a 3 T MRI device were analyzed. Regions of interests focusing on the center of tumor foci and noncancerous tissue on the maps of mean diffusivity (MD) and fractional anisotropy (FA) were used to extract the minimum, the maximum and the mean measures. Measure ratio was computed by dividing tumor measure by noncancerous tissue measure. Logistic regression models were fitted for all possible pair combinations of the measures using 5-fold cross validation. Systematic differences are present for all MD measures and also for all FA measures in distinguishing the high risk tumors [GS ≥ 7(4 + 3)] from the low risk tumors [GS ≤ 7(3 + 4)] (P Logistic regression modelling provides a favorable solution for the joint evaluations easily adoptable in clinical practice. Copyright © 2018 Elsevier Inc. All rights reserved.

A Novel Imbalanced Data Classification Approach Based on Logistic Regression and Fisher Discriminant

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Baofeng Shi

2015-01-01

Full Text Available We introduce an imbalanced data classification approach based on logistic regression significant discriminant and Fisher discriminant. First of all, a key indicators extraction model based on logistic regression significant discriminant and correlation analysis is derived to extract features for customer classification. Secondly, on the basis of the linear weighted utilizing Fisher discriminant, a customer scoring model is established. And then, a customer rating model where the customer number of all ratings follows normal distribution is constructed. The performance of the proposed model and the classical SVM classification method are evaluated in terms of their ability to correctly classify consumers as default customer or nondefault customer. Empirical results using the data of 2157 customers in financial engineering suggest that the proposed approach better performance than the SVM model in dealing with imbalanced data classification. Moreover, our approach contributes to locating the qualified customers for the banks and the bond investors.

Screening for ketosis using multiple logistic regression based on milk yield and composition.

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Kayano, Mitsunori; Kataoka, Tomoko

2015-11-01

Multiple logistic regression was applied to milk yield and composition data for 632 records of healthy cows and 61 records of ketotic cows in Hokkaido, Japan. The purpose was to diagnose ketosis based on milk yield and composition, simultaneously. The cows were divided into two groups: (1) multiparous, including 314 healthy cows and 45 ketotic cows and (2) primiparous, including 318 healthy cows and 16 ketotic cows, since nutritional status, milk yield and composition are affected by parity. Multiple logistic regression was applied to these groups separately. For multiparous cows, milk yield (kg/day/cow) and protein-to-fat (P/F) ratio in milk were significant factors (Pketosis. For primiparous cows, lactose content (%), solid not fat (SNF) content (%) and milk urea nitrogen (MUN) content (mg/dl) were significantly associated with ketosis (Pketosis, provided the sensitivity, specificity and AUC values of (1) 0.711, 0.726 and 0.781; and (2) 0.678, 0.767 and 0.738, respectively.

A Predictive Logistic Regression Model of World Conflict Using Open Source Data

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2015-03-26

No correlation between the error terms and the independent variables 9. Absence of perfect multicollinearity (Menard, 2001) When assumptions are...some of the variables before initial model building. Multicollinearity , or near-linear dependence among the variables will cause problems in the...model. High multicollinearity tends to produce unreasonably high logistic regression coefficients and can result in coefficients that are not

Landslide susceptibility mapping using logistic statistical regression in Babaheydar Watershed, Chaharmahal Va Bakhtiari Province, Iran

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Ebrahim Karimi Sangchini

2015-01-01

Full Text Available Landslides are amongst the most damaging natural hazards in mountainous regions. Every year, hundreds of people all over the world lose their lives in landslides; furthermore, there are large impacts on the local and global economy from these events. In this study, landslide hazard zonation in Babaheydar watershed using logistic regression was conducted to determine landslide hazard areas. At first, the landslide inventory map was prepared using aerial photograph interpretations and field surveys. The next step, ten landslide conditioning factors such as altitude, slope percentage, slope aspect, lithology, distance from faults, rivers, settlement and roads, land use, and precipitation were chosen as effective factors on landsliding in the study area. Subsequently, landslide susceptibility map was constructed using the logistic regression model in Geographic Information System (GIS. The ROC and Pseudo-R2 indexes were used for model assessment. Results showed that the logistic regression model provided slightly high prediction accuracy of landslide susceptibility maps in the Babaheydar Watershed with ROC equal to 0.876. Furthermore, the results revealed that about 44% of the watershed areas were located in high and very high hazard classes. The resultant landslide susceptibility maps can be useful in appropriate watershed management practices and for sustainable development in the region.

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.

Online gaming and risks predict cyberbullying perpetration and victimization in adolescents.

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Chang, Fong-Ching; Chiu, Chiung-Hui; Miao, Nae-Fang; Chen, Ping-Hung; Lee, Ching-Mei; Huang, Tzu-Fu; Pan, Yun-Chieh

2015-02-01

The present study examined factors associated with the emergence and cessation of youth cyberbullying and victimization in Taiwan. A total of 2,315 students from 26 high schools were assessed in the 10th grade, with follow-up performed in the 11th grade. Self-administered questionnaires were collected in 2010 and 2011. Multiple logistic regression was conducted to examine the factors. Multivariate analysis results indicated that higher levels of risk factors (online game use, exposure to violence in media, internet risk behaviors, cyber/school bullying experiences) in the 10th grade coupled with an increase in risk factors from grades 10 to 11 could be used to predict the emergence of cyberbullying perpetration/victimization. In contrast, lower levels of risk factors in the 10th grade and higher levels of protective factors coupled with a decrease in risk factors predicted the cessation of cyberbullying perpetration/victimization. Online game use, exposure to violence in media, Internet risk behaviors, and cyber/school bullying experiences can be used to predict the emergence and cessation of youth cyberbullying perpetration and victimization.

Future Orientation among Students Exposed to School Bullying and Cyberbullying Victimization

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Sara B. Låftman

2018-03-01

Full Text Available Future orientation can be defined as an individual’s thoughts, beliefs, plans, and hopes for the future. Earlier research has shown adolescents’ future orientation to predict outcomes later in life, which makes it relevant to analyze differences in future orientation among youth. The aim of the present study was to analyze if bullying victimization was associated with an increased likelihood of reporting a pessimistic future orientation among school youth. To be able to distinguish between victims and bully-victims (i.e., students who are both bullies and victims, we also took perpetration into account. The data were derived from the Stockholm School Survey performed in 2016 among ninth grade students (ages 15–16 years (n = 5144. Future orientation and involvement in school bullying and in cyberbullying were based on self-reports. The statistical method used was binary logistic regression. The results demonstrated that victims and bully-victims of school bullying and of cyberbullying were more likely to report a pessimistic future orientation compared with students not involved in bullying. These associations were shown also when involvement in school bullying and cyberbullying were mutually adjusted. The findings underline the importance of anti-bullying measures that target both school bullying and cyberbullying.

Model-based bootstrapping when correcting for measurement error with application to logistic regression.

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Buonaccorsi, John P; Romeo, Giovanni; Thoresen, Magne

2018-03-01

When fitting regression models, measurement error in any of the predictors typically leads to biased coefficients and incorrect inferences. A plethora of methods have been proposed to correct for this. Obtaining standard errors and confidence intervals using the corrected estimators can be challenging and, in addition, there is concern about remaining bias in the corrected estimators. The bootstrap, which is one option to address these problems, has received limited attention in this context. It has usually been employed by simply resampling observations, which, while suitable in some situations, is not always formally justified. In addition, the simple bootstrap does not allow for estimating bias in non-linear models, including logistic regression. Model-based bootstrapping, which can potentially estimate bias in addition to being robust to the original sampling or whether the measurement error variance is constant or not, has received limited attention. However, it faces challenges that are not present in handling regression models with no measurement error. This article develops new methods for model-based bootstrapping when correcting for measurement error in logistic regression with replicate measures. The methodology is illustrated using two examples, and a series of simulations are carried out to assess and compare the simple and model-based bootstrap methods, as well as other standard methods. While not always perfect, the model-based approaches offer some distinct improvements over the other methods. © 2017, The International Biometric Society.

Comparing Linear Discriminant Function with Logistic Regression for the Two-Group Classification Problem.

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Fan, Xitao; Wang, Lin

The Monte Carlo study compared the performance of predictive discriminant analysis (PDA) and that of logistic regression (LR) for the two-group classification problem. Prior probabilities were used for classification, but the cost of misclassification was assumed to be equal. The study used a fully crossed three-factor experimental design (with…

Accuracy of Bayes and Logistic Regression Subscale Probabilities for Educational and Certification Tests

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Rudner, Lawrence

2016-01-01

In the machine learning literature, it is commonly accepted as fact that as calibration sample sizes increase, Naïve Bayes classifiers initially outperform Logistic Regression classifiers in terms of classification accuracy. Applied to subtests from an on-line final examination and from a highly regarded certification examination, this study shows…

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

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

2012-01-01

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

Trends in bullying victimization in Scottish adolescents 1994-2014: changing associations with mental well-being.

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Cosma, Alina; Whitehead, Ross; Neville, Fergus; Currie, Dorothy; Inchley, Jo

2017-07-01

Bullying victimization among schoolchildren is a major public health concern. This paper aims to analyse the changing associations over two decades between bullying victimization and mental well-being in a representative Scottish schoolchildren sample. Data were collected in six rounds of the cross-sectional Health Behaviour in School-aged Children study in Scotland, with 42,312 adolescents (aged 11, 13 and 15 years). Logistic and linear regressions were used to examine changes in the association between bullying victimization and mental well-being. The prevalence of bullying victimization rates in Scotland increased between 1994 and 2014 for most age-gender groups, apart from 13-year-old boys and 15-year-old girls. Over time, female victims reported less confidence and happiness and more psychological complaints than their non-bullied counterparts. This worsening effect over time was not observed in boys. Overall, our evidence indicates that the associations between bullying victimization and poor mental well-being strengthened overtime for bullied girls. This finding might partly explain the observed deterioration in mental health indicators among Scottish adolescent girls.

Risk Factors for Social Networking Site Scam Victimization Among Malaysian Students.

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Kirwan, Gráinne H; Fullwood, Chris; Rooney, Brendan

2018-02-01

Social networking sites (SNSs) can provide cybercriminals with various opportunities, including gathering of user data and login credentials to enable fraud, and directing of users toward online locations that may install malware onto their devices. The techniques employed by such cybercriminals can include clickbait (text or video), advertisement of nonexistent but potentially desirable products, and hoax competitions/giveaways. This study aimed to identify risk factors associated with falling victim to these malicious techniques. An online survey was completed by 295 Malaysian undergraduate students, finding that more than one-third had fallen victim to SNS scams. Logistic regression analysis identified several victimization risk factors including having higher scores in impulsivity (specifically cognitive complexity), using fewer devices for SNSs, and having been on an SNS for a longer duration. No reliable model was found for vulnerability to hoax valuable gift giveaways and "friend view application" advertising specifically, but vulnerability to video clickbait was predicted by lower extraversion scores, higher levels of openness to experience, using fewer devices, and being on an SNS for a longer duration. Other personality traits were not associated with either overall victimization susceptibility or increased risk of falling victim to the specific techniques. However, age approached significance within both the video clickbait and overall victimization models. These findings suggest that routine activity theory may be particularly beneficial in understanding and preventing SNSs scam victimization.

Inverse estimation of multiple muscle activations based on linear logistic regression.

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Sekiya, Masashi; Tsuji, Toshiaki

2017-07-01

This study deals with a technology to estimate the muscle activity from the movement data using a statistical model. A linear regression (LR) model and artificial neural networks (ANN) have been known as statistical models for such use. Although ANN has a high estimation capability, it is often in the clinical application that the lack of data amount leads to performance deterioration. On the other hand, the LR model has a limitation in generalization performance. We therefore propose a muscle activity estimation method to improve the generalization performance through the use of linear logistic regression model. The proposed method was compared with the LR model and ANN in the verification experiment with 7 participants. As a result, the proposed method showed better generalization performance than the conventional methods in various tasks.

The non-condition logistic regression analysis of the reason of hypothyroidism after hyperthyroidism with 131I treatment

International Nuclear Information System (INIS)

Dang Yaping; Hu Guoying; Meng Xianwen

1994-01-01

There are many opinions on the reason of hypothyroidism after hyperthyroidism with 131 I treatment. In this respect, there are a few scientific analyses and reports. The non-condition logistic regression solved this problem successfully. It has a higher scientific value and confidence in the risk factor analysis. 748 follow-up patients' data were analysed by the non-condition logistic regression. The results shown that the half-life and 131 I dose were the main causes of the incidence of hypothyroidism. The degree of confidence is 92.4%

Determination of osteoporosis risk factors using a multiple logistic regression model in postmenopausal Turkish women.

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Akkus, Zeki; Camdeviren, Handan; Celik, Fatma; Gur, Ali; Nas, Kemal

2005-09-01

To determine the risk factors of osteoporosis using a multiple binary logistic regression method and to assess the risk variables for osteoporosis, which is a major and growing health problem in many countries. We presented a case-control study, consisting of 126 postmenopausal healthy women as control group and 225 postmenopausal osteoporotic women as the case group. The study was carried out in the Department of Physical Medicine and Rehabilitation, Dicle University, Diyarbakir, Turkey between 1999-2002. The data from the 351 participants were collected using a standard questionnaire that contains 43 variables. A multiple logistic regression model was then used to evaluate the data and to find the best regression model. We classified 80.1% (281/351) of the participants using the regression model. Furthermore, the specificity value of the model was 67% (84/126) of the control group while the sensitivity value was 88% (197/225) of the case group. We found the distribution of residual values standardized for final model to be exponential using the Kolmogorow-Smirnow test (p=0.193). The receiver operating characteristic curve was found successful to predict patients with risk for osteoporosis. This study suggests that low levels of dietary calcium intake, physical activity, education, and longer duration of menopause are independent predictors of the risk of low bone density in our population. Adequate dietary calcium intake in combination with maintaining a daily physical activity, increasing educational level, decreasing birth rate, and duration of breast-feeding may contribute to healthy bones and play a role in practical prevention of osteoporosis in Southeast Anatolia. In addition, the findings of the present study indicate that the use of multivariate statistical method as a multiple logistic regression in osteoporosis, which maybe influenced by many variables, is better than univariate statistical evaluation.

Use of multiple linear regression and logistic regression models to investigate changes in birthweight for term singleton infants in Scotland.

Science.gov (United States)

Bonellie, Sandra R

2012-10-01

To illustrate the use of regression and logistic regression models to investigate changes over time in size of babies particularly in relation to social deprivation, age of the mother and smoking. Mean birthweight has been found to be increasing in many countries in recent years, but there are still a group of babies who are born with low birthweights. Population-based retrospective cohort study. Multiple linear regression and logistic regression models are used to analyse data on term 'singleton births' from Scottish hospitals between 1994-2003. Mothers who smoke are shown to give birth to lighter babies on average, a difference of approximately 0.57 Standard deviations lower (95% confidence interval. 0.55-0.58) when adjusted for sex and parity. These mothers are also more likely to have babies that are low birthweight (odds ratio 3.46, 95% confidence interval 3.30-3.63) compared with non-smokers. Low birthweight is 30% more likely where the mother lives in the most deprived areas compared with the least deprived, (odds ratio 1.30, 95% confidence interval 1.21-1.40). Smoking during pregnancy is shown to have a detrimental effect on the size of infants at birth. This effect explains some, though not all, of the observed socioeconomic birthweight. It also explains much of the observed birthweight differences by the age of the mother.   Identifying mothers at greater risk of having a low birthweight baby as important implications for the care and advice this group receives. © 2012 Blackwell Publishing Ltd.

Evaluating penalized logistic regression models to predict Heat-Related Electric grid stress days

Energy Technology Data Exchange (ETDEWEB)

Bramer, L. M.; Rounds, J.; Burleyson, C. D.; Fortin, D.; Hathaway, J.; Rice, J.; Kraucunas, I.

2017-11-01

Understanding the conditions associated with stress on the electricity grid is important in the development of contingency plans for maintaining reliability during periods when the grid is stressed. In this paper, heat-related grid stress and the relationship with weather conditions is examined using data from the eastern United States. Penalized logistic regression models were developed and applied to predict stress on the electric grid using weather data. The inclusion of other weather variables, such as precipitation, in addition to temperature improved model performance. Several candidate models and datasets were examined. A penalized logistic regression model fit at the operation-zone level was found to provide predictive value and interpretability. Additionally, the importance of different weather variables observed at different time scales were examined. Maximum temperature and precipitation were identified as important across all zones while the importance of other weather variables was zone specific. The methods presented in this work are extensible to other regions and can be used to aid in planning and development of the electrical grid.

Predictive market segmentation model: An application of logistic regression model and CHAID procedure

Directory of Open Access Journals (Sweden)

Soldić-Aleksić Jasna

2009-01-01

Full Text Available Market segmentation presents one of the key concepts of the modern marketing. The main goal of market segmentation is focused on creating groups (segments of customers that have similar characteristics, needs, wishes and/or similar behavior regarding the purchase of concrete product/service. Companies can create specific marketing plan for each of these segments and therefore gain short or long term competitive advantage on the market. Depending on the concrete marketing goal, different segmentation schemes and techniques may be applied. This paper presents a predictive market segmentation model based on the application of logistic regression model and CHAID analysis. The logistic regression model was used for the purpose of variables selection (from the initial pool of eleven variables which are statistically significant for explaining the dependent variable. Selected variables were afterwards included in the CHAID procedure that generated the predictive market segmentation model. The model results are presented on the concrete empirical example in the following form: summary model results, CHAID tree, Gain chart, Index chart, risk and classification tables.

Using Multiple and Logistic Regression to Estimate the Median WillCost and Probability of Cost and Schedule Overrun for Program Managers

Science.gov (United States)

2017-03-23

Logistic Regression to Estimate the Median Will-Cost and Probability of Cost and Schedule Overrun for Program Managers Ryan C. Trudelle, B.S...not the other. We are able to give logistic regression models to program managers that identify several program characteristics for either...considered acceptable. We recommend the use of our logistic models as a tool to manage a portfolio of programs in order to gain potential elusive

A Generalized Logistic Regression Procedure to Detect Differential Item Functioning among Multiple Groups

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Magis, David; Raiche, Gilles; Beland, Sebastien; Gerard, Paul

2011-01-01

We present an extension of the logistic regression procedure to identify dichotomous differential item functioning (DIF) in the presence of more than two groups of respondents. Starting from the usual framework of a single focal group, we propose a general approach to estimate the item response functions in each group and to test for the presence…

Risk Factors of Falls in Community-Dwelling Older Adults: Logistic Regression Tree Analysis

Science.gov (United States)

Yamashita, Takashi; Noe, Douglas A.; Bailer, A. John

2012-01-01

Purpose of the Study: A novel logistic regression tree-based method was applied to identify fall risk factors and possible interaction effects of those risk factors. Design and Methods: A nationally representative sample of American older adults aged 65 years and older (N = 9,592) in the Health and Retirement Study 2004 and 2006 modules was used.…

Logistic Regression and Path Analysis Method to Analyze Factors influencing Students’ Achievement

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Noeryanti, N.; Suryowati, K.; Setyawan, Y.; Aulia, R. R.

2018-04-01

Students' academic achievement cannot be separated from the influence of two factors namely internal and external factors. The first factors of the student (internal factors) consist of intelligence (X1), health (X2), interest (X3), and motivation of students (X4). The external factors consist of family environment (X5), school environment (X6), and society environment (X7). The objects of this research are eighth grade students of the school year 2016/2017 at SMPN 1 Jiwan Madiun sampled by using simple random sampling. Primary data are obtained by distributing questionnaires. The method used in this study is binary logistic regression analysis that aims to identify internal and external factors that affect student’s achievement and how the trends of them. Path Analysis was used to determine the factors that influence directly, indirectly or totally on student’s achievement. Based on the results of binary logistic regression, variables that affect student’s achievement are interest and motivation. And based on the results obtained by path analysis, factors that have a direct impact on student’s achievement are students’ interest (59%) and students’ motivation (27%). While the factors that have indirect influences on students’ achievement, are family environment (97%) and school environment (37).

[Calculating Pearson residual in logistic regressions: a comparison between SPSS and SAS].

Science.gov (United States)

Xu, Hao; Zhang, Tao; Li, Xiao-song; Liu, Yuan-yuan

2015-01-01

To compare the results of Pearson residual calculations in logistic regression models using SPSS and SAS. We reviewed Pearson residual calculation methods, and used two sets of data to test logistic models constructed by SPSS and STATA. One model contained a small number of covariates compared to the number of observed. The other contained a similar number of covariates as the number of observed. The two software packages produced similar Pearson residual estimates when the models contained a similar number of covariates as the number of observed, but the results differed when the number of observed was much greater than the number of covariates. The two software packages produce different results of Pearson residuals, especially when the models contain a small number of covariates. Further studies are warranted.

Logistic LASSO regression for the diagnosis of breast cancer using clinical demographic data and the BI-RADS lexicon for ultrasonography

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Sun Mi Kim

2018-01-01

Full Text Available Purpose The aim of this study was to compare the performance of image analysis for predicting breast cancer using two distinct regression models and to evaluate the usefulness of incorporating clinical and demographic data (CDD into the image analysis in order to improve the diagnosis of breast cancer. Methods This study included 139 solid masses from 139 patients who underwent a ultrasonography-guided core biopsy and had available CDD between June 2009 and April 2010. Three breast radiologists retrospectively reviewed 139 breast masses and described each lesion using the Breast Imaging Reporting and Data System (BI-RADS lexicon. We applied and compared two regression methods-stepwise logistic (SL regression and logistic least absolute shrinkage and selection operator (LASSO regression-in which the BI-RADS descriptors and CDD were used as covariates. We investigated the performances of these regression methods and the agreement of radiologists in terms of test misclassification error and the area under the curve (AUC of the tests. Results Logistic LASSO regression was superior (P<0.05 to SL regression, regardless of whether CDD was included in the covariates, in terms of test misclassification errors (0.234 vs. 0.253, without CDD; 0.196 vs. 0.258, with CDD and AUC (0.785 vs. 0.759, without CDD; 0.873 vs. 0.735, with CDD. However, it was inferior (P<0.05 to the agreement of three radiologists in terms of test misclassification errors (0.234 vs. 0.168, without CDD; 0.196 vs. 0.088, with CDD and the AUC without CDD (0.785 vs. 0.844, P<0.001, but was comparable to the AUC with CDD (0.873 vs. 0.880, P=0.141. Conclusion Logistic LASSO regression based on BI-RADS descriptors and CDD showed better performance than SL in predicting the presence of breast cancer. The use of CDD as a supplement to the BI-RADS descriptors significantly improved the prediction of breast cancer using logistic LASSO regression.

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

Science.gov (United States)

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

2014-01-01

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

Semi-parametric estimation of random effects in a logistic regression model using conditional inference

DEFF Research Database (Denmark)

Petersen, Jørgen Holm

2016-01-01

This paper describes a new approach to the estimation in a logistic regression model with two crossed random effects where special interest is in estimating the variance of one of the effects while not making distributional assumptions about the other effect. A composite likelihood is studied...

Multinomial logistic regression modelling of obesity and overweight among primary school students in a rural area of Negeri Sembilan

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Ghazali, Amirul Syafiq Mohd; Ali, Zalila; Noor, Norlida Mohd; Baharum, Adam [Pusat Pengajian Sains Matematik, Universiti Sains Malaysia, 11800 USM, Pulau Pinang, Malaysia amirul@unisel.edu.my, zalila@cs.usm.my, norlida@usm.my, adam@usm.my (Malaysia)

2015-10-22

Multinomial logistic regression is widely used to model the outcomes of a polytomous response variable, a categorical dependent variable with more than two categories. The model assumes that the conditional mean of the dependent categorical variables is the logistic function of an affine combination of predictor variables. Its procedure gives a number of logistic regression models that make specific comparisons of the response categories. When there are q categories of the response variable, the model consists of q-1 logit equations which are fitted simultaneously. The model is validated by variable selection procedures, tests of regression coefficients, a significant test of the overall model, goodness-of-fit measures, and validation of predicted probabilities using odds ratio. This study used the multinomial logistic regression model to investigate obesity and overweight among primary school students in a rural area on the basis of their demographic profiles, lifestyles and on the diet and food intake. The results indicated that obesity and overweight of students are related to gender, religion, sleep duration, time spent on electronic games, breakfast intake in a week, with whom meals are taken, protein intake, and also, the interaction between breakfast intake in a week with sleep duration, and the interaction between gender and protein intake.

Multinomial logistic regression modelling of obesity and overweight among primary school students in a rural area of Negeri Sembilan

International Nuclear Information System (INIS)

Ghazali, Amirul Syafiq Mohd; Ali, Zalila; Noor, Norlida Mohd; Baharum, Adam

2015-01-01

Multinomial logistic regression is widely used to model the outcomes of a polytomous response variable, a categorical dependent variable with more than two categories. The model assumes that the conditional mean of the dependent categorical variables is the logistic function of an affine combination of predictor variables. Its procedure gives a number of logistic regression models that make specific comparisons of the response categories. When there are q categories of the response variable, the model consists of q-1 logit equations which are fitted simultaneously. The model is validated by variable selection procedures, tests of regression coefficients, a significant test of the overall model, goodness-of-fit measures, and validation of predicted probabilities using odds ratio. This study used the multinomial logistic regression model to investigate obesity and overweight among primary school students in a rural area on the basis of their demographic profiles, lifestyles and on the diet and food intake. The results indicated that obesity and overweight of students are related to gender, religion, sleep duration, time spent on electronic games, breakfast intake in a week, with whom meals are taken, protein intake, and also, the interaction between breakfast intake in a week with sleep duration, and the interaction between gender and protein intake

A brief conceptual tutorial of multilevel analysis in social epidemiology: using measures of clustering in multilevel logistic regression to investigate contextual phenomena

DEFF Research Database (Denmark)

Merlo, J; Chaix, B; Ohlsson, H

2006-01-01

STUDY OBJECTIVE: In social epidemiology, it is easy to compute and interpret measures of variation in multilevel linear regression, but technical difficulties exist in the case of logistic regression. The aim of this study was to present measures of variation appropriate for the logistic case...... in a didactic rather than a mathematical way. Design and PARTICIPANTS: Data were used from the health survey conducted in 2000 in the county of Scania, Sweden, that comprised 10 723 persons aged 18-80 years living in 60 areas. Conducting multilevel logistic regression different techniques were applied...... propensity areas with the area educational level. The sorting out index was equal to 82%. CONCLUSION: Measures of variation in logistic regression should be promoted in social epidemiological and public health research as efficient means of quantifying the importance of the context of residence...

Factors Associated With Follow-Up Attendance Among Rape Victims Seen in Acute Medical Care.

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Darnell, Doyanne; Peterson, Roselyn; Berliner, Lucy; Stewart, Terri; Russo, Joan; Whiteside, Lauren; Zatzick, Douglas

2015-01-01

Rape is associated with posttraumatic stress disorder (PTSD) and related comorbidities. Most victims do not obtain treatment for these conditions. Acute care medical settings are well positioned to link patients to services; however, difficulty engaging victims and low attendance at provided follow-up appointments is well documented. Identifying factors associated with follow-up can inform engagement and linkage strategies. Administrative, patient self-report, and provider observational data from Harborview Medical Center were combined for the analysis. Using logistic regression, we examined factors associated with follow-up health service utilization after seeking services for rape in the emergency department. Of the 521 diverse female (n = 476) and male (n = 45) rape victims, 28% attended the recommended medical/counseling follow-up appointment. In the final (adjusted) logistic regression model, having a developmental or other disability (OR = 0.40, 95% CI = 0.21-0.77), having a current mental illness (OR = 0.25, 95% CI = 0.13-0.49), and being assaulted in public (OR = 0.50, 95% CI = 0.28-0.87) were uniquely associated with reduced odds of attending the follow-up. Having a prior mental health condition (OR = 3.02, 95% CI = 1.86-4.91), a completed Sexual Assault Nurse Examiner's (SANE) examination (OR = 2.97, 95% CI = 1.84-4.81), and social support available to help cope with the assault (OR = 3.54, 95% CI = 1.76-7.11) were associated with an increased odds of attending the follow-up. Findings point to relevant characteristics ascertained at the acute care medical visit for rape that may be used to identify victims less likely to obtain posttraumatic medical and mental health services. Efforts to improve service linkage for these patients is warranted and may require alternative service delivery models that engage rape survivors and support posttraumatic recovery.

Predicting Student Success on the Texas Chemistry STAAR Test: A Logistic Regression Analysis

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Johnson, William L.; Johnson, Annabel M.; Johnson, Jared

2012-01-01

Background: The context is the new Texas STAAR end-of-course testing program. Purpose: The authors developed a logistic regression model to predict who would pass-or-fail the new Texas chemistry STAAR end-of-course exam. Setting: Robert E. Lee High School (5A) with an enrollment of 2700 students, Tyler, Texas. Date of the study was the 2011-2012…

Re-experiencing Violence across the Life Course: Histories of Childhood Maltreatment and Elder Abuse Victimization.

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Kong, Jooyoung; Easton, Scott D

2018-03-26

This study primarily examines the associations between histories of childhood maltreatment (i.e., neglect, emotional, physical, and sexual abuse) and elder abuse victimization and explores whether gender moderates the associations. We conducted a secondary data analysis of 5,968 older adults (mean age = 71 years) based on data from the Wisconsin Longitudinal Study (2010-2011). Using retrospective self-reports of childhood and current (past 12 months) victimization experiences, logistic regression analyses were conducted to estimate the effects of early-life adversities on the likelihood of elder abuse victimization. Results indicate that childhood emotional abuse and childhood sexual abuse were associated with greater risk of being abused as older adults, after controlling for childhood and adult background factors. We also found that the effect of childhood sexual abuse on elder abuse victimization was weaker for women than men. Findings suggest that the phenomenon of revictimization may occur not only in early and middle adulthood, but also in late life. To advance our understanding of victimization across the life course, future research on root causes of elder abuse should include histories of child abuse.

PREDICTION OF MALIGNANT BREAST LESIONS FROM MRI FEATURES: A COMPARISON OF ARTIFICIAL NEURAL NETWORK AND LOGISTIC REGRESSION TECHNIQUES

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McLaren, Christine E.; Chen, Wen-Pin; Nie, Ke; Su, Min-Ying

2009-01-01

Rationale and Objectives Dynamic contrast enhanced MRI (DCE-MRI) is a clinical imaging modality for detection and diagnosis of breast lesions. Analytical methods were compared for diagnostic feature selection and performance of lesion classification to differentiate between malignant and benign lesions in patients. Materials and Methods The study included 43 malignant and 28 benign histologically-proven lesions. Eight morphological parameters, ten gray level co-occurrence matrices (GLCM) texture features, and fourteen Laws’ texture features were obtained using automated lesion segmentation and quantitative feature extraction. Artificial neural network (ANN) and logistic regression analysis were compared for selection of the best predictors of malignant lesions among the normalized features. Results Using ANN, the final four selected features were compactness, energy, homogeneity, and Law_LS, with area under the receiver operating characteristic curve (AUC) = 0.82, and accuracy = 0.76. The diagnostic performance of these 4-features computed on the basis of logistic regression yielded AUC = 0.80 (95% CI, 0.688 to 0.905), similar to that of ANN. The analysis also shows that the odds of a malignant lesion decreased by 48% (95% CI, 25% to 92%) for every increase of 1 SD in the Law_LS feature, adjusted for differences in compactness, energy, and homogeneity. Using logistic regression with z-score transformation, a model comprised of compactness, NRL entropy, and gray level sum average was selected, and it had the highest overall accuracy of 0.75 among all models, with AUC = 0.77 (95% CI, 0.660 to 0.880). When logistic modeling of transformations using the Box-Cox method was performed, the most parsimonious model with predictors, compactness and Law_LS, had an AUC of 0.79 (95% CI, 0.672 to 0.898). Conclusion The diagnostic performance of models selected by ANN and logistic regression was similar. The analytic methods were found to be roughly equivalent in terms of

LOGISTIC REGRESSION AS A TOOL FOR DETERMINATION OF THE PROBABILITY OF DEFAULT FOR ENTERPRISES

Directory of Open Access Journals (Sweden)

Erika SPUCHLAKOVA

2017-12-01

Full Text Available In a rapidly changing world it is necessary to adapt to new conditions. From a day to day approaches can vary. For the proper management of the company it is essential to know the financial situation. Assessment of the company financial health can be carried out by financial analysis which provides a number of methods how to evaluate the company financial health. Analysis indicators are often included in the company assessment, in obtaining bank loans and other financial resources to ensure the functioning of the company. As company focuses on the future and its planning, it is essential to forecast the future financial situation. According to the results of company´s financial health prediction, the company decides on the extension or limitation of its business. It depends mainly on the capabilities of company´s management how they will use information obtained from financial analysis in practice. The findings of logistic regression methods were published firstly in the 60s, as an alternative to the least squares method. The essence of logistic regression is to determine the relationship between being explained (dependent variable and explanatory (independent variables. The basic principle of this static method is based on the regression analysis, but unlike linear regression, it can predict the probability of a phenomenon that has occurred or not. The aim of this paper is to determine the probability of bankruptcy enterprises.

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

International Nuclear Information System (INIS)

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

2013-01-01

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

Low-level violence in schools: is there an association between school safety measures and peer victimization?

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Blosnich, John; Bossarte, Robert

2011-02-01

Low-level violent behavior, particularly school bullying, remains a critical public health issue that has been associated with negative mental and physical health outcomes. School-based prevention programs, while a valuable line of defense to stave off bullying, have shown inconsistent results in terms of decreasing bullying. This study explored whether school safety measures (eg, security guards, cameras, ID badges) were associated with student reports of different forms of peer victimization related to bullying. Data came from the 2007 School Crime Supplement of the National Crime Victimization Survey. Chi-square tests of independence were used to examine differences among categorical variables. Logistic regression models were constructed for the peer victimization outcomes. A count variable was constructed among the bullying outcomes (0-7) with which a Poisson regression model was constructed to analyze school safety measures' impacts on degree of victimization. Of the various school safety measures, only having adults in hallways resulted in a significant reduction in odds of being physically bullied, having property vandalized, or having rumors spread. In terms of degree of victimization, having adults and/or staff supervising hallways was associated with an approximate 26% decrease in students experiencing an additional form of peer victimization. Results indicated that school safety measures overall were not associated with decreased reports of low-level violent behaviors related to bullying. More research is needed to further explore what best promotes comprehensive safety in schools. © 2011, American School Health Association.

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

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

2017-01-01

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

The View From the Bottom: Relative Deprivation and Bullying Victimization in Canadian Adolescents.

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Napoletano, Anthony; Elgar, Frank J; Saul, Grace; Dirks, Melanie; Craig, Wendy

2016-12-01

We investigated the relation between relative deprivation (RD)-disparity in affluence between adolescents and their more affluent schoolmates-and involvement in bullying among 23,383 students (aged 9-19) in 413 schools that participated in the 2010 Canadian Health Behavior in School-Aged Children survey. Students reported family affluence and frequency of bullying victimization and perpetration during the previous 2 months. Using the Yitzhaki index of RD and multinomial logistic regression analysis, we found that RD positively related to three types of bullying victimization (physical, relational, and cyberbullying) and to two types of perpetration (relational and cyberbullying) after differences in absolute affluence were held constant. These findings suggest that RD uniquely contributes to risk of bullying involvement. © The Author(s) 2015.

[Domestic and family violence against women: a case-control study with victims treated in emergency rooms].

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Garcia, Leila Posenato; Duarte, Elisabeth Carmen; Freitas, Lúcia Rolim Santana de; Silva, Gabriela Drummond Marques da

2016-01-01

This study aimed to identify factors associated with treatment of victims of domestic and family violence in emergency rooms in Brazil. This is a case-control study based on the Surveillance System for Violence and Accidents (VIVA), 2011. Women ≥ 18 years who were victims of family and domestic violence were selected as cases and compared to accident victims (controls). Adjusted odds ratios were estimated by unconditional logistic regression. 623 cases and 10,120 controls were included. Risk factors according to the adjusted analysis were younger age (18-29 years), low schooling, lack of paid work, alcohol consumption, having sought treatment in a different health service, and violence on weekends or at night or in the early morning hours. The study concludes that domestic and family violence shows alcohol consumption as a strongly associated factor. Days and hours with the highest ocurrence reveal the need to adjust emergency services to treat victims.

A secure distributed logistic regression protocol for the detection of rare adverse drug events.

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El Emam, Khaled; Samet, Saeed; Arbuckle, Luk; Tamblyn, Robyn; Earle, Craig; Kantarcioglu, Murat

2013-05-01

There is limited capacity to assess the comparative risks of medications after they enter the market. For rare adverse events, the pooling of data from multiple sources is necessary to have the power and sufficient population heterogeneity to detect differences in safety and effectiveness in genetic, ethnic and clinically defined subpopulations. However, combining datasets from different data custodians or jurisdictions to perform an analysis on the pooled data creates significant privacy concerns that would need to be addressed. Existing protocols for addressing these concerns can result in reduced analysis accuracy and can allow sensitive information to leak. To develop a secure distributed multi-party computation protocol for logistic regression that provides strong privacy guarantees. We developed a secure distributed logistic regression protocol using a single analysis center with multiple sites providing data. A theoretical security analysis demonstrates that the protocol is robust to plausible collusion attacks and does not allow the parties to gain new information from the data that are exchanged among them. The computational performance and accuracy of the protocol were evaluated on simulated datasets. The computational performance scales linearly as the dataset sizes increase. The addition of sites results in an exponential growth in computation time. However, for up to five sites, the time is still short and would not affect practical applications. The model parameters are the same as the results on pooled raw data analyzed in SAS, demonstrating high model accuracy. The proposed protocol and prototype system would allow the development of logistic regression models in a secure manner without requiring the sharing of personal health information. This can alleviate one of the key barriers to the establishment of large-scale post-marketing surveillance programs. We extended the secure protocol to account for correlations among patients within sites through

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

Science.gov (United States)

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

2015-02-01

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

A hybrid approach of stepwise regression, logistic regression, support vector machine, and decision tree for forecasting fraudulent financial statements.

Science.gov (United States)

Chen, Suduan; Goo, Yeong-Jia James; Shen, Zone-De

2014-01-01

As the fraudulent financial statement of an enterprise is increasingly serious with each passing day, establishing a valid forecasting fraudulent financial statement model of an enterprise has become an important question for academic research and financial practice. After screening the important variables using the stepwise regression, the study also matches the logistic regression, support vector machine, and decision tree to construct the classification models to make a comparison. The study adopts financial and nonfinancial variables to assist in establishment of the forecasting fraudulent financial statement model. Research objects are the companies to which the fraudulent and nonfraudulent financial statement happened between years 1998 to 2012. The findings are that financial and nonfinancial information are effectively used to distinguish the fraudulent financial statement, and decision tree C5.0 has the best classification effect 85.71%.

Estimating traffic volume on Wyoming low volume roads using linear and logistic regression methods

Directory of Open Access Journals (Sweden)

Dick Apronti

2016-12-01

Full Text Available Traffic volume is an important parameter in most transportation planning applications. Low volume roads make up about 69% of road miles in the United States. Estimating traffic on the low volume roads is a cost-effective alternative to taking traffic counts. This is because traditional traffic counts are expensive and impractical for low priority roads. The purpose of this paper is to present the development of two alternative means of cost-effectively estimating traffic volumes for low volume roads in Wyoming and to make recommendations for their implementation. The study methodology involves reviewing existing studies, identifying data sources, and carrying out the model development. The utility of the models developed were then verified by comparing actual traffic volumes to those predicted by the model. The study resulted in two regression models that are inexpensive and easy to implement. The first regression model was a linear regression model that utilized pavement type, access to highways, predominant land use types, and population to estimate traffic volume. In verifying the model, an R2 value of 0.64 and a root mean square error of 73.4% were obtained. The second model was a logistic regression model that identified the level of traffic on roads using five thresholds or levels. The logistic regression model was verified by estimating traffic volume thresholds and determining the percentage of roads that were accurately classified as belonging to the given thresholds. For the five thresholds, the percentage of roads classified correctly ranged from 79% to 88%. In conclusion, the verification of the models indicated both model types to be useful for accurate and cost-effective estimation of traffic volumes for low volume Wyoming roads. The models developed were recommended for use in traffic volume estimations for low volume roads in pavement management and environmental impact assessment studies.

Cyberbullying victimization and mental health in adolescents and the moderating role of family dinners.

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Elgar, Frank J; Napoletano, Anthony; Saul, Grace; Dirks, Melanie A; Craig, Wendy; Poteat, V Paul; Holt, Melissa; Koenig, Brian W

2014-11-01

This study presents evidence that cyberbullying victimization relates to internalizing, externalizing, and substance use problems in adolescents and that the frequency of family dinners attenuate these associations. To examine the unique association between cyberbullying victimization and adolescent mental health (after controlling differences in involvement in traditional, face-to-face bullying) and to explore the potential moderating role of family contact in this association. This cross-sectional, observational study used survey data on 18,834 students (aged 12-18 years) from 49 schools in a Midwestern US state. Logistic regression analysis tested associations between cyberbullying victimization and the likelihood of mental health and substance use problems. Negative binomial regression analysis tested direct and synergistic contributions of cyberbullying victimization and family dinners on the rates of mental health and substance use problems. Frequency of cyberbullying victimization during the previous 12 months; victimization by traditional (face-to-face) bullying; and perpetration of traditional bullying. Five internalizing mental health problems (anxiety, depression, self-harm, suicide ideation, and suicide attempt), 2 externalizing problems (fighting and vandalism), and 4 substance use problems (frequent alcohol use, frequent binge drinking, prescription drug misuse, and over-the-counter drug misuse). About one-fifth (18.6%) of the sample experienced cyberbullying during the previous 12 months. The frequency of cyberbullying positively related to all 11 internalizing, externalizing, and substance use problems (odds ratios from 2.6 [95% CI, 1.7-3.8] to 4.5 [95% CI, 3.0-6.6]). However, victimization related more closely to rates of problems in adolescents that had fewer family dinners. Cyberbullying relates to mental health and substance use problems in adolescents, even after their involvement in face-to-face bullying is taken into account. Although

Non-proportional odds multivariate logistic regression of ordinal family data.

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Zaloumis, Sophie G; Scurrah, Katrina J; Harrap, Stephen B; Ellis, Justine A; Gurrin, Lyle C

2015-03-01

Methods to examine whether genetic and/or environmental sources can account for the residual variation in ordinal family data usually assume proportional odds. However, standard software to fit the non-proportional odds model to ordinal family data is limited because the correlation structure of family data is more complex than for other types of clustered data. To perform these analyses we propose the non-proportional odds multivariate logistic regression model and take a simulation-based approach to model fitting using Markov chain Monte Carlo methods, such as partially collapsed Gibbs sampling and the Metropolis algorithm. We applied the proposed methodology to male pattern baldness data from the Victorian Family Heart Study. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Economic Insecurity and Intimate Partner and Sexual Violence Victimization.

Science.gov (United States)

Breiding, Matthew J; Basile, Kathleen C; Klevens, Joanne; Smith, Sharon G

2017-10-01

Previous research has consistently found that low SES is associated with higher levels of both intimate partner violence (IPV) and sexual violence (SV) victimization. Though associated with poverty, two indicators of economic insecurity, food and housing insecurity, have been identified as conceptually distinct social determinants of health. This study examined the relationship between food and housing insecurity experienced in the preceding 12 months and IPV and SV victimization experienced in the preceding 12 months, after controlling for SES and other demographic variables. Data were from the 2010 National Intimate Partner and Sexual Violence Survey, a nationally representative telephone survey of U.S. adults. In 2016, multivariate logistic regression modeling was used to examine the association between food and housing insecurity and multiple forms of IPV and SV victimization. Robust associations were found between food and housing insecurity experienced in the preceding 12 months and IPV and SV experienced in the preceding 12 months, for women and men, even after controlling for age, family income, race/ethnicity, education, and marital status. Food and housing insecurity may be important considerations for the prevention of SV and IPV or the reductions of their consequences, although future research is needed to disentangle the direction of the association. Strategies aimed at buffering economic insecurity may reduce vulnerability to IPV and SV victimization. Copyright © 2017. Published by Elsevier Inc.

Logistic Regression in the Identification of Hazards in Construction

Science.gov (United States)

Drozd, Wojciech

2017-10-01

The construction site and its elements create circumstances that are conducive to the formation of risks to safety during the execution of works. Analysis indicates the critical importance of these factors in the set of characteristics that describe the causes of accidents in the construction industry. This article attempts to analyse the characteristics related to the construction site, in order to indicate their importance in defining the circumstances of accidents at work. The study includes sites inspected in 2014 - 2016 by the employees of the District Labour Inspectorate in Krakow (Poland). The analysed set of detailed (disaggregated) data includes both quantitative and qualitative characteristics. The substantive task focused on classification modelling in the identification of hazards in construction and identifying those of the analysed characteristics that are important in an accident. In terms of methodology, resource data analysis using statistical classifiers, in the form of logistic regression, was the method used.

Alcohol Policies and Alcohol-Involved Homicide Victimization in the United States.

Science.gov (United States)

Naimi, Timothy S; Xuan, Ziming; Coleman, Sharon M; Lira, Marlene C; Hadland, Scott E; Cooper, Susanna E; Heeren, Timothy C; Swahn, Monica H

2017-09-01

The purpose of this study was to examine the associations between the alcohol policy environment and alcohol involvement in homicide victims in the United States, overall and by sociodemographic groups. To characterize the alcohol policy environment, the presence, efficacy, and degree of implementation of 29 alcohol policies were used to determine Alcohol Policy Scale (APS) scores by state and year. Data about homicide victims from 17 states from 2003 to 2012 were obtained from the National Violent Death Reporting System. APS scores were used as lagged exposure variables in generalized estimating equation logistic regression models to predict the individual-level odds of alcohol involvement (i.e., blood alcohol concentration [BAC] > 0.00% vs. = 0.00% and BAC ≥ 0.08% vs. ≤ 0.079%) among homicide victims. A 10 percentage point increase in APS score (representing a more restrictive policy environment) was associated with reduced odds of alcohol-involved homicide with BAC greater than 0.00% (adjusted odds ratio [AOR] = 0.89, 95% CI [0.82, 0.99]) and BAC of 0.08% or more (AOR = 0.91, 95% CI [0.82, 1.02]). In stratified analyses of homicide victims, more restrictive policy environments were significantly protective of alcohol involvement at both BAC levels among those who were female, ages 21-29 years, Hispanic, unmarried, victims of firearm homicides, and victims of homicides related to intimate partner violence. More restrictive alcohol policy environments were associated with reduced odds of alcohol-involved homicide victimization overall and among groups at high risk of homicide. Strengthening alcohol policies is a promising homicide prevention strategy.

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.

Assessment of diagnostic value of tumor markers for colorectal neoplasm by logistic regression and ROC curve

International Nuclear Information System (INIS)

Ping, G.

2007-01-01

Full text: Objective: To assess the diagnostic value of CEA CA199 and CA50 for colorectal neoplasm by logistic regression and ROC curve. Methods: The subjects include 75 patients of colorectal cancer, 35 patients of benign intestinal disease and 49 health controls. CEA CA199 and CA50 are measured by CLIA ECLIA and IRMA respectively. The area under the curve (AUC) of CEA CA 199 CA50 and logistic regression results are compared. [Result] In the cancer-benign group, the AUC of CA50 is larger than the AUC of CA199 Compared with the AUC of combination of CEA CA199 and CA50 (0.604),the AUC of combination of CEA and CA50 (0.875) is larger and it is also larger than any other AUC of CEA CA199 or CA50 alone. In the cancerhealth group, the AUC of combination of CEA CA199 and CA50 is larger than any other AUC of CEA CA199 or CA50 alone. No matter in the cancer-benign group or cancerhealth group. The AUC of CEA is larger than the AUC of CA199 or CA50. Conclusion: CEA is useful in the diagnosis of colorectal cancer. In the process of differential diagnosis, the combination of CEA and CA50 can give more information, while the combination of three tumor markers does not perform well. Furthermore, as a statistical method, logistic regression can improve the diagnostic sensitivity and specificity. (author)

Influential factors of red-light running at signalized intersection and prediction using a rare events logistic regression model.

Science.gov (United States)

Ren, Yilong; Wang, Yunpeng; Wu, Xinkai; Yu, Guizhen; Ding, Chuan

2016-10-01

Red light running (RLR) has become a major safety concern at signalized intersection. To prevent RLR related crashes, it is critical to identify the factors that significantly impact the drivers' behaviors of RLR, and to predict potential RLR in real time. In this research, 9-month's RLR events extracted from high-resolution traffic data collected by loop detectors from three signalized intersections were applied to identify the factors that significantly affect RLR behaviors. The data analysis indicated that occupancy time, time gap, used yellow time, time left to yellow start, whether the preceding vehicle runs through the intersection during yellow, and whether there is a vehicle passing through the intersection on the adjacent lane were significantly factors for RLR behaviors. Furthermore, due to the rare events nature of RLR, a modified rare events logistic regression model was developed for RLR prediction. The rare events logistic regression method has been applied in many fields for rare events studies and shows impressive performance, but so far none of previous research has applied this method to study RLR. The results showed that the rare events logistic regression model performed significantly better than the standard logistic regression model. More importantly, the proposed RLR prediction method is purely based on loop detector data collected from a single advance loop detector located 400 feet away from stop-bar. This brings great potential for future field applications of the proposed method since loops have been widely implemented in many intersections and can collect data in real time. This research is expected to contribute to the improvement of intersection safety significantly. Copyright © 2016 Elsevier Ltd. All rights reserved.

How to deal with continuous and dichotomic outcomes in epidemiological research: linear and logistic regression analyses

NARCIS (Netherlands)

Tripepi, Giovanni; Jager, Kitty J.; Stel, Vianda S.; Dekker, Friedo W.; Zoccali, Carmine

2011-01-01

Because of some limitations of stratification methods, epidemiologists frequently use multiple linear and logistic regression analyses to address specific epidemiological questions. If the dependent variable is a continuous one (for example, systolic pressure and serum creatinine), the researcher

Proportional Odds Logistic Regression - Effective Means of Dealing with Limited Uncertainty in Dichotomizing Clinical Outcomes

Czech Academy of Sciences Publication Activity Database

Valenta, Zdeněk; Pitha, J.; Poledne, R.

2006-01-01

Roč. 25, č. 24 (2006), s. 4227-4234 ISSN 0277-6715 R&D Projects: GA MZd NA7512 Institutional research plan: CEZ:AV0Z10300504 Keywords : proportional odds logistic regression * dichotomized outcomes * uncertainty Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 1.737, year: 2006

Combining logistic regression with classification and regression tree to predict quality of care in a home health nursing data set.

Science.gov (United States)

Guo, Huey-Ming; Shyu, Yea-Ing Lotus; Chang, Her-Kun

2006-01-01

In this article, the authors provide an overview of a research method to predict quality of care in home health nursing data set. The results of this study can be visualized through classification an regression tree (CART) graphs. The analysis was more effective, and the results were more informative since the home health nursing dataset was analyzed with a combination of the logistic regression and CART, these two techniques complete each other. And the results more informative that more patients' characters were related to quality of care in home care. The results contributed to home health nurse predict patient outcome in case management. Improved prediction is needed for interventions to be appropriately targeted for improved patient outcome and quality of care.

Using Logistic Regression to Predict the Probability of Debris Flows in Areas Burned by Wildfires, Southern California, 2003-2006

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Rupert, Michael G.; Cannon, Susan H.; Gartner, Joseph E.; Michael, John A.; Helsel, Dennis R.

2008-01-01

Logistic regression was used to develop statistical models that can be used to predict the probability of debris flows in areas recently burned by wildfires by using data from 14 wildfires that burned in southern California during 2003-2006. Twenty-eight independent variables describing the basin morphology, burn severity, rainfall, and soil properties of 306 drainage basins located within those burned areas were evaluated. The models were developed as follows: (1) Basins that did and did not produce debris flows soon after the 2003 to 2006 fires were delineated from data in the National Elevation Dataset using a geographic information system; (2) Data describing the basin morphology, burn severity, rainfall, and soil properties were compiled for each basin. These data were then input to a statistics software package for analysis using logistic regression; and (3) Relations between the occurrence or absence of debris flows and the basin morphology, burn severity, rainfall, and soil properties were evaluated, and five multivariate logistic regression models were constructed. All possible combinations of independent variables were evaluated to determine which combinations produced the most effective models, and the multivariate models that best predicted the occurrence of debris flows were identified. Percentage of high burn severity and 3-hour peak rainfall intensity were significant variables in all models. Soil organic matter content and soil clay content were significant variables in all models except Model 5. Soil slope was a significant variable in all models except Model 4. The most suitable model can be selected from these five models on the basis of the availability of independent variables in the particular area of interest and field checking of probability maps. The multivariate logistic regression models can be entered into a geographic information system, and maps showing the probability of debris flows can be constructed in recently burned areas of

Mothers who were sexually abused during childhood are more likely to have a child victim of sexual violence

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Luis Eduardo Wearick-Silva

2014-06-01

Full Text Available Introduction: Recurrent exposure to childhood sexual abuse (CSA seems to be higher among victims of sexual abuse. In this sense, experiences related to sexual violence can perpetuate within the family context itself in various ways. Here, we investigate the association between being exposed to CSA and having a child victim of sexual abuse. Method: We used a sample with 123 mothers, who were divided into 2 groups: one consisting of 41 mothers of sexually abused children and another consisting of 82 mothers of non-sexually abused children. History of exposure to CSA was evaluated by means of the Childhood Trauma Questionnaire - Short Form (CTQ and we used a logistic regression model to estimate the prediction values regarding having or not a child exposed to sexual violence. Results: Mothers of sexually abused children had significantly higher scores on CTQ, especially on the sexual abuse subscale (SA. According to our logistic regression model, higher scores on the CTQ significantly predicted the status of being a mother of children exposed to sexual violence in our sample (Wald = 7.074; p = 0.008; Exp(B = 1.681. Years of formal education reduced the likelihood of having a child victim of sexual violence (Wald = 18.994; p = 0.001; Exp(B = 0.497. Conclusion: Our findings highlight the importance of a possible intergenerational effect of sexual abuse. Family intervention and prevention against childhood maltreatment should take this issue in account.

Predicting smear negative pulmonary tuberculosis with classification trees and logistic regression: a cross-sectional study

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Kritski Afrânio

2006-02-01

Full Text Available Abstract Background Smear negative pulmonary tuberculosis (SNPT accounts for 30% of pulmonary tuberculosis cases reported yearly in Brazil. This study aimed to develop a prediction model for SNPT for outpatients in areas with scarce resources. Methods The study enrolled 551 patients with clinical-radiological suspicion of SNPT, in Rio de Janeiro, Brazil. The original data was divided into two equivalent samples for generation and validation of the prediction models. Symptoms, physical signs and chest X-rays were used for constructing logistic regression and classification and regression tree models. From the logistic regression, we generated a clinical and radiological prediction score. The area under the receiver operator characteristic curve, sensitivity, and specificity were used to evaluate the model's performance in both generation and validation samples. Results It was possible to generate predictive models for SNPT with sensitivity ranging from 64% to 71% and specificity ranging from 58% to 76%. Conclusion The results suggest that those models might be useful as screening tools for estimating the risk of SNPT, optimizing the utilization of more expensive tests, and avoiding costs of unnecessary anti-tuberculosis treatment. Those models might be cost-effective tools in a health care network with hierarchical distribution of scarce resources.

A Hybrid Approach of Stepwise Regression, Logistic Regression, Support Vector Machine, and Decision Tree for Forecasting Fraudulent Financial Statements

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Suduan Chen

2014-01-01

Full Text Available As the fraudulent financial statement of an enterprise is increasingly serious with each passing day, establishing a valid forecasting fraudulent financial statement model of an enterprise has become an important question for academic research and financial practice. After screening the important variables using the stepwise regression, the study also matches the logistic regression, support vector machine, and decision tree to construct the classification models to make a comparison. The study adopts financial and nonfinancial variables to assist in establishment of the forecasting fraudulent financial statement model. Research objects are the companies to which the fraudulent and nonfraudulent financial statement happened between years 1998 to 2012. The findings are that financial and nonfinancial information are effectively used to distinguish the fraudulent financial statement, and decision tree C5.0 has the best classification effect 85.71%.

A Two-Stage Penalized Logistic Regression Approach to Case-Control Genome-Wide Association Studies

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Jingyuan Zhao

2012-01-01

Full Text Available We propose a two-stage penalized logistic regression approach to case-control genome-wide association studies. This approach consists of a screening stage and a selection stage. In the screening stage, main-effect and interaction-effect features are screened by using L1-penalized logistic like-lihoods. In the selection stage, the retained features are ranked by the logistic likelihood with the smoothly clipped absolute deviation (SCAD penalty (Fan and Li, 2001 and Jeffrey’s Prior penalty (Firth, 1993, a sequence of nested candidate models are formed, and the models are assessed by a family of extended Bayesian information criteria (J. Chen and Z. Chen, 2008. The proposed approach is applied to the analysis of the prostate cancer data of the Cancer Genetic Markers of Susceptibility (CGEMS project in the National Cancer Institute, USA. Simulation studies are carried out to compare the approach with the pair-wise multiple testing approach (Marchini et al. 2005 and the LASSO-patternsearch algorithm (Shi et al. 2007.

Adolescent predictors of young adult cyberbullying perpetration and victimization among Australian youth.

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Hemphill, Sheryl A; Heerde, Jessica A

2014-10-01

The purpose of the current article was to examine the adolescent risk and protective factors (at the individual, peer group, and family level) for young adult cyberbullying perpetration and victimization. Data from 2006 (Grade 9) to 2010 (young adulthood) were analyzed from a community sample of 927 Victorian students originally recruited as a statewide representative sample in Grade 5 (age, 10-11 years) in 2002 and followed-up to age 18-19 years in 2010 (N = 809). Participants completed a self-report survey on adolescent risk and protective factors and traditional and cyberbullying perpetration and victimization and young adult cyberbullying perpetration and victimization. As young adults, 5.1% self-reported cyberbullying perpetration only, 5.0% reported cyberbullying victimization only, and 9.5% reported both cyberbullying perpetration and victimization. In fully adjusted logistic regression analyses, the adolescent predictors of cyberbullying perpetration only were traditional bullying perpetration, traditional bullying perpetration and victimization, and poor family management. For young adulthood cyberbullying victimization only, the adolescent predictor was emotion control. The adolescent predictors for young adult cyberbullying perpetration and victimization were traditional bullying perpetration and cyberbullying perpetration and victimization. Based on the results of this study, possible targets for prevention and early intervention are reducing adolescent involvement in (traditional or cyber) bullying through the development of social skills and conflict resolution skills. In addition, another important prevention target is to support families with adolescents to ensure that they set clear rules and monitor adolescents' behavior. Universal programs that assist adolescents to develop skills in emotion control are warranted. Copyright © 2014 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

The Role of Parent Communication and Connectedness in Dating Violence Victimization among Latino Adolescents.

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Kast, Nicole Rebecca; Eisenberg, Marla E; Sieving, Renee E

2016-06-01

Dating violence among U.S. adolescents is a substantial concern. Previous research indicates that Latino youth are at increased risk of dating violence victimization. This secondary data analysis examined the prevalence of physical and sexual dating violence victimization among subgroups of Latino adolescents and associations of parent communication, parent caring, and dating violence victimization using data from the 2010 Minnesota Student Survey (N = 4,814). Parallel analyses were conducted for Latino-only and multiple-race Latino adolescents, stratified by gender. Multivariate logistic regression models tested associations between race/ethnicity, parent communication, perceived parent caring, and adolescent dating violence experiences. Overall, 7.2% to 16.2% of Latinos reported physical or sexual dating violence. Both types of dating violence were more prevalent among multiple-race Latinos than among Latino-only adolescents, with prevalence rates highest among multiple-race Latino females (19.8% and 19.7% for physical and sexual dating violence victimization, respectively). In multivariate models, perceived parent caring was the most important protective factor against physical and sexual dating violence among males and females. High levels of mother and father communication were associated with less physical violence victimization among males and females and with less sexual violence victimization among females. Results highlight the importance of parent communication and parent caring as buffers against dating violence victimization for Latino youth. These findings indicate potential for preventive interventions with Latino adolescents targeting family connectedness to address dating violence victimization. © The Author(s) 2015.

Efficient logistic regression designs under an imperfect population identifier.

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Albert, Paul S; Liu, Aiyi; Nansel, Tonja

2014-03-01

Motivated by actual study designs, this article considers efficient logistic regression designs where the population is identified with a binary test that is subject to diagnostic error. We consider the case where the imperfect test is obtained on all participants, while the gold standard test is measured on a small chosen subsample. Under maximum-likelihood estimation, we evaluate the optimal design in terms of sample selection as well as verification. We show that there may be substantial efficiency gains by choosing a small percentage of individuals who test negative on the imperfect test for inclusion in the sample (e.g., verifying 90% test-positive cases). We also show that a two-stage design may be a good practical alternative to a fixed design in some situations. Under optimal and nearly optimal designs, we compare maximum-likelihood and semi-parametric efficient estimators under correct and misspecified models with simulations. The methodology is illustrated with an analysis from a diabetes behavioral intervention trial. © 2013, The International Biometric Society.

Forecast Model of Urban Stagnant Water Based on Logistic Regression

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Liu Pan

2017-01-01

Full Text Available With the development of information technology, the construction of water resource system has been gradually carried out. In the background of big data, the work of water information needs to carry out the process of quantitative to qualitative change. Analyzing the correlation of data and exploring the deep value of data which are the key of water information’s research. On the basis of the research on the water big data and the traditional data warehouse architecture, we try to find out the connection of different data source. According to the temporal and spatial correlation of stagnant water and rainfall, we use spatial interpolation to integrate data of stagnant water and rainfall which are from different data source and different sensors, then use logistic regression to find out the relationship between them.

Predicting Insolvency : A comparison between discriminant analysis and logistic regression using principal components

OpenAIRE

Geroukis, Asterios; Brorson, Erik

2014-01-01

In this study, we compare the two statistical techniques logistic regression and discriminant analysis to see how well they classify companies based on clusters – made from the solvency ratio ­– using principal components as independent variables. The principal components are made with different financial ratios. We use cluster analysis to find groups with low, medium and high solvency ratio of 1200 different companies found on the NASDAQ stock market and use this as an apriori definition of ...

Impact of bullying victimization on suicide and negative health behaviors among adolescents in Latin America.

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Romo, Matthew L; Kelvin, Elizabeth A

2016-11-01

To compare the prevalence of bullying victimization, suicidal ideation, suicidal attempts, and negative health behaviors (current tobacco use, recent heavy alcohol use, truancy, involvement in physical fighting, and unprotected sexual intercourse) in five different Latin American countries and determine the association of bullying victimization with these outcomes, exploring both bullying type and frequency. Study data were from Global School-based Student Health Surveys from Bolivia, Costa Rica, Honduras, Peru, and Uruguay, which covered nationally representative samples of school-going adolescents. The surveys used a two-stage clustered sample design, sampling schools and then classrooms. Logistic regression models were run to determine the statistical significance of associations with bullying. Among the 14 560 school-going adolescents included in this study, the prevalence of any bullying victimization in the past 30 days was 37.8%. Bullying victimization was associated with greater odds of suicidal ideation with planning (adjusted odds ratio (AOR): 3.12; P bullying victimization on suicide outcomes was also observed. Bullying victimization was associated with higher odds of current tobacco use (AOR: 2.14; P bullying victimization varied by country, its association with suicidal ideation and behavior and negative health behaviors remained relatively consistent. Addressing bullying needs to be made a priority in Latin America, and an integrated approach that also includes mental and physical health promotion is needed.

A Comparison of Logistic Regression, Neural Networks, and Classification Trees Predicting Success of Actuarial Students

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Schumacher, Phyllis; Olinsky, Alan; Quinn, John; Smith, Richard

2010-01-01

The authors extended previous research by 2 of the authors who conducted a study designed to predict the successful completion of students enrolled in an actuarial program. They used logistic regression to determine the probability of an actuarial student graduating in the major or dropping out. They compared the results of this study with those…

Use of Social Networking Sites and Risk of Cyberbullying Victimization: A Population-Level Study of Adolescents.

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Sampasa-Kanyinga, Hugues; Hamilton, Hayley A

2015-12-01

Social networking sites (SNSs) have gained considerable popularity among youth in recent years; however, there is a noticeable paucity of research examining the association between the use of these web-based platforms and cyberbullying victimization at the population level. This study examines the association between the use of SNSs and cyberbullying victimization using a large-scale survey of Canadian middle and high school students. Data on 5,329 students aged 11-20 years were derived from the 2013 Ontario Student Drug Use and Health Survey. Logistic regression was used to examine the relationship between the use of SNSs and cyberbullying victimization while adjusting for covariates. Overall, 19 percent of adolescents were cyberbullied in the past 12 months. Adolescents who were female, younger, of lower socioeconomic status, and who used alcohol or tobacco were at greater odds of being cyberbullied. The use of SNSs was associated with an increased risk of cyberbullying victimization in a dose-response manner (p-trend <0.001). Gender was not a significant moderator of the association between use of SNSs and being cyberbullied. Results from this study underscore the need for raising awareness and educating adolescents on effective strategies to prevent cyberbullying victimization.

Easy and low-cost identification of metabolic syndrome in patients treated with second-generation antipsychotics: artificial neural network and logistic regression models.

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Lin, Chao-Cheng; Bai, Ya-Mei; Chen, Jen-Yeu; Hwang, Tzung-Jeng; Chen, Tzu-Ting; Chiu, Hung-Wen; Li, Yu-Chuan

2010-03-01

Metabolic syndrome (MetS) is an important side effect of second-generation antipsychotics (SGAs). However, many SGA-treated patients with MetS remain undetected. In this study, we trained and validated artificial neural network (ANN) and multiple logistic regression models without biochemical parameters to rapidly identify MetS in patients with SGA treatment. A total of 383 patients with a diagnosis of schizophrenia or schizoaffective disorder (DSM-IV criteria) with SGA treatment for more than 6 months were investigated to determine whether they met the MetS criteria according to the International Diabetes Federation. The data for these patients were collected between March 2005 and September 2005. The input variables of ANN and logistic regression were limited to demographic and anthropometric data only. All models were trained by randomly selecting two-thirds of the patient data and were internally validated with the remaining one-third of the data. The models were then externally validated with data from 69 patients from another hospital, collected between March 2008 and June 2008. The area under the receiver operating characteristic curve (AUC) was used to measure the performance of all models. Both the final ANN and logistic regression models had high accuracy (88.3% vs 83.6%), sensitivity (93.1% vs 86.2%), and specificity (86.9% vs 83.8%) to identify MetS in the internal validation set. The mean +/- SD AUC was high for both the ANN and logistic regression models (0.934 +/- 0.033 vs 0.922 +/- 0.035, P = .63). During external validation, high AUC was still obtained for both models. Waist circumference and diastolic blood pressure were the common variables that were left in the final ANN and logistic regression models. Our study developed accurate ANN and logistic regression models to detect MetS in patients with SGA treatment. The models are likely to provide a noninvasive tool for large-scale screening of MetS in this group of patients. (c) 2010 Physicians

Estimating the causes of traffic accidents using logistic regression and discriminant analysis.

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Karacasu, Murat; Ergül, Barış; Altin Yavuz, Arzu

2014-01-01

Factors that affect traffic accidents have been analysed in various ways. In this study, we use the methods of logistic regression and discriminant analysis to determine the damages due to injury and non-injury accidents in the Eskisehir Province. Data were obtained from the accident reports of the General Directorate of Security in Eskisehir; 2552 traffic accidents between January and December 2009 were investigated regarding whether they resulted in injury. According to the results, the effects of traffic accidents were reflected in the variables. These results provide a wealth of information that may aid future measures toward the prevention of undesired results.

Large scale identification and categorization of protein sequences using structured logistic regression

DEFF Research Database (Denmark)

Pedersen, Bjørn Panella; Ifrim, Georgiana; Liboriussen, Poul

2014-01-01

Abstract Background Structured Logistic Regression (SLR) is a newly developed machine learning tool first proposed in the context of text categorization. Current availability of extensive protein sequence databases calls for an automated method to reliably classify sequences and SLR seems well...... problem. Results Using SLR, we have built classifiers to identify and automatically categorize P-type ATPases into one of 11 pre-defined classes. The SLR-classifiers are compared to a Hidden Markov Model approach and shown to be highly accurate and scalable. Representing the bulk of currently known...... for further biochemical characterization and structural analysis....

Adolescent predictors of young adult cyber-bullying perpetration and victimization among Australian youth

Science.gov (United States)

Hemphill, Sheryl A.; Heerde, Jessica A.

2014-01-01

Purpose The purpose of the current paper was to examine the adolescent risk and protective factors (at the individual, peer group, and family level) for young adult cyber-bullying perpetration and victimization. Methods Data from 2006 (Grade 9) to 2010 (young adulthood) were analyzed from a community sample of 927 Victorian students originally recruited as a state-wide representative sample in Grade 5 (age 10–11 years) in 2002 and followed up to age 18–19 years in 2010 (N = 809). Participants completed a self-report survey on adolescent risk and protective factors and traditional and cyber-bullying perpetration and victimization, and young adult cyber-bullying perpetration and victimization. Results As young adults, 5.1% self-reported cyber-bullying perpetration only, 5.0% cyber-bullying victimization only, and 9.5% reported both cyber-bullying perpetration and victimization. In fully adjusted logistic regression analyses, the adolescent predictors of cyber-bullying perpetration only were traditional bullying perpetration, traditional bullying perpetration and victimization, and poor family management. For young adulthood cyber-bullying victimization only, the adolescent predictor was emotion control. The adolescent predictors for young adult cyber-bullying perpetration and victimization were traditional bullying perpetration and cyber-bullying perpetration and victimization. Conclusions Based on the results of this study, possible targets for prevention and early intervention are reducing adolescent involvement in (traditional or cyber-) bullying through the development of social skills and conflict resolution skills. In addition, another important prevention target is to support families with adolescents to ensure they set clear rules and monitor adolescent’s behavior. Universal programs that assist adolescents to develop skills in emotion control are warranted. PMID:24939014

High school students' experiences of bullying and victimization and the association with school health center use.

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Lewis, Catherine; Deardorff, Julianna; Lahiff, Maureen; Soleimanpour, Samira; Sakashita, Kimi; Brindis, Claire D

2015-05-01

Bullying and victimization are ongoing concerns in schools. School health centers (SHCs) are well situated to support affected students because they provide crisis intervention, mental health care, and broader interventions to improve school climate. This study examined the association between urban adolescents' experiences of school-based bullying and victimization and their use of SHCs. Data was analyzed from 2063 high school students in 5 Northern California school districts using the 2009-2010 California Healthy Kids Survey. Chi-square tests and multivariate logistic regression were used to measure associations. Students who were bullied or victimized at school had significantly higher odds of using the SHCs compared with students who were not, and were also significantly more likely to report confidentiality concerns. The magnitude of associations was largest for Asian/Pacific Islander students, though this was likely due to greater statistical power. African American students reported victimization experiences at approximately the same rate as their peers, but were significantly less likely to indicate they experienced bullying. Findings suggest that SHCs may be an important place to address bullying and victimization at school, but confidentiality concerns are barriers that may be more common among bullied and victimized youth. © 2015, American School Health Association.

Predictors of Placement Stability at the State Level: The Use of Logistic Regression to Inform Practice

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Courtney, Jon R.; Prophet, Retta

2011-01-01

Placement instability is often associated with a number of negative outcomes for children. To gain state level contextual knowledge of factors associated with placement stability/instability, logistic regression was applied to selected variables from the New Mexico Adoption and Foster Care Administrative Reporting System dataset. Predictors…

Criminal victimization and psychotic experiences: cross-sectional associations in 35 low- and middle-income countries.

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DeVylder, J E; Kelleher, I; Oh, H; Link, B G; Yang, L H; Koyanagi, A

2018-04-22

Criminal victimization has been associated with elevated risk for psychotic symptoms in the United Kingdom, but has not been studied in low- and middle-income countries (LMICs). Understanding whether crime exposure may play a role in the social etiology of psychosis could help guide prevention and intervention efforts. We tested the hypothesis that criminal victimization would be associated with elevated odds of psychotic experiences in 35 LMICs (N = 146 999) using cross-sectional data from the World Health Organization World Health Survey. Multivariable logistic regression analyses were used to test for associations between criminal victimization and psychotic experiences. Victimization was associated with greater odds of psychotic experiences, OR (95% CI) = 1.72 (1.50-1.98), and was significantly more strongly associated with psychotic experiences in non-urban, OR (95% CI) = 1.93 (1.60-2.33), compared to urban settings, OR (95% CI) = 1.48 (1.21-1.81). The association between victimization and psychosis did not change across countries with varying aggregated levels of criminal victimization. In the largest ever study of victimization and psychosis, the association between criminal victimization and psychosis appears to generalize across a range of LMICs and, therefore, across nations with a broad range of crime rates, degree of urban development, average per capita income, and racial/ethnic make-up. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Peer victimization and social phobia: a follow-up study among adolescents.

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Ranta, Klaus; Kaltiala-Heino, Riittakerttu; Fröjd, Sari; Marttunen, Mauri

2013-04-01

This study examined longitudinal associations between direct and relational peer victimization (DV/RV) and self-reported social phobia (SP) among adolescents from 15 to 17 years of age, controlling for depression and family socioeconomic covariates. A total of 3,278 Finnish adolescents with a mean age of 15.5 years were surveyed at baseline (T1), and followed up 2 years afterwards (T2) their mean age being 17.6 years. In all, 2,070 adolescents were reached for the follow-up. Both types of victimization were assessed with structured questions, SP with the Social Phobia Inventory, and depression with the 13-item Beck Depression Inventory. Socioeconomic covariates were assessed with items from the Life Events Checklist. Frequency of victimization and SP were assessed at T1 and T2, and incidence and persistence from T1 to T2. Longitudinal associations between victimization and SP were examined with three logistic regression analyses with depression and socioeconomic covariates controlled for, with SP, DV, and RV in turn as the dependent endpoint (T2) variables. Among boys a bidirectional association between DV and SP was found with DV both predicting SP [Odds Ratio (OR) 2.6] and being predicted by SP (OR 3.9). Among girls RV predicted SP (OR 2.8), but not vice versa, while depression in turn predicted DV (OR 4.3). Direct victimization and SP have a bidirectional association among boys, while among girls RV increases the risk of subsequent SP.

Cytopathologic differential diagnosis of low-grade urothelial carcinoma and reactive urothelial proliferation in bladder washings: a logistic regression analysis.

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Cakir, Ebru; Kucuk, Ulku; Pala, Emel Ebru; Sezer, Ozlem; Ekin, Rahmi Gokhan; Cakmak, Ozgur

2017-05-01

Conventional cytomorphologic assessment is the first step to establish an accurate diagnosis in urinary cytology. In cytologic preparations, the separation of low-grade urothelial carcinoma (LGUC) from reactive urothelial proliferation (RUP) can be exceedingly difficult. The bladder washing cytologies of 32 LGUC and 29 RUP were reviewed. The cytologic slides were examined for the presence or absence of the 28 cytologic features. The cytologic criteria showing statistical significance in LGUC were increased numbers of monotonous single (non-umbrella) cells, three-dimensional cellular papillary clusters without fibrovascular cores, irregular bordered clusters, atypical single cells, irregular nuclear overlap, cytoplasmic homogeneity, increased N/C ratio, pleomorphism, nuclear border irregularity, nuclear eccentricity, elongated nuclei, and hyperchromasia (p ˂ 0.05), and the cytologic criteria showing statistical significance in RUP were inflammatory background, mixture of small and large urothelial cells, loose monolayer aggregates, and vacuolated cytoplasm (p ˂ 0.05). When these variables were subjected to a stepwise logistic regression analysis, four features were selected to distinguish LGUC from RUP: increased numbers of monotonous single (non-umbrella) cells, increased nuclear cytoplasmic ratio, hyperchromasia, and presence of small and large urothelial cells (p = 0.0001). By this logistic model of the 32 cases with proven LGUC, the stepwise logistic regression analysis correctly predicted 31 (96.9%) patients with this diagnosis, and of the 29 patients with RUP, the logistic model correctly predicted 26 (89.7%) patients as having this disease. There are several cytologic features to separate LGUC from RUP. Stepwise logistic regression analysis is a valuable tool for determining the most useful cytologic criteria to distinguish these entities. © 2017 APMIS. Published by John Wiley & Sons Ltd.

An investigation of the speeding-related crash designation through crash narrative reviews sampled via logistic regression.

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Fitzpatrick, Cole D; Rakasi, Saritha; Knodler, Michael A

2017-01-01

Speed is one of the most important factors in traffic safety as higher speeds are linked to increased crash risk and higher injury severities. Nearly a third of fatal crashes in the United States are designated as "speeding-related", which is defined as either "the driver behavior of exceeding the posted speed limit or driving too fast for conditions." While many studies have utilized the speeding-related designation in safety analyses, no studies have examined the underlying accuracy of this designation. Herein, we investigate the speeding-related crash designation through the development of a series of logistic regression models that were derived from the established speeding-related crash typologies and validated using a blind review, by multiple researchers, of 604 crash narratives. The developed logistic regression model accurately identified crashes which were not originally designated as speeding-related but had crash narratives that suggested speeding as a causative factor. Only 53.4% of crashes designated as speeding-related contained narratives which described speeding as a causative factor. Further investigation of these crashes revealed that the driver contributing code (DCC) of "driving too fast for conditions" was being used in three separate situations. Additionally, this DCC was also incorrectly used when "exceeding the posted speed limit" would likely have been a more appropriate designation. Finally, it was determined that the responding officer only utilized one DCC in 82% of crashes not designated as speeding-related but contained a narrative indicating speed as a contributing causal factor. The use of logistic regression models based upon speeding-related crash typologies offers a promising method by which all possible speeding-related crashes could be identified. Published by Elsevier Ltd.

Victimization, family environment and the management of the blame-shame on bullying

Directory of Open Access Journals (Sweden)

Manuel Ramírez Zaragoza

2015-11-01

Full Text Available This work aimed to determine to what extent a history of victimization, handling shame-guilt and family climate differentiate students with and without harassing behavior toward peers. 132 students were identified as aggressors and reported an average of three or more aggressive behavior toward peers. A random sample of similar size was taken to complete the final number of participants. Using logistic regression, variables studied pointed significant difference between groups of students with and without aggressive behavior toward peers (R2 = .58. Victimization (OR = 10.76, shame displacement (OR = 1.99 and family conflict (OR = 1.51 increase the probability of belonging to the group of assailants while recognition (OR = 0.62 and family life (OR = 0.60 decrease this probability. It is concluded that is necessary to analyze bullying from an ecological framework considering variables located in the contexts where individuals interact.

The spatial prediction of landslide susceptibility applying artificial neural network and logistic regression models: A case study of Inje, Korea

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Saro, Lee; Woo, Jeon Seong; Kwan-Young, Oh; Moung-Jin, Lee

2016-02-01

The aim of this study is to predict landslide susceptibility caused using the spatial analysis by the application of a statistical methodology based on the GIS. Logistic regression models along with artificial neutral network were applied and validated to analyze landslide susceptibility in Inje, Korea. Landslide occurrence area in the study were identified based on interpretations of optical remote sensing data (Aerial photographs) followed by field surveys. A spatial database considering forest, geophysical, soil and topographic data, was built on the study area using the Geographical Information System (GIS). These factors were analysed using artificial neural network (ANN) and logistic regression models to generate a landslide susceptibility map. The study validates the landslide susceptibility map by comparing them with landslide occurrence areas. The locations of landslide occurrence were divided randomly into a training set (50%) and a test set (50%). A training set analyse the landslide susceptibility map using the artificial network along with logistic regression models, and a test set was retained to validate the prediction map. The validation results revealed that the artificial neural network model (with an accuracy of 80.10%) was better at predicting landslides than the logistic regression model (with an accuracy of 77.05%). Of the weights used in the artificial neural network model, `slope' yielded the highest weight value (1.330), and `aspect' yielded the lowest value (1.000). This research applied two statistical analysis methods in a GIS and compared their results. Based on the findings, we were able to derive a more effective method for analyzing landslide susceptibility.

The spatial prediction of landslide susceptibility applying artificial neural network and logistic regression models: A case study of Inje, Korea

Directory of Open Access Journals (Sweden)

Saro Lee

2016-02-01

Full Text Available The aim of this study is to predict landslide susceptibility caused using the spatial analysis by the application of a statistical methodology based on the GIS. Logistic regression models along with artificial neutral network were applied and validated to analyze landslide susceptibility in Inje, Korea. Landslide occurrence area in the study were identified based on interpretations of optical remote sensing data (Aerial photographs followed by field surveys. A spatial database considering forest, geophysical, soil and topographic data, was built on the study area using the Geographical Information System (GIS. These factors were analysed using artificial neural network (ANN and logistic regression models to generate a landslide susceptibility map. The study validates the landslide susceptibility map by comparing them with landslide occurrence areas. The locations of landslide occurrence were divided randomly into a training set (50% and a test set (50%. A training set analyse the landslide susceptibility map using the artificial network along with logistic regression models, and a test set was retained to validate the prediction map. The validation results revealed that the artificial neural network model (with an accuracy of 80.10% was better at predicting landslides than the logistic regression model (with an accuracy of 77.05%. Of the weights used in the artificial neural network model, ‘slope’ yielded the highest weight value (1.330, and ‘aspect’ yielded the lowest value (1.000. This research applied two statistical analysis methods in a GIS and compared their results. Based on the findings, we were able to derive a more effective method for analyzing landslide susceptibility.

Characterization of sonographically indeterminate ovarian tumors with MR imaging. A logistic regression analysis

International Nuclear Information System (INIS)

Yamashita, Y.; Hatanaka, Y.; Torashima, M.; Takahashi, M.; Miyazaki, K.; Okamura, H.

1997-01-01

Purpose: The goal of this study was to maximize the discrimination between benign and malignant masses in patients with sonographically indeterminate ovarian lesions by means of unenhanced and contrast-enhanced MR imaging, and to develop a computer-assisted diagnosis system. Material and Methods: Findings in precontrast and Gd-DTPA contrast-enhanced MR images of 104 patients with 115 sonographically indeterminate ovarian masses were analyzed, and the results were correlated with histopathological findings. Of 115 lesions, 65 were benign (23 cystadenomas, 13 complex cysts, 11 teratomas, 6 fibrothecomas, 12 others) and 50 were malignant (32 ovarian carcinomas, 7 metastatic tumors of the ovary, 4 carcinomas of the fallopian tubes, 7 others). A logistic regression analysis was performed to discriminate between benign and malignant lesions, and a model of a computer-assisted diagnosis was developed. This model was prospectively tested in 75 cases of ovarian tumors found at other institutions. Results: From the univariate analysis, the following parameters were selected as significant for predicting malignancy (p≤0.05): A solid or cystic mass with a large solid component or wall thickness greater than 3 mm; complex internal architecture; ascites; and bilaterality. Based on these parameters, a model of a computer-assisted diagnosis system was developed with the logistic regression analysis. To distinguish benign from malignant lesions, the maximum cut-off point was obtained between 0.47 and 0.51. In a prospective application of this model, 87% of the lesions were accurately identified as benign or malignant. (orig.)

Application of Logistic Regression Tree Model in Determining Habitat Distribution of Astragalus verus

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M. Saki

2013-03-01

Full Text Available The relationship between plant species and environmental factors has always been a central issue in plant ecology. With rising power of statistical techniques, geo-statistics and geographic information systems (GIS, the development of predictive habitat distribution models of organisms has rapidly increased in ecology. This study aimed to evaluate the ability of Logistic Regression Tree model to create potential habitat map of Astragalus verus. This species produces Tragacanth and has economic value. A stratified- random sampling was applied to 100 sites (50 presence- 50 absence of given species, and produced environmental and edaphic factors maps by using Kriging and Inverse Distance Weighting methods in the ArcGIS software for the whole study area. Relationships between species occurrence and environmental factors were determined by Logistic Regression Tree model and extended to the whole study area. The results indicated species occurrence has strong correlation with environmental factors such as mean daily temperature and clay, EC and organic carbon content of the soil. Species occurrence showed direct relationship with mean daily temperature and clay and organic carbon, and inverse relationship with EC. Model accuracy was evaluated both by Cohen’s kappa statistics (κ and by area under Receiver Operating Characteristics curve based on independent test data set. Their values (kappa=0.9, Auc of ROC=0.96 indicated the high power of LRT to create potential habitat map on local scales. This model, therefore, can be applied to recognize potential sites for rangeland reclamation projects.

Optimization of Game Formats in U-10 Soccer Using Logistic Regression Analysis

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Amatria Mario

2016-12-01

Full Text Available Small-sided games provide young soccer players with better opportunities to develop their skills and progress as individual and team players. There is, however, little evidence on the effectiveness of different game formats in different age groups, and furthermore, these formats can vary between and even within countries. The Royal Spanish Soccer Association replaced the traditional grassroots 7-a-side format (F-7 with the 8-a-side format (F-8 in the 2011-12 season and the country’s regional federations gradually followed suit. The aim of this observational methodology study was to investigate which of these formats best suited the learning needs of U-10 players transitioning from 5-aside futsal. We built a multiple logistic regression model to predict the success of offensive moves depending on the game format and the area of the pitch in which the move was initiated. Success was defined as a shot at the goal. We also built two simple logistic regression models to evaluate how the game format influenced the acquisition of technicaltactical skills. It was found that the probability of a shot at the goal was higher in F-7 than in F-8 for moves initiated in the Creation Sector-Own Half (0.08 vs 0.07 and the Creation Sector-Opponent's Half (0.18 vs 0.16. The probability was the same (0.04 in the Safety Sector. Children also had more opportunities to control the ball and pass or take a shot in the F-7 format (0.24 vs 0.20, and these were also more likely to be successful in this format (0.28 vs 0.19.

Maternal depression and bullying victimization among adolescents: Results from the 2004 Pelotas cohort study.

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Azeredo, Catarina Machado; Santos, Iná S; Barros, Aluísio J D; Barros, Fernando C; Matijasevich, Alicia

2017-10-01

Maternal depression impacts on several detrimental outcomes during a child's life course, and could increase their risk of victimization. This longitudinal study examined the association between antenatal maternal depression, postnatal trajectories, and current maternal depression and offspring bullying victimization at 11 years. We included 3,441 11-year-old adolescents from the 2004 Pelotas Cohort Study. Antenatal maternal depression, postnatal trajectories, and current maternal depression data were assessed during the follow-up waves. Bullying victimization was self-reported by the adolescents. We used ordinal logistic regression to estimate the odds ratio (OR) and 95% confidence intervals (CIs), for the association between maternal depression and offspring bullying victimization. The most prevalent type of bullying was verbal victimization (37.9%). We observed a positive association between antenatal maternal depression, postnatal trajectories, and current maternal depression and physical bullying victimization. Maternal mood symptoms during pregnancy were associated with physical (OR = 1.30, 95%CI = 1.11-1.53), verbal (OR = 1.29, 95%CI = 1.12-1.49), and any victimization (OR = 1.22, 95%CI = 1.05-1.41). Severe current maternal depression was associated with physical (OR = 1.34, 95%CI = 1.10-1.62), social manipulation (OR = 1.29, 95%CI = 1.08-1.53), attacks on property (OR = 1.30, 95%CI = 1.08-1.57) and any victimization (OR = 1.32, 95%CI = 1.12-1.56). Regarding maternal depression trajectories, the "chronic-high" group was associated with higher risk of social manipulation, attacks on property and any victimization, than the "low" group. Our results strengthen the evidence of association between maternal depression and offspring bullying victimization, and physical victimization appears to be the main component. Further studies are warranted to confirm our findings and to elucidate the theoretical pathways for this longitudinal association. © 2017 Wiley

Performance of a New Restricted Biased Estimator in Logistic Regression

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Yasin ASAR

2017-12-01

Full Text Available It is known that the variance of the maximum likelihood estimator (MLE inflates when the explanatory variables are correlated. This situation is called the multicollinearity problem. As a result, the estimations of the model may not be trustful. Therefore, this paper introduces a new restricted estimator (RLTE that may be applied to get rid of the multicollinearity when the parameters lie in some linear subspace  in logistic regression. The mean squared errors (MSE and the matrix mean squared errors (MMSE of the estimators considered in this paper are given. A Monte Carlo experiment is designed to evaluate the performances of the proposed estimator, the restricted MLE (RMLE, MLE and Liu-type estimator (LTE. The criterion of performance is chosen to be MSE. Moreover, a real data example is presented. According to the results, proposed estimator has better performance than MLE, RMLE and LTE.

Physical victimization, gender identity and suicide risk among transgender men and women

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Gia Elise Barboza, PhD

2016-12-01

Full Text Available We investigated whether being attacked physically due to one's gender identity or expression was associated with suicide risk among trans men and women living in Virginia. The sample consisted of 350 transgender men and women who participated in the Virginia Transgender Health Initiative Survey (THIS. Multivariate multinomial logistic regression was used to explore the competing outcomes associated with suicidal risk. Thirty-seven percent of trans men and women experienced at least one physical attack since the age of 13. On average, individuals experienced 3.97 (SD = 2.86 physical attacks; among these about half were attributed to one's gender identity or expression (mean = 2.08, SD = 1.96. In the multivariate multinomial regression, compared to those with no risk, being physically attacked increased the odds of both attempting and contemplating suicide regardless of gender attribution. Nevertheless, the relative impact of physical victimization on suicidal behavior was higher among those who were targeted on the basis of their gender identity or expression. Finally, no significant association was found between multiple measures of institutional discrimination and suicide risk once discriminatory and non-discriminatory physical victimization was taken into account. Trans men and women experience high levels of physical abuse and face multiple forms of discrimination. They are also at an increased risk for suicidal tendencies. Interventions that help transindividuals cope with discrimination and physical victimization simultaneously may be more effective in saving lives.

Physical victimization, gender identity and suicide risk among transgender men and women.

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Barboza, Gia Elise; Dominguez, Silvia; Chance, Elena

2016-12-01

We investigated whether being attacked physically due to one's gender identity or expression was associated with suicide risk among trans men and women living in Virginia. The sample consisted of 350 transgender men and women who participated in the Virginia Transgender Health Initiative Survey (THIS). Multivariate multinomial logistic regression was used to explore the competing outcomes associated with suicidal risk. Thirty-seven percent of trans men and women experienced at least one physical attack since the age of 13. On average, individuals experienced 3.97 (SD = 2.86) physical attacks; among these about half were attributed to one's gender identity or expression (mean = 2.08, SD = 1.96). In the multivariate multinomial regression, compared to those with no risk, being physically attacked increased the odds of both attempting and contemplating suicide regardless of gender attribution. Nevertheless, the relative impact of physical victimization on suicidal behavior was higher among those who were targeted on the basis of their gender identity or expression. Finally, no significant association was found between multiple measures of institutional discrimination and suicide risk once discriminatory and non-discriminatory physical victimization was taken into account. Trans men and women experience high levels of physical abuse and face multiple forms of discrimination. They are also at an increased risk for suicidal tendencies. Interventions that help transindividuals cope with discrimination and physical victimization simultaneously may be more effective in saving lives.

Predictive ability of logistic regression, auto-logistic regression and neural network models in empirical land-use change modeling: a case study

NARCIS (Netherlands)

Lin, Y.P.; Chu, H.J.; Wu, C.F.; Verburg, P.H.

2011-01-01

The objective of this study is to compare the abilities of logistic, auto-logistic and artificial neural network (ANN) models for quantifying the relationships between land uses and their drivers. In addition, the application of the results obtained by the three techniques is tested in a dynamic

Dynamic Network Logistic Regression: A Logistic Choice Analysis of Inter- and Intra-Group Blog Citation Dynamics in the 2004 US Presidential Election

OpenAIRE

Almquist, Zack W.; Butts, Carter T.

2013-01-01

Methods for analysis of network dynamics have seen great progress in the past decade. This article shows how Dynamic Network Logistic Regression techniques (a special case of the Temporal Exponential Random Graph Models) can be used to implement decision theoretic models for network dynamics in a panel data context. We also provide practical heuristics for model building and assessment. We illustrate the power of these techniques by applying them to a dynamic blog network sampled during the 2...

Updated logistic regression equations for the calculation of post-fire debris-flow likelihood in the western United States

Science.gov (United States)

Staley, Dennis M.; Negri, Jacquelyn A.; Kean, Jason W.; Laber, Jayme L.; Tillery, Anne C.; Youberg, Ann M.

2016-06-30

Wildfire can significantly alter the hydrologic response of a watershed to the extent that even modest rainstorms can generate dangerous flash floods and debris flows. To reduce public exposure to hazard, the U.S. Geological Survey produces post-fire debris-flow hazard assessments for select fires in the western United States. We use publicly available geospatial data describing basin morphology, burn severity, soil properties, and rainfall characteristics to estimate the statistical likelihood that debris flows will occur in response to a storm of a given rainfall intensity. Using an empirical database and refined geospatial analysis methods, we defined new equations for the prediction of debris-flow likelihood using logistic regression methods. We showed that the new logistic regression model outperformed previous models used to predict debris-flow likelihood.

For Whom Does Hate Crime Hurt More? A Comparison of Consequences of Victimization Across Motives and Crime Types.

Science.gov (United States)

Mellgren, Caroline; Andersson, Mika; Ivert, Anna-Karin

2017-12-01

Hate crimes have been found to have more severe consequences than other parallel crimes that were not motivated by the offenders' hostility toward someone because of their real or perceived difference. Many countries today have hate crime laws that make it possible to increase the penalties for such crimes. The main critique against hate crime laws is that they punish thoughts. Instead, proponents of hate crime laws argue that sentence enhancement is justified because hate crimes cause greater harm. This study compares consequences of victimization across groups of victims to test for whom hate crimes hurt more. We analyzed data that were collected through questionnaires distributed to almost 3,000 students at Malmö University, Sweden, during 2013. The survey focused on students' exposure to, and experiences of, hate crime. A series of separate logistic regression analyses were performed, which analyzed the likelihood for reporting consequences following a crime depending on crime type, perceived motive, repeat victimization, gender, and age. Analyzed as one victim group, victims of hate crime more often reported any of the consequences following a crime compared with victims of parallel non-hate-motivated crimes. And, overall victims of threat more often reported consequences compared with victims of sexual harassment and minor assault. However, all hate crime victim groups did not report more consequences than the non-hate crime victim group. The results provide grounds for questioning that hate crimes hurt the individual victim more. It seems that hate crimes do not hurt all more but hate crimes hurt some victims of some crimes more in some ways.

An application in identifying high-risk populations in alternative tobacco product use utilizing logistic regression and CART: a heuristic comparison.

Science.gov (United States)

Lei, Yang; Nollen, Nikki; Ahluwahlia, Jasjit S; Yu, Qing; Mayo, Matthew S

2015-04-09

Other forms of tobacco use are increasing in prevalence, yet most tobacco control efforts are aimed at cigarettes. In light of this, it is important to identify individuals who are using both cigarettes and alternative tobacco products (ATPs). Most previous studies have used regression models. We conducted a traditional logistic regression model and a classification and regression tree (CART) model to illustrate and discuss the added advantages of using CART in the setting of identifying high-risk subgroups of ATP users among cigarettes smokers. The data were collected from an online cross-sectional survey administered by Survey Sampling International between July 5, 2012 and August 15, 2012. Eligible participants self-identified as current smokers, African American, White, or Latino (of any race), were English-speaking, and were at least 25 years old. The study sample included 2,376 participants and was divided into independent training and validation samples for a hold out validation. Logistic regression and CART models were used to examine the important predictors of cigarettes + ATP users. The logistic regression model identified nine important factors: gender, age, race, nicotine dependence, buying cigarettes or borrowing, whether the price of cigarettes influences the brand purchased, whether the participants set limits on cigarettes per day, alcohol use scores, and discrimination frequencies. The C-index of the logistic regression model was 0.74, indicating good discriminatory capability. The model performed well in the validation cohort also with good discrimination (c-index = 0.73) and excellent calibration (R-square = 0.96 in the calibration regression). The parsimonious CART model identified gender, age, alcohol use score, race, and discrimination frequencies to be the most important factors. It also revealed interesting partial interactions. The c-index is 0.70 for the training sample and 0.69 for the validation sample. The misclassification

Drought Patterns Forecasting using an Auto-Regressive Logistic Model

Science.gov (United States)

del Jesus, M.; Sheffield, J.; Méndez Incera, F. J.; Losada, I. J.; Espejo, A.

2014-12-01

Drought is characterized by a water deficit that may manifest across a large range of spatial and temporal scales. Drought may create important socio-economic consequences, many times of catastrophic dimensions. A quantifiable definition of drought is elusive because depending on its impacts, consequences and generation mechanism, different water deficit periods may be identified as a drought by virtue of some definitions but not by others. Droughts are linked to the water cycle and, although a climate change signal may not have emerged yet, they are also intimately linked to climate.In this work we develop an auto-regressive logistic model for drought prediction at different temporal scales that makes use of a spatially explicit framework. Our model allows to include covariates, continuous or categorical, to improve the performance of the auto-regressive component.Our approach makes use of dimensionality reduction (principal component analysis) and classification techniques (K-Means and maximum dissimilarity) to simplify the representation of complex climatic patterns, such as sea surface temperature (SST) and sea level pressure (SLP), while including information on their spatial structure, i.e. considering their spatial patterns. This procedure allows us to include in the analysis multivariate representation of complex climatic phenomena, as the El Niño-Southern Oscillation. We also explore the impact of other climate-related variables such as sun spots. The model allows to quantify the uncertainty of the forecasts and can be easily adapted to make predictions under future climatic scenarios. The framework herein presented may be extended to other applications such as flash flood analysis, or risk assessment of natural hazards.

Transmission Risks of Schistosomiasis Japonica: Extraction from Back-propagation Artificial Neural Network and Logistic Regression Model

Science.gov (United States)

Xu, Jun-Fang; Xu, Jing; Li, Shi-Zhu; Jia, Tia-Wu; Huang, Xi-Bao; Zhang, Hua-Ming; Chen, Mei; Yang, Guo-Jing; Gao, Shu-Jing; Wang, Qing-Yun; Zhou, Xiao-Nong

2013-01-01

Background The transmission of schistosomiasis japonica in a local setting is still poorly understood in the lake regions of the People's Republic of China (P. R. China), and its transmission patterns are closely related to human, social and economic factors. Methodology/Principal Findings We aimed to apply the integrated approach of artificial neural network (ANN) and logistic regression model in assessment of transmission risks of Schistosoma japonicum with epidemiological data collected from 2339 villagers from 1247 households in six villages of Jiangling County, P.R. China. By using the back-propagation (BP) of the ANN model, 16 factors out of 27 factors were screened, and the top five factors ranked by the absolute value of mean impact value (MIV) were mainly related to human behavior, i.e. integration of water contact history and infection history, family with past infection, history of water contact, infection history, and infection times. The top five factors screened by the logistic regression model were mainly related to the social economics, i.e. village level, economic conditions of family, age group, education level, and infection times. The risk of human infection with S. japonicum is higher in the population who are at age 15 or younger, or with lower education, or with the higher infection rate of the village, or with poor family, and in the population with more than one time to be infected. Conclusion/Significance Both BP artificial neural network and logistic regression model established in a small scale suggested that individual behavior and socioeconomic status are the most important risk factors in the transmission of schistosomiasis japonica. It was reviewed that the young population (≤15) in higher-risk areas was the main target to be intervened for the disease transmission control. PMID:23556015

Exploring School Victimization and Weapon Carrying Among Military-Connected Lesbian, Gay, Bisexual, and Transgender Youth in California Schools.

Science.gov (United States)

Pedro, Kris Tunac De; Esqueda, Monica Christina

2017-07-01

Military-connected youth often experience daily stressors that affect their academic success and social and emotional development. Stressors such as multiple deployments and frequent school transitions may weaken the social ties that military-connected youth have with school communities, placing them at risk of social alienation and victimization. Within this youth population, military-connected lesbian, gay, bisexual, and transgender (LGBT) youth may be especially at risk of school victimization. However, to the authors' knowledge, no empirical studies have been conducted on the school experiences of military-connected LGBT youth. Drawing from the California Healthy Kids Survey (CHKS; n = 634,978), this study explored school victimization and weapon carrying among military-connected LGBT youth and their peers. Multivariate logistic regression analyses revealed that military connection, LGB identity, and transgender identity were associated with an increased odds of nonphysical victimization, physical violence, and weapon carrying. Military transgender youth were at an increased risk of weapon carrying (adjusted odds ratio [AOR] = 1.63; 95% confidence interval [CI] = [1.23, 2.16]). Future research is needed to explore risk and protective factors influencing school victimization and weapon carrying among military-connected LGBT youth.

A general equation to obtain multiple cut-off scores on a test from multinomial logistic regression.

Science.gov (United States)

Bersabé, Rosa; Rivas, Teresa

2010-05-01

The authors derive a general equation to compute multiple cut-offs on a total test score in order to classify individuals into more than two ordinal categories. The equation is derived from the multinomial logistic regression (MLR) model, which is an extension of the binary logistic regression (BLR) model to accommodate polytomous outcome variables. From this analytical procedure, cut-off scores are established at the test score (the predictor variable) at which an individual is as likely to be in category j as in category j+1 of an ordinal outcome variable. The application of the complete procedure is illustrated by an example with data from an actual study on eating disorders. In this example, two cut-off scores on the Eating Attitudes Test (EAT-26) scores are obtained in order to classify individuals into three ordinal categories: asymptomatic, symptomatic and eating disorder. Diagnoses were made from the responses to a self-report (Q-EDD) that operationalises DSM-IV criteria for eating disorders. Alternatives to the MLR model to set multiple cut-off scores are discussed.

Parental Vaccine Acceptance: A Logistic Regression Model Using Previsit Decisions.

Science.gov (United States)

Lee, Sara; Riley-Behringer, Maureen; Rose, Jeanmarie C; Meropol, Sharon B; Lazebnik, Rina

2017-07-01

This study explores how parents' intentions regarding vaccination prior to their children's visit were associated with actual vaccine acceptance. A convenience sample of parents accompanying 6-week-old to 17-year-old children completed a written survey at 2 pediatric practices. Using hierarchical logistic regression, for hospital-based participants (n = 216), vaccine refusal history ( P < .01) and vaccine decision made before the visit ( P < .05) explained 87% of vaccine refusals. In community-based participants (n = 100), vaccine refusal history ( P < .01) explained 81% of refusals. Over 1 in 5 parents changed their minds about vaccination during the visit. Thirty parents who were previous vaccine refusers accepted current vaccines, and 37 who had intended not to vaccinate choose vaccination. Twenty-nine parents without a refusal history declined vaccines, and 32 who did not intend to refuse before the visit declined vaccination. Future research should identify key factors to nudge parent decision making in favor of vaccination.

Mapping of the DLQI scores to EQ-5D utility values using ordinal logistic regression.

Science.gov (United States)

Ali, Faraz Mahmood; Kay, Richard; Finlay, Andrew Y; Piguet, Vincent; Kupfer, Joerg; Dalgard, Florence; Salek, M Sam

2017-11-01

The Dermatology Life Quality Index (DLQI) and the European Quality of Life-5 Dimension (EQ-5D) are separate measures that may be used to gather health-related quality of life (HRQoL) information from patients. The EQ-5D is a generic measure from which health utility estimates can be derived, whereas the DLQI is a specialty-specific measure to assess HRQoL. To reduce the burden of multiple measures being administered and to enable a more disease-specific calculation of health utility estimates, we explored an established mathematical technique known as ordinal logistic regression (OLR) to develop an appropriate model to map DLQI data to EQ-5D-based health utility estimates. Retrospective data from 4010 patients were randomly divided five times into two groups for the derivation and testing of the mapping model. Split-half cross-validation was utilized resulting in a total of ten ordinal logistic regression models for each of the five EQ-5D dimensions against age, sex, and all ten items of the DLQI. Using Monte Carlo simulation, predicted health utility estimates were derived and compared against those observed. This method was repeated for both OLR and a previously tested mapping methodology based on linear regression. The model was shown to be highly predictive and its repeated fitting demonstrated a stable model using OLR as well as linear regression. The mean differences between OLR-predicted health utility estimates and observed health utility estimates ranged from 0.0024 to 0.0239 across the ten modeling exercises, with an average overall difference of 0.0120 (a 1.6% underestimate, not of clinical importance). This modeling framework developed in this study will enable researchers to calculate EQ-5D health utility estimates from a specialty-specific study population, reducing patient and economic burden.

Effective factors contraceptive use by logistic regression model in Tehran, 1996

Directory of Open Access Journals (Sweden)

Ramezani F

1999-07-01

Full Text Available Despite unwillingness to fertility, about 30% of couples do not use any kind of contraception and this will lead to unwanted pregnancy. In this clinical trial study, 4177 subjects who had at least one alive child, and delivered in one of the 12 university hospitals in Tehran were recruited. This study was conducted in 1996. The questionnaire included some questions about contraceptive use, their attitudes about unwantedness or wantedness of their current pregnancies. Data were analysed using a Logistic Regrassion Model. Results showed that 20.3% of those who had no fertility intention, did not use any kind of contraception methods, 41.1% of the subjects who were using a contraception method before pregnancy, had got pregnant unwantedly. Based on Logistic Regression Model; age, education, previous familiarity of women with contraception methods and husband's education were the most significant factors in contraceptive use. Subjects who were 20 years old and less or 35 years old and more and illeterate subjects were at higher risk for unuse of contraception methods. This risk was not related to the gender of their children that suggests a positive change in their perspectives towards sex and the number of children. It is suggested that health politicians choose an appropriate model to enhance the literacy, education and counseling for the correct usage of contraceptives and prevention of unwanted pregnancy.

Using ROC curves to compare neural networks and logistic regression for modeling individual noncatastrophic tree mortality

Science.gov (United States)

Susan L. King

2003-01-01

The performance of two classifiers, logistic regression and neural networks, are compared for modeling noncatastrophic individual tree mortality for 21 species of trees in West Virginia. The output of the classifier is usually a continuous number between 0 and 1. A threshold is selected between 0 and 1 and all of the trees below the threshold are classified as...

A Logistic Regression Model with a Hierarchical Random Error Term for Analyzing the Utilization of Public Transport

Directory of Open Access Journals (Sweden)

Chong Wei

2015-01-01

Full Text Available Logistic regression models have been widely used in previous studies to analyze public transport utilization. These studies have shown travel time to be an indispensable variable for such analysis and usually consider it to be a deterministic variable. This formulation does not allow us to capture travelers’ perception error regarding travel time, and recent studies have indicated that this error can have a significant effect on modal choice behavior. In this study, we propose a logistic regression model with a hierarchical random error term. The proposed model adds a new random error term for the travel time variable. This term structure enables us to investigate travelers’ perception error regarding travel time from a given choice behavior dataset. We also propose an extended model that allows constraining the sign of this error in the model. We develop two Gibbs samplers to estimate the basic hierarchical model and the extended model. The performance of the proposed models is examined using a well-known dataset.

Differential item functioning (DIF) analyses of health-related quality of life instruments using logistic regression

DEFF Research Database (Denmark)

Scott, Neil W; Fayers, Peter M; Aaronson, Neil K

2010-01-01

Differential item functioning (DIF) methods can be used to determine whether different subgroups respond differently to particular items within a health-related quality of life (HRQoL) subscale, after allowing for overall subgroup differences in that scale. This article reviews issues that arise ...... when testing for DIF in HRQoL instruments. We focus on logistic regression methods, which are often used because of their efficiency, simplicity and ease of application....

Susceptibility assessment of earthquake-triggered landslides in El Salvador using logistic regression

Science.gov (United States)

García-Rodríguez, M. J.; Malpica, J. A.; Benito, B.; Díaz, M.

2008-03-01

This work has evaluated the probability of earthquake-triggered landslide occurrence in the whole of El Salvador, with a Geographic Information System (GIS) and a logistic regression model. Slope gradient, elevation, aspect, mean annual precipitation, lithology, land use, and terrain roughness are the predictor variables used to determine the dependent variable of occurrence or non-occurrence of landslides within an individual grid cell. The results illustrate the importance of terrain roughness and soil type as key factors within the model — using only these two variables the analysis returned a significance level of 89.4%. The results obtained from the model within the GIS were then used to produce a map of relative landslide susceptibility.

Analyzing thresholds and efficiency with hierarchical Bayesian logistic regression.

Science.gov (United States)

Houpt, Joseph W; Bittner, Jennifer L

2018-05-10

Ideal observer analysis is a fundamental tool used widely in vision science for analyzing the efficiency with which a cognitive or perceptual system uses available information. The performance of an ideal observer provides a formal measure of the amount of information in a given experiment. The ratio of human to ideal performance is then used to compute efficiency, a construct that can be directly compared across experimental conditions while controlling for the differences due to the stimuli and/or task specific demands. In previous research using ideal observer analysis, the effects of varying experimental conditions on efficiency have been tested using ANOVAs and pairwise comparisons. In this work, we present a model that combines Bayesian estimates of psychometric functions with hierarchical logistic regression for inference about both unadjusted human performance metrics and efficiencies. Our approach improves upon the existing methods by constraining the statistical analysis using a standard model connecting stimulus intensity to human observer accuracy and by accounting for variability in the estimates of human and ideal observer performance scores. This allows for both individual and group level inferences. Copyright © 2018 Elsevier Ltd. All rights reserved.

Peer victimization and subjective health among students reporting disability or chronic illness in 11 Western countries.

Science.gov (United States)

Sentenac, Mariane; Gavin, Aoife; Gabhainn, Saoirse Nic; Molcho, Michal; Due, Pernille; Ravens-Sieberer, Ulrike; Matos, Margarida Gaspar de; Malkowska-Szkutnik, Agnieszka; Gobina, Inese; Vollebergh, Wilma; Arnaud, Catherine; Godeau, Emmanuelle

2013-06-01

To compare the strength of the association between peer victimization at school and subjective health according to the disability or chronic illness (D/CI) status of students across countries. This study used data from 55 030 students aged 11, 13 and 15 years from 11 countries participating in the 2005-06 Health Behaviour in School-aged Children survey. Self-completed questionnaires were administered in classrooms. Multivariate models of logistic regression (controlled for confounding factors and countries) were used to investigate differences in the association between peer victimization and poor subjective health according to the D/CI status. Overall, 13.5% of the students reported having been bullied at least two or three times a month. The percentage of victims was significantly higher among those reporting D/CI than among others in all countries studied. Victims of bullying were more likely to report poor self-rated health, low life satisfaction and multiple health complaints. However, there were no differences in the associations between peer victimization and subjective health indicators according to the D/CI status. In all countries studied, students reporting D/CI were more likely to report being victims of bullying. Victims of bullying reported more negative subjective health outcomes regardless of their D/CI status. Although inclusive education is currently a major topic of educational policies in most countries, additional efforts should be made to improve the quality of the integration of students with D/CI.

Factors that influence police conceptualizations of girls involved in prostitution in six U.S. cities: child sexual exploitation victims or delinquents?

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Halter, Stephanie

2010-05-01

This study examined how the police conceptualize juveniles involved in prostitution as victims of child sexual exploitation (CSE) or delinquents. Case files from six police agencies in major U.S. cities of 126 youth allegedly involved in prostitution, who were almost entirely girls, provided the data for this inquiry. This study found that 60% of youth in this sample were conceptualized as victims by the police and 40% as offenders. Logistic regression predicted the youths' culpability status as victims. The full model predicted 91% of youth's culpability status correctly and explained 67% of the variance in the youths' culpability status. The police considered youth with greater levels of cooperation, greater presence of identified exploiters, no prior record, and that came to their attention through a report more often as victims. In addition, the police may consider local youth more often as victims. It appears that the police use criminal charges as a paternalistic protective response to detain some of the youth treated as offenders, even though they considered these youth victims. Legislatively mandating this form of CSE as child abuse or adopting a ''secure care'' approach is needed to ensure these youth receive the necessary treatment and services.

Landslide susceptibility assessment using logistic regression and its comparison with a rock mass classification system, along a road section in the northern Himalayas (India)

Science.gov (United States)

Das, Iswar; Sahoo, Sashikant; van Westen, Cees; Stein, Alfred; Hack, Robert

2010-02-01

Landslide studies are commonly guided by ground knowledge and field measurements of rock strength and slope failure criteria. With increasing sophistication of GIS-based statistical methods, however, landslide susceptibility studies benefit from the integration of data collected from various sources and methods at different scales. This study presents a logistic regression method for landslide susceptibility mapping and verifies the result by comparing it with the geotechnical-based slope stability probability classification (SSPC) methodology. The study was carried out in a landslide-prone national highway road section in the northern Himalayas, India. Logistic regression model performance was assessed by the receiver operator characteristics (ROC) curve, showing an area under the curve equal to 0.83. Field validation of the SSPC results showed a correspondence of 72% between the high and very high susceptibility classes with present landslide occurrences. A spatial comparison of the two susceptibility maps revealed the significance of the geotechnical-based SSPC method as 90% of the area classified as high and very high susceptible zones by the logistic regression method corresponds to the high and very high class in the SSPC method. On the other hand, only 34% of the area classified as high and very high by the SSPC method falls in the high and very high classes of the logistic regression method. The underestimation by the logistic regression method can be attributed to the generalisation made by the statistical methods, so that a number of slopes existing in critical equilibrium condition might not be classified as high or very high susceptible zones.

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

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

2015-01-01

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

Logistic regression analysis of factors associated with avascular necrosis of the femoral head following femoral neck fractures in middle-aged and elderly patients.

Science.gov (United States)

Ai, Zi-Sheng; Gao, You-Shui; Sun, Yuan; Liu, Yue; Zhang, Chang-Qing; Jiang, Cheng-Hua

2013-03-01

Risk factors for femoral neck fracture-induced avascular necrosis of the femoral head have not been elucidated clearly in middle-aged and elderly patients. Moreover, the high incidence of screw removal in China and its effect on the fate of the involved femoral head require statistical methods to reflect their intrinsic relationship. Ninety-nine patients older than 45 years with femoral neck fracture were treated by internal fixation between May 1999 and April 2004. Descriptive analysis, interaction analysis between associated factors, single factor logistic regression, multivariate logistic regression, and detailed interaction analysis were employed to explore potential relationships among associated factors. Avascular necrosis of the femoral head was found in 15 cases (15.2 %). Age × the status of implants (removal vs. maintenance) and gender × the timing of reduction were interactive according to two-factor interactive analysis. Age, the displacement of fractures, the quality of reduction, and the status of implants were found to be significant factors in single factor logistic regression analysis. Age, age × the status of implants, and the quality of reduction were found to be significant factors in multivariate logistic regression analysis. In fine interaction analysis after multivariate logistic regression analysis, implant removal was the most important risk factor for avascular necrosis in 56-to-85-year-old patients, with a risk ratio of 26.00 (95 % CI = 3.076-219.747). The middle-aged and elderly have less incidence of avascular necrosis of the femoral head following femoral neck fractures treated by cannulated screws. The removal of cannulated screws can induce a significantly high incidence of avascular necrosis of the femoral head in elderly patients, while a high-quality reduction is helpful to reduce avascular necrosis.

Logistic regression accuracy across different spatial and temporal scales for a wide-ranging species, the marbled murrelet

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Carolyn B. Meyer; Sherri L. Miller; C. John Ralph

2004-01-01

The scale at which habitat variables are measured affects the accuracy of resource selection functions in predicting animal use of sites. We used logistic regression models for a wide-ranging species, the marbled murrelet, (Brachyramphus marmoratus) in a large region in California to address how much changing the spatial or temporal scale of...

Psychosocial profile of bullies, victims, and bully-victims: A cross-sectional study

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Marie eLeiner

2014-01-01

Full Text Available While adverse conditions in a child’s life do not excuse inappropriate behavior, they may cause emotional and behavioral problems that require treatment as a preventive measure to reduce the likelihood of bullying. We aimed to identify differences in the psychosocial profiles of adolescents who classified themselves as bullies, victims, or bully-victims. We performed a cross-sectional study in which data were collected between January 2009 and January 2010 from seven university-based clinics in a large metropolitan area with a predominantly Mexican-American population. We collected data on physical aggression among adolescents who self-categorized into the following groups: uninvolved, bullies, victims, and bully-victims. We determined the psychosocial profiles of the adolescents based on responses to the Youth Self Report (YSR and parent’s responses to the Child Behavior Checklist (CBCL. A one-way analysis of variance and multivariate regression analyses were performed to compare the various components of the psychosocial profiles among the groups. Our analysis of the CBCL and the YSR assessments identified differences between the uninvolved group and one or more of the other groups. No significant differences were observed among the bully, victim, and bully-victim groups based on the CBCL. We did find significant differences among those groups based on the YSR, however. Our results suggest that emotional and behavioral problems exist among bullies, victims, and bully-victims. Therefore, treatment should not focus only on the victims of bullying; treatment is equally important for the other groups (bullies and bully-victims. Failure to adequately treat the underlying problems experienced by all three groups of individuals could allow the problems of bullying to continue.

Trajectories of Intimate Partner Violence Victimization

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Kevin M. Swartout

2012-08-01

Full Text Available Introduction: The purposes of this study were to assess the extent to which latent trajectories of female intimate partner violence (IPV victimization exist; and, if so, use negative childhood experiences to predict trajectory membership.Methods: We collected data from 1,575 women at 5 time-points regarding experiences during adolescence and their 4 years of college. We used latent class growth analysis to fit a series of personcentered, longitudinal models ranging from 1 to 5 trajectories. Once the best-fitting model was selected, we used negative childhood experience variables—sexual abuse, physical abuse, and witnessing domestic violence—to predict most-likely trajectory membership via multinomial logistic regression.Results: A 5-trajectory model best fit the data both statistically and in terms of interpretability. The trajectories across time were interpreted as low or no IPV, low to moderate IPV, moderate to low IPV, high to moderate IPV, and high and increasing IPV, respectively. Negative childhood experiences differentiated trajectory membership, somewhat, with childhood sexual abuse as a consistent predictor of membership in elevated IPV trajectories.Conclusion: Our analyses show how IPV risk changes over time and in different ways. These differential patterns of IPV suggest the need for prevention strategies tailored for women that consider victimization experiences in childhood and early adulthood. [West J Emerg Med. 2012;13(3:272–277.

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

NARCIS (Netherlands)

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

2017-01-01

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

Mediation analysis for logistic regression with interactions: Application of a surrogate marker in ophthalmology

DEFF Research Database (Denmark)

Jensen, Signe Marie; Hauger, Hanne; Ritz, Christian

2018-01-01

Mediation analysis is often based on fitting two models, one including and another excluding a potential mediator, and subsequently quantify the mediated effects by combining parameter estimates from these two models. Standard errors of such derived parameters may be approximated using the delta...... method. For a study evaluating a treatment effect on visual acuity, a binary outcome, we demonstrate how mediation analysis may conveniently be carried out by means of marginally fitted logistic regression models in combination with the delta method. Several metrics of mediation are estimated and results...

Modeling the dynamics of urban growth using multinomial logistic regression: a case study of Jiayu County, Hubei Province, China

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Nong, Yu; Du, Qingyun; Wang, Kun; Miao, Lei; Zhang, Weiwei

2008-10-01

Urban growth modeling, one of the most important aspects of land use and land cover change study, has attracted substantial attention because it helps to comprehend the mechanisms of land use change thus helps relevant policies made. This study applied multinomial logistic regression to model urban growth in the Jiayu county of Hubei province, China to discover the relationship between urban growth and the driving forces of which biophysical and social-economic factors are selected as independent variables. This type of regression is similar to binary logistic regression, but it is more general because the dependent variable is not restricted to two categories, as those previous studies did. The multinomial one can simulate the process of multiple land use competition between urban land, bare land, cultivated land and orchard land. Taking the land use type of Urban as reference category, parameters could be estimated with odds ratio. A probability map is generated from the model to predict where urban growth will occur as a result of the computation.

Evaluation of Logistic Regression and Multivariate Adaptive Regression Spline Models for Groundwater Potential Mapping Using R and GIS

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Soyoung Park

2017-07-01

Full Text Available This study mapped and analyzed groundwater potential using two different models, logistic regression (LR and multivariate adaptive regression splines (MARS, and compared the results. A spatial database was constructed for groundwater well data and groundwater influence factors. Groundwater well data with a high potential yield of ≥70 m3/d were extracted, and 859 locations (70% were used for model training, whereas the other 365 locations (30% were used for model validation. We analyzed 16 groundwater influence factors including altitude, slope degree, slope aspect, plan curvature, profile curvature, topographic wetness index, stream power index, sediment transport index, distance from drainage, drainage density, lithology, distance from fault, fault density, distance from lineament, lineament density, and land cover. Groundwater potential maps (GPMs were constructed using LR and MARS models and tested using a receiver operating characteristics curve. Based on this analysis, the area under the curve (AUC for the success rate curve of GPMs created using the MARS and LR models was 0.867 and 0.838, and the AUC for the prediction rate curve was 0.836 and 0.801, respectively. This implies that the MARS model is useful and effective for groundwater potential analysis in the study area.

Least Square Support Vector Machine Classifier vs a Logistic Regression Classifier on the Recognition of Numeric Digits

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Danilo A. López-Sarmiento

2013-11-01

Full Text Available In this paper is compared the performance of a multi-class least squares support vector machine (LSSVM mc versus a multi-class logistic regression classifier to problem of recognizing the numeric digits (0-9 handwritten. To develop the comparison was used a data set consisting of 5000 images of handwritten numeric digits (500 images for each number from 0-9, each image of 20 x 20 pixels. The inputs to each of the systems were vectors of 400 dimensions corresponding to each image (not done feature extraction. Both classifiers used OneVsAll strategy to enable multi-classification and a random cross-validation function for the process of minimizing the cost function. The metrics of comparison were precision and training time under the same computational conditions. Both techniques evaluated showed a precision above 95 %, with LS-SVM slightly more accurate. However the computational cost if we found a marked difference: LS-SVM training requires time 16.42 % less than that required by the logistic regression model based on the same low computational conditions.

Modelling the risk of Pb and PAH intervention value exceedance in allotment soils by robust logistic regression

International Nuclear Information System (INIS)

Papritz, A.; Reichard, P.U.

2009-01-01

Soils of allotments are often contaminated by heavy metals and persistent organic pollutants. In particular, lead (Pb) and polycyclic aromatic hydrocarbons (PAHs) frequently exceed legal intervention values (IVs). Allotments are popular in European countries; cities may own and let several thousand allotment plots. Assessing soil contamination for all the plots would be very costly. Soil contamination in allotments is often linked to gardening practice and historic land use. Hence, we predict the risk of IV exceedance from attributes that characterize the history and management of allotment areas (age, nearby presence of pollutant sources, prior land use). Robust logistic regression analyses of data of Swiss allotments demonstrate that the risk of IV exceedance can be predicted quite precisely without costly soil analyses. Thus, the new method allows screening many allotments at small costs, and it helps to deploy the resources available for soil contamination surveying more efficiently. - The contamination of allotment soils, expressed as frequency of intervention value exceedance, depends on the age and further attributes of the allotments and can be predicted by logistic regression.

Association Among Television and Computer/Video Game Use, Victimization, and Suicide Risk Among U.S. High School Students.

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Rostad, Whitney L; Basile, Kathleen C; Clayton, Heather B

2018-03-01

With the increasing popularity of mobile Internet devices, the exposure of adolescents to media has significantly increased. There is limited information about associations between the types and frequency of media use and experiences of violence victimization and suicide risk. The current study sought to examine the association of bullying and teen dating violence (TDV) victimization, suicide risk with different types of media use (i.e., television and computer/video game use), and number of total media use hours per school day. Data from the nationally representative 2015 Youth Risk Behavior Survey ( n = 15,624) were used to examine the association between media use and violence victimization and suicide risk. Logistic regression models generated prevalence ratios adjusted for demographic characteristics and substance use behaviors to identify significant associations between media use and victimization and suicide risk, stratified by gender. Media use was associated with TDV victimization for male students only, while media use was related to experiences of bullying and suicide risk for both male and female students. In addition, limited (2 or fewer hours) and excessive (5 or more hours) media use emerged as significant correlates of suicide risk and bullying victimization, with limited media use associated with decreased risk and excessive media use with increased risk. Comprehensive, cross-cutting efforts to prevent different forms of victimization should take into account media use and its potential association with adolescent victimization and suicide risk. The current study results suggest limiting adolescent media use, as part of comprehensive prevention programming, might relate to reductions in risk for victimization and suicide.

Bayesian logistic regression approaches to predict incorrect DRG assignment.

Science.gov (United States)

Suleiman, Mani; Demirhan, Haydar; Boyd, Leanne; Girosi, Federico; Aksakalli, Vural

2018-05-07

Episodes of care involving similar diagnoses and treatments and requiring similar levels of resource utilisation are grouped to the same Diagnosis-Related Group (DRG). In jurisdictions which implement DRG based payment systems, DRGs are a major determinant of funding for inpatient care. Hence, service providers often dedicate auditing staff to the task of checking that episodes have been coded to the correct DRG. The use of statistical models to estimate an episode's probability of DRG error can significantly improve the efficiency of clinical coding audits. This study implements Bayesian logistic regression models with weakly informative prior distributions to estimate the likelihood that episodes require a DRG revision, comparing these models with each other and to classical maximum likelihood estimates. All Bayesian approaches had more stable model parameters than maximum likelihood. The best performing Bayesian model improved overall classification per- formance by 6% compared to maximum likelihood, with a 34% gain compared to random classification, respectively. We found that the original DRG, coder and the day of coding all have a significant effect on the likelihood of DRG error. Use of Bayesian approaches has improved model parameter stability and classification accuracy. This method has already lead to improved audit efficiency in an operational capacity.

Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat—Turkey)

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Yilmaz, Işık

2009-06-01

The purpose of this study is to compare the landslide susceptibility mapping methods of frequency ratio (FR), logistic regression and artificial neural networks (ANN) applied in the Kat County (Tokat—Turkey). Digital elevation model (DEM) was first constructed using GIS software. Landslide-related factors such as geology, faults, drainage system, topographical elevation, slope angle, slope aspect, topographic wetness index (TWI) and stream power index (SPI) were used in the landslide susceptibility analyses. Landslide susceptibility maps were produced from the frequency ratio, logistic regression and neural networks models, and they were then compared by means of their validations. The higher accuracies of the susceptibility maps for all three models were obtained from the comparison of the landslide susceptibility maps with the known landslide locations. However, respective area under curve (AUC) values of 0.826, 0.842 and 0.852 for frequency ratio, logistic regression and artificial neural networks showed that the map obtained from ANN model is more accurate than the other models, accuracies of all models can be evaluated relatively similar. The results obtained in this study also showed that the frequency ratio model can be used as a simple tool in assessment of landslide susceptibility when a sufficient number of data were obtained. Input process, calculations and output process are very simple and can be readily understood in the frequency ratio model, however logistic regression and neural networks require the conversion of data to ASCII or other formats. Moreover, it is also very hard to process the large amount of data in the statistical package.

Increased risk of sadness and suicidality among victims of bullying experiencing additional threats to physical safety.

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Pham, Tammy B; Adesman, Andrew

2017-11-23

Objective To examine, in a nationally-representative sample of high school students, to what extent one or more additional threats to physical safety exacerbates the risk of sadness and suicidality among victims of school and/or cyber-bullying. Methods National data from the 2015 Youth Risk Behavior Survey (YRBS) were analyzed for grades 9-12 (n = 15,624). Victimization groups were characterized by school-bullying and cyber-bullying, with and without additional threats to physical safety: fighting at school, being threatened/injured at school, and skipping school out of fear for one's safety. Outcomes included 2-week sadness and suicidality. Outcomes for victimization groups were compared to non-victims using logistic regression adjusting for sex, grade and race/ethnicity. Results Overall, 20.2% of students were school-bullied, and 15.5% were cyber-bullied in the past year. Compared to non-victims, victims of school-bullying and victims of cyber-bullying (VoCBs) who did not experience additional threats to physical safety were 2.76 and 3.83 times more likely to report 2-week sadness, and 3.39 and 3.27 times more likely to exhibit suicidality, respectively. Conversely, victims of bullying who experienced one or more additional threats to physical safety were successively more likely to report these adverse outcomes. Notably, victims of school-bullying and VoCBs with all three additional risk factors were 13.13 and 17.75 times more likely to exhibit suicidality, respectively. Conclusion Risk of depression symptoms and suicidality among victims of school-bullying and/or cyber-bullying is greatly increased among those who have experienced additional threats to physical safety: fighting at school, being threatened/injured at school and skipping school out of fear for their safety.

Comparison of naïve Bayes and logistic regression for computer-aided diagnosis of breast masses using ultrasound imaging

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Cary, Theodore W.; Cwanger, Alyssa; Venkatesh, Santosh S.; Conant, Emily F.; Sehgal, Chandra M.

2012-03-01

This study compares the performance of two proven but very different machine learners, Naïve Bayes and logistic regression, for differentiating malignant and benign breast masses using ultrasound imaging. Ultrasound images of 266 masses were analyzed quantitatively for shape, echogenicity, margin characteristics, and texture features. These features along with patient age, race, and mammographic BI-RADS category were used to train Naïve Bayes and logistic regression classifiers to diagnose lesions as malignant or benign. ROC analysis was performed using all of the features and using only a subset that maximized information gain. Performance was determined by the area under the ROC curve, Az, obtained from leave-one-out cross validation. Naïve Bayes showed significant variation (Az 0.733 +/- 0.035 to 0.840 +/- 0.029, P machine learning models for characterizing solid breast masses on ultrasound.

Logistic Regression with Multiple Random Effects: A Simulation Study of Estimation Methods and Statistical Packages

Science.gov (United States)

Kim, Yoonsang; Emery, Sherry

2013-01-01

Several statistical packages are capable of estimating generalized linear mixed models and these packages provide one or more of three estimation methods: penalized quasi-likelihood, Laplace, and Gauss-Hermite. Many studies have investigated these methods’ performance for the mixed-effects logistic regression model. However, the authors focused on models with one or two random effects and assumed a simple covariance structure between them, which may not be realistic. When there are multiple correlated random effects in a model, the computation becomes intensive, and often an algorithm fails to converge. Moreover, in our analysis of smoking status and exposure to anti-tobacco advertisements, we have observed that when a model included multiple random effects, parameter estimates varied considerably from one statistical package to another even when using the same estimation method. This article presents a comprehensive review of the advantages and disadvantages of each estimation method. In addition, we compare the performances of the three methods across statistical packages via simulation, which involves two- and three-level logistic regression models with at least three correlated random effects. We apply our findings to a real dataset. Our results suggest that two packages—SAS GLIMMIX Laplace and SuperMix Gaussian quadrature—perform well in terms of accuracy, precision, convergence rates, and computing speed. We also discuss the strengths and weaknesses of the two packages in regard to sample sizes. PMID:24288415

Logistic Regression with Multiple Random Effects: A Simulation Study of Estimation Methods and Statistical Packages.

Science.gov (United States)

Kim, Yoonsang; Choi, Young-Ku; Emery, Sherry

2013-08-01

Several statistical packages are capable of estimating generalized linear mixed models and these packages provide one or more of three estimation methods: penalized quasi-likelihood, Laplace, and Gauss-Hermite. Many studies have investigated these methods' performance for the mixed-effects logistic regression model. However, the authors focused on models with one or two random effects and assumed a simple covariance structure between them, which may not be realistic. When there are multiple correlated random effects in a model, the computation becomes intensive, and often an algorithm fails to converge. Moreover, in our analysis of smoking status and exposure to anti-tobacco advertisements, we have observed that when a model included multiple random effects, parameter estimates varied considerably from one statistical package to another even when using the same estimation method. This article presents a comprehensive review of the advantages and disadvantages of each estimation method. In addition, we compare the performances of the three methods across statistical packages via simulation, which involves two- and three-level logistic regression models with at least three correlated random effects. We apply our findings to a real dataset. Our results suggest that two packages-SAS GLIMMIX Laplace and SuperMix Gaussian quadrature-perform well in terms of accuracy, precision, convergence rates, and computing speed. We also discuss the strengths and weaknesses of the two packages in regard to sample sizes.

Urban Growth Modelling with Artificial Neural Network and Logistic Regression. Case Study: Sanandaj City, Iran

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SASSAN MOHAMMADY

2013-01-01

Full Text Available Cities have shown remarkable growth due to attraction, economic, social and facilities centralization in the past few decades. Population and urban expansion especially in developing countries, led to lack of resources, land use change from appropriate agricultural land to urban land use and marginalization. Under these circumstances, land use activity is a major issue and challenge for town and country planners. Different approaches have been attempted in urban expansion modelling. Artificial Neural network (ANN models are among knowledge-based models which have been used for urban growth modelling. ANNs are powerful tools that use a machine learning approach to quantify and model complex behaviour and patterns. In this research, ANN and logistic regression have been employed for interpreting urban growth modelling. Our case study is Sanandaj city and we used Landsat TM and ETM+ imageries acquired at 2000 and 2006. The dataset used includes distance to main roads, distance to the residence region, elevation, slope, and distance to green space. Percent Area Match (PAM obtained from modelling of these changes with ANN is equal to 90.47% and the accuracy achieved for urban growth modelling with Logistic Regression (LR is equal to 88.91%. Percent Correct Match (PCM and Figure of Merit for ANN method were 91.33% and 59.07% and then for LR were 90.84% and 57.07%, respectively.

Neck-focused panic attacks among Cambodian refugees; a logistic and linear regression analysis.

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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.

PATH ANALYSIS WITH LOGISTIC REGRESSION MODELS : EFFECT ANALYSIS OF FULLY RECURSIVE CAUSAL SYSTEMS OF CATEGORICAL VARIABLES

OpenAIRE

Nobuoki, Eshima; Minoru, Tabata; Geng, Zhi; Department of Medical Information Analysis, Faculty of Medicine, Oita Medical University; Department of Applied Mathematics, Faculty of Engineering, Kobe University; Department of Probability and Statistics, Peking University

2001-01-01

This paper discusses path analysis of categorical variables with logistic regression models. The total, direct and indirect effects in fully recursive causal systems are considered by using model parameters. These effects can be explained in terms of log odds ratios, uncertainty differences, and an inner product of explanatory variables and a response variable. A study on food choice of alligators as a numerical exampleis reanalysed to illustrate the present approach.

Relative accuracy of spatial predictive models for lynx Lynx canadensis derived using logistic regression-AIC, multiple criteria evaluation and Bayesian approaches

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Shelley M. ALEXANDER

2009-02-01

Full Text Available We compared probability surfaces derived using one set of environmental variables in three Geographic Information Systems (GIS-based approaches: logistic regression and Akaike’s Information Criterion (AIC, Multiple Criteria Evaluation (MCE, and Bayesian Analysis (specifically Dempster-Shafer theory. We used lynx Lynx canadensis as our focal species, and developed our environment relationship model using track data collected in Banff National Park, Alberta, Canada, during winters from 1997 to 2000. The accuracy of the three spatial models were compared using a contingency table method. We determined the percentage of cases in which both presence and absence points were correctly classified (overall accuracy, the failure to predict a species where it occurred (omission error and the prediction of presence where there was absence (commission error. Our overall accuracy showed the logistic regression approach was the most accurate (74.51%. The multiple criteria evaluation was intermediate (39.22%, while the Dempster-Shafer (D-S theory model was the poorest (29.90%. However, omission and commission error tell us a different story: logistic regression had the lowest commission error, while D-S theory produced the lowest omission error. Our results provide evidence that habitat modellers should evaluate all three error measures when ascribing confidence in their model. We suggest that for our study area at least, the logistic regression model is optimal. However, where sample size is small or the species is very rare, it may also be useful to explore and/or use a more ecologically cautious modelling approach (e.g. Dempster-Shafer that would over-predict, protect more sites, and thereby minimize the risk of missing critical habitat in conservation plans[Current Zoology 55(1: 28 – 40, 2009].

An Alternative Flight Software Paradigm: Applying Multivariate Logistic Regression to Sense Trigger Conditions using Inaccurate or Scarce Information

Science.gov (United States)

Smith, Kelly; Gay, Robert; Stachowiak, Susan

2013-01-01

In late 2014, NASA will fly the Orion capsule on a Delta IV-Heavy rocket for the Exploration Flight Test-1 (EFT-1) mission. For EFT-1, the Orion capsule will be flying with a new GPS receiver and new navigation software. Given the experimental nature of the flight, the flight software must be robust to the loss of GPS measurements. Once the high-speed entry is complete, the drogue parachutes must be deployed within the proper conditions to stabilize the vehicle prior to deploying the main parachutes. When GPS is available in nominal operations, the vehicle will deploy the drogue parachutes based on an altitude trigger. However, when GPS is unavailable, the navigated altitude errors become excessively large, driving the need for a backup barometric altimeter to improve altitude knowledge. In order to increase overall robustness, the vehicle also has an alternate method of triggering the parachute deployment sequence based on planet-relative velocity if both the GPS and the barometric altimeter fail. However, this backup trigger results in large altitude errors relative to the targeted altitude. Motivated by this challenge, this paper demonstrates how logistic regression may be employed to semi-automatically generate robust triggers based on statistical analysis. Logistic regression is used as a ground processor pre-flight to develop a statistical classifier. The classifier would then be implemented in flight software and executed in real-time. This technique offers improved performance even in the face of highly inaccurate measurements. Although the logistic regression-based trigger approach will not be implemented within EFT-1 flight software, the methodology can be carried forward for future missions and vehicles

Reporting Crime Victimizations to the Police and the Incidence of Future Victimizations: A Longitudinal Study.

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Ranapurwala, Shabbar I; Berg, Mark T; Casteel, Carri

2016-01-01

Law enforcement depends on cooperation from the public and crime victims to protect citizens and maintain public safety; however, many crimes are not reported to police because of fear of repercussions or because the crime is considered trivial. It is unclear how police reporting affects the incidence of future victimization. To evaluate the association between reporting victimization to police and incident future victimization. We conducted a retrospective cohort study using National Crime Victimization Survey 2008-2012 data. Participants were 12+ years old household members who may or may not be victimized, were followed biannually for 3 years, and who completed at least one follow-up survey after their first reported victimization between 2008 and 2012. Crude and adjusted generalized linear mixed regression for survey data with Poisson link were used to compare rates of future victimization. Out of 18,657 eligible participants, 41% participants reported to their initial victimization to police and had a future victimization rate of 42.8/100 person-years (PY) (95% CI: 40.7, 44.8). The future victimization rate of those who did not report to the police (59%) was 55.0/100 PY (95% CI: 53.0, 57.0). The adjusted rate ratio comparing police reporting to not reporting was 0.78 (95%CI: 0.72, 0.84) for all future victimizations, 0.80 (95% CI: 0.72, 0.90) for interpersonal violence, 0.73 (95% CI: 0.68, 0.78) for thefts, and 0.95 (95% CI: 0.84, 1.07) for burglaries. Reporting victimization to police is associated with fewer future victimization, underscoring the importance of police reporting in crime prevention. This association may be attributed to police action and victim services provisions resulting from reporting.

AN APPLICATION OF THE LOGISTIC REGRESSION MODEL IN THE EXPERIMENTAL PHYSICAL CHEMISTRY

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Elpidio Corral-López

2015-06-01

Full Text Available The calculation of intensive properties molar volumes of ethanol-water mixtures by experimental densities and tangent method in the Physical Chemistry Laboratory presents the problem of making manually the molar volume curve versus mole fraction and the trace of the tangent line trace. The advantage of using a statistical model the Logistic Regression on a Texas VOYAGE graphing calculator allowed trace the curve and the tangents in situ, and also evaluate the students work during the experimental session. The error percentage between the molar volumes calculated using literature data and those obtained with statistical method is minimal, which validates the model. It is advantageous use the calculator with this application as a teaching support tool, reducing the evaluation time of 3 weeks to 3 hours.

Dating Violence Victimization Among High School Students in Minnesota: Associations With Family Violence, Unsafe Schools, and Resources for Support.

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Earnest, Alicia A; Brady, Sonya S

2016-02-01

The present study examines whether being a victim of violence by an adult in the household, witnessing intra-familial physical violence, and feeling unsafe at school are associated with physical dating violence victimization. It also examines whether extracurricular activity involvement and perceived care by parents, teachers, and friends attenuate those relationships, consistent with a stress-buffering model. Participants were 75,590 ninth-and twelfth-grade students (51% female, 77% White, 24% receiving free/reduced price lunch) who completed the 2010 Minnesota Student Survey. Overall, 8.5% of students reported being victims of dating violence. Significant differences were found by gender, grade, ethnicity, and free/reduced price lunch status. Logistic regression analyses demonstrated that being a victim of violence by an adult in the household, witnessing intra-familial physical violence, feeling unsafe at school, and low perceived care by parents were strongly associated with dating violence victimization. Associations of moderate strength were found for low perceived care by teachers and friends. Little to no extracurricular activity involvement was weakly associated with dating violence victimization. Attenuating effects of perceived care and extracurricular activity involvement on associations between risk factors (victimization by a family adult, witnessing intra-familial violence, feeling unsafe at school) and dating violence victimization were smaller in magnitude than main effects. Findings are thus more consistent with an additive model of risk and protective factors in relation to dating violence victimization than a stress-buffering model. Health promotion efforts should attempt to minimize family violence exposure, create safer school environments, and encourage parental involvement and support. © The Author(s) 2014.

The Intersection of Gender Identity and Violence: Victimization Experienced by Transgender College Students.

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Griner, Stacey B; Vamos, Cheryl A; Thompson, Erika L; Logan, Rachel; Vázquez-Otero, Coralia; Daley, Ellen M

2017-08-01

College students disproportionately experience victimization, stalking, and relationship violence when compared with other groups. Few studies explore victimization by the gender identity of college students, including those who identify as transgender. The purpose of this study is to explore the rates of violence experienced by transgender students compared with male and female college students. This study utilized the National College Health Assessment-II (NCHA-II) and included data from students ( n = 82,538) across fall 2011, 2012, and 2013. Bivariate statistics and binary logistic regression were conducted to test the relationships between gender identity and victimization. Transgender students ( n = 204) were compared with male ( n = 27,322) and female ( n = 55,012) students. After adjusting for individual factors, transgender students had higher odds of experiencing all nine types of violence when compared with males and higher odds of experiencing eight types of violence than females. Transgender students experienced the highest odds in crimes involving sexual victimization, including attempted sexual penetration (adjusted odds ratio [aOR]: 9.49, 95% confidence interval [CI] = [6.17, 14.59], d = 1.00), sexual penetration without consent (aOR: 9.06, 95% CI = [5.64, 14.53], d = 0.94), and being in a sexually abusive relationship (aOR: 6.48, 95% CI = [4.01, 10.49], d = 0.48), than did male students. Findings reveal increased odds of victimization among transgender students when compared with male and female students. Results demonstrate the need for more comprehensive violence prevention efforts in college settings.

Gaussian Process Regression Model in Spatial Logistic Regression

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Sofro, A.; Oktaviarina, A.

2018-01-01

Spatial analysis has developed very quickly in the last decade. One of the favorite approaches is based on the neighbourhood of the region. Unfortunately, there are some limitations such as difficulty in prediction. Therefore, we offer Gaussian process regression (GPR) to accommodate the issue. In this paper, we will focus on spatial modeling with GPR for binomial data with logit link function. The performance of the model will be investigated. We will discuss the inference of how to estimate the parameters and hyper-parameters and to predict as well. Furthermore, simulation studies will be explained in the last section.

Prediction of spatial patterns of collapsed pipes in loess-derived soils in a temperate humid climate using logistic regression

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Verachtert, E.; Den Eeckhaut, M. Van; Poesen, J.; Govers, G.; Deckers, J.

2011-07-01

Soil piping (tunnel erosion) has been recognised as an important erosion process in collapsible loess-derived soils of temperate humid climates, which can cause collapse of the topsoil and formation of discontinuous gullies. Information about the spatial patterns of collapsed pipes and regional models describing these patterns is still limited. Therefore, this study aims at better understanding the factors controlling the spatial distribution and predicting pipe collapse. A dataset with parcels suffering from collapsed pipes (n = 560) and parcels without collapsed pipes was obtained through a regional survey in a 236 km² study area in the Flemish Ardennes (Belgium). Logistic regression was applied to find the best model describing the relationship between the presence/absence of a collapsed pipe and a set of independent explanatory variables (i.e. slope gradient, drainage area, distance-to-thalweg, curvature, aspect, soil type and lithology). Special attention was paid to the selection procedure of the grid cells without collapsed pipes. Apart from the first piping susceptibility map created by logistic regression modelling, a second map was made based on topographical thresholds of slope gradient and upslope drainage area. The logistic regression model allowed identification of the most important factors controlling pipe collapse. Pipes are much more likely to occur when a topographical threshold depending on both slope gradient and upslope area is exceeded in zones with a sufficient water supply (due to topographical convergence and/or the presence of a clay-rich lithology). On the other hand, the use of slope-area thresholds only results in reasonable predictions of piping susceptibility, with minimum information.

The Effect of Latent Binary Variables on the Uncertainty of the Prediction of a Dichotomous Outcome Using Logistic Regression Based Propensity Score Matching.

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Szekér, Szabolcs; Vathy-Fogarassy, Ágnes

2018-01-01

Logistic regression based propensity score matching is a widely used method in case-control studies to select the individuals of the control group. This method creates a suitable control group if all factors affecting the output variable are known. However, if relevant latent variables exist as well, which are not taken into account during the calculations, the quality of the control group is uncertain. In this paper, we present a statistics-based research in which we try to determine the relationship between the accuracy of the logistic regression model and the uncertainty of the dependent variable of the control group defined by propensity score matching. Our analyses show that there is a linear correlation between the fit of the logistic regression model and the uncertainty of the output variable. In certain cases, a latent binary explanatory variable can result in a relative error of up to 70% in the prediction of the outcome variable. The observed phenomenon calls the attention of analysts to an important point, which must be taken into account when deducting conclusions.

Cyber and Traditional Bullying Victimization as a Risk Factor for Mental Health Problems and Suicidal Ideation in Adolescents

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Bannink, Rienke; Broeren, Suzanne; van de Looij – Jansen, Petra M.; de Waart, Frouwkje G.; Raat, Hein

2014-01-01

Purpose To examine whether traditional and cyber bullying victimization were associated with adolescent's mental health problems and suicidal ideation at two-year follow-up. Gender differences were explored to determine whether bullying affects boys and girls differently. Methods A two-year longitudinal study was conducted among first-year secondary school students (N = 3181). Traditional and cyber bullying victimization were assessed at baseline, whereas mental health status and suicidal ideation were assessed at baseline and follow-up by means of self-report questionnaires. Logistic regression analyses were conducted to assess associations between these variables while controlling for baseline problems. Additionally, we tested whether gender differences in mental health and suicidal ideation were present for the two types of bullying. Results There was a significant interaction between gender and traditional bullying victimization and between gender and cyber bullying victimization on mental health problems. Among boys, traditional and cyber bullying victimization were not related to mental health problems after controlling for baseline mental health. Among girls, both traditional and cyber bullying victimization were associated with mental health problems after controlling for baseline mental health. No significant interaction between gender and traditional or cyber bullying victimization on suicidal ideation was found. Traditional bullying victimization was associated with suicidal ideation, whereas cyber bullying victimization was not associated with suicidal ideation after controlling for baseline suicidal ideation. Conclusions Traditional bullying victimization is associated with an increased risk of suicidal ideation, whereas traditional, as well as cyber bullying victimization is associated with an increased risk of mental health problems among girls. These findings stress the importance of programs aimed at reducing bullying behavior, especially

Cyber and traditional bullying victimization as a risk factor for mental health problems and suicidal ideation in adolescents.

Science.gov (United States)

Bannink, Rienke; Broeren, Suzanne; van de Looij-Jansen, Petra M; de Waart, Frouwkje G; Raat, Hein

2014-01-01

To examine whether traditional and cyber bullying victimization were associated with adolescent's mental health problems and suicidal ideation at two-year follow-up. Gender differences were explored to determine whether bullying affects boys and girls differently. A two-year longitudinal study was conducted among first-year secondary school students (N = 3181). Traditional and cyber bullying victimization were assessed at baseline, whereas mental health status and suicidal ideation were assessed at baseline and follow-up by means of self-report questionnaires. Logistic regression analyses were conducted to assess associations between these variables while controlling for baseline problems. Additionally, we tested whether gender differences in mental health and suicidal ideation were present for the two types of bullying. There was a significant interaction between gender and traditional bullying victimization and between gender and cyber bullying victimization on mental health problems. Among boys, traditional and cyber bullying victimization were not related to mental health problems after controlling for baseline mental health. Among girls, both traditional and cyber bullying victimization were associated with mental health problems after controlling for baseline mental health. No significant interaction between gender and traditional or cyber bullying victimization on suicidal ideation was found. Traditional bullying victimization was associated with suicidal ideation, whereas cyber bullying victimization was not associated with suicidal ideation after controlling for baseline suicidal ideation. Traditional bullying victimization is associated with an increased risk of suicidal ideation, whereas traditional, as well as cyber bullying victimization is associated with an increased risk of mental health problems among girls. These findings stress the importance of programs aimed at reducing bullying behavior, especially because early-onset mental health problems

Cyber and traditional bullying victimization as a risk factor for mental health problems and suicidal ideation in adolescents.

Directory of Open Access Journals (Sweden)

Rienke Bannink

Full Text Available PURPOSE: To examine whether traditional and cyber bullying victimization were associated with adolescent's mental health problems and suicidal ideation at two-year follow-up. Gender differences were explored to determine whether bullying affects boys and girls differently. METHODS: A two-year longitudinal study was conducted among first-year secondary school students (N = 3181. Traditional and cyber bullying victimization were assessed at baseline, whereas mental health status and suicidal ideation were assessed at baseline and follow-up by means of self-report questionnaires. Logistic regression analyses were conducted to assess associations between these variables while controlling for baseline problems. Additionally, we tested whether gender differences in mental health and suicidal ideation were present for the two types of bullying. RESULTS: There was a significant interaction between gender and traditional bullying victimization and between gender and cyber bullying victimization on mental health problems. Among boys, traditional and cyber bullying victimization were not related to mental health problems after controlling for baseline mental health. Among girls, both traditional and cyber bullying victimization were associated with mental health problems after controlling for baseline mental health. No significant interaction between gender and traditional or cyber bullying victimization on suicidal ideation was found. Traditional bullying victimization was associated with suicidal ideation, whereas cyber bullying victimization was not associated with suicidal ideation after controlling for baseline suicidal ideation. CONCLUSIONS: Traditional bullying victimization is associated with an increased risk of suicidal ideation, whereas traditional, as well as cyber bullying victimization is associated with an increased risk of mental health problems among girls. These findings stress the importance of programs aimed at reducing bullying

Bullying Victimization Among Chinese Middle School Students: The Role of Family Violence.

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Zhu, Yuhong; Chan, Ko Ling; Chen, Jinsong

2018-06-01

This study used the data from a representative sample to investigate the association between family violence (FV) and child bullying victimization (BV) in Xi'an city, China. Data on social demographic information and the prevalence of BV and FV were collected from a randomly selected sample with 3,175 middle school students aged 15 to 17 by self-administrated questionnaires. Results show that 55.9% and 30.3% of the participants have witnessed intimate partner violence (IPV), 37.7% and 30.8% have been victims of child abuse, and 54.9% and 44.6% have been bullied in a lifetime and in the preceding year, respectively. The lifetime and preceding-year co-occurrence rate of FV and BV are 45% and 30.4%, respectively. Multiple logistic regressions confirm FV as a unique risk factor in predicting both direct and relational BV after controlling for a number of confounding factors. This study suggests that FV experiences should be included in the screening and assessment of risk for child BV.

Comparative study of biodegradability prediction of chemicals using decision trees, functional trees, and logistic regression.

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Chen, Guangchao; Li, Xuehua; Chen, Jingwen; Zhang, Ya-Nan; Peijnenburg, Willie J G M

2014-12-01

Biodegradation is the principal environmental dissipation process of chemicals. As such, it is a dominant factor determining the persistence and fate of organic chemicals in the environment, and is therefore of critical importance to chemical management and regulation. In the present study, the authors developed in silico methods assessing biodegradability based on a large heterogeneous set of 825 organic compounds, using the techniques of the C4.5 decision tree, the functional inner regression tree, and logistic regression. External validation was subsequently carried out by 2 independent test sets of 777 and 27 chemicals. As a result, the functional inner regression tree exhibited the best predictability with predictive accuracies of 81.5% and 81.0%, respectively, on the training set (825 chemicals) and test set I (777 chemicals). Performance of the developed models on the 2 test sets was subsequently compared with that of the Estimation Program Interface (EPI) Suite Biowin 5 and Biowin 6 models, which also showed a better predictability of the functional inner regression tree model. The model built in the present study exhibits a reasonable predictability compared with existing models while possessing a transparent algorithm. Interpretation of the mechanisms of biodegradation was also carried out based on the models developed. © 2014 SETAC.

Characteristics of family violence victims presenting to emergency departments in Hong Kong.

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Chan, Ko Ling; Choi, Wai Man Anna; Fong, Daniel Y T; Chow, Chun Bong; Leung, Ming; Ip, Patrick

2013-01-01

The Emergency Department (ED) has been shown to be a valuable location to screen for family violence. To investigate the characteristics of family violence victims presenting to EDs in a Chinese population in Hong Kong. This study examined a retrospective cohort of patients presenting to the Accident and Emergency Departments of three regional hospitals in the Kwai Tsing district of Hong Kong for evaluation and management of physical injuries related to family violence during the period of January 1, 1997 to December 31, 2008. A total of 15,797 patients were assessed. The sample comprised cases of intimate partner violence (IPV; n=10,839), child abuse and neglect (CAN; n=3491), and elder abuse (EA; n=1467). Gender differences were found in patterns of ED utilization among the patients. The rates of readmission were 12.9% for IPV, 12.8% for CAN, and 8.9% for EA. Logistic regression showed that being male, being discharged against medical advice, and arriving at the hospital via ambulance were the common factors associated with readmission to the EDs for patients victimized by IPV and CAN. This study investigates the victim profile of a large cohort of a Chinese population, providing a unique data set not previously released in this cultural or medical system. The findings give insights to early identification of victims of family violence in the EDs and suggest that screening techniques focused on multiple forms of family violence would improve identification of violence cases. Multidisciplinary collaboration between health, legal, and social service professionals is also warranted to meet the various needs of victims and to reduce hospital readmissions. Copyright © 2013 Elsevier Inc. All rights reserved.

Logistic regression analysis of psychosocial correlates associated with recovery from schizophrenia in a Chinese community.

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Tse, Samson; Davidson, Larry; Chung, Ka-Fai; Yu, Chong Ho; Ng, King Lam; Tsoi, Emily

2015-02-01

More mental health services are adopting the recovery paradigm. This study adds to prior research by (a) using measures of stages of recovery and elements of recovery that were designed and validated in a non-Western, Chinese culture and (b) testing which demographic factors predict advanced recovery and whether placing importance on certain elements predicts advanced recovery. We examined recovery and factors associated with recovery among 75 Hong Kong adults who were diagnosed with schizophrenia and assessed to be in clinical remission. Data were collected on socio-demographic factors, recovery stages and elements associated with recovery. Logistic regression analysis was used to identify variables that could best predict stages of recovery. Receiver operating characteristic curves were used to detect the classification accuracy of the model (i.e. rates of correct classification of stages of recovery). Logistic regression results indicated that stages of recovery could be distinguished with reasonable accuracy for Stage 3 ('living with disability', classification accuracy = 75.45%) and Stage 4 ('living beyond disability', classification accuracy = 75.50%). However, there was no sufficient information to predict Combined Stages 1 and 2 ('overwhelmed by disability' and 'struggling with disability'). It was found that having a meaningful role and age were the most important differentiators of recovery stage. Preliminary findings suggest that adopting salient life roles personally is important to recovery and that this component should be incorporated into mental health services. © The Author(s) 2014.

Modelling Status Food Security Households Disease Sufferers Pulmonary Tuberculosis Uses the Method Regression Logistics Binary

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Wulandari, S. P.; Salamah, M.; Rositawati, A. F. D.

2018-04-01

Food security is the condition where the food fulfilment is managed well for the country till the individual. Indonesia is one of the country which has the commitment to create the food security becomes main priority. However, the food necessity becomes common thing means that it doesn’t care about nutrient standard and the health condition of family member, so in the fulfilment of food necessity also has to consider the disease suffered by the family member, one of them is pulmonary tuberculosa. From that reasons, this research is conducted to know the factors which influence on household food security status which suffered from pulmonary tuberculosis in the coastal area of Surabaya by using binary logistic regression method. The analysis result by using binary logistic regression shows that the variables wife latest education, house density and spacious house ventilation significantly affect on household food security status which suffered from pulmonary tuberculosis in the coastal area of Surabaya, where the wife education level is University/equivalent, the house density is eligible or 8 m2/person and spacious house ventilation 10% of the floor area has the opportunity to become food secure households amounted to 0.911089. While the chance of becoming food insecure households amounted to 0.088911. The model household food security status which suffered from pulmonary tuberculosis in the coastal area of Surabaya has been conformable, and the overall percentages of those classifications are at 71.8%.

Brief report: Associations between in-person and electronic bullying victimization and missing school because of safety concerns among U.S. high school students.

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Steiner, Riley J; Rasberry, Catherine N

2015-08-01

Although associations between bullying and health risk behaviors are well-documented, research on bullying and education-related outcomes, including school attendance, is limited. This study examines associations between bullying victimization (in-person and electronic) and missing school because of safety concerns among a nationally representative sample of U.S. high school students. We used logistic regression analyses to analyze data from the 2013 national Youth Risk Behavior Survey of students in grades 9-12. In-person and electronic victimization were each associated with increased odds of missing school due to safety concerns compared to no bullying victimization. Having been bullied both in-person and electronically was associated with greater odds of missing school compared to electronic bullying only for female students and in-person bullying only for male students. Collaborations between health professionals and educators to prevent bullying may improve school attendance. Published by Elsevier Ltd.

A logistic regression analysis of factors related to the treatment compliance of infertile patients with polycystic ovary syndrome.

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Li, Saijiao; He, Aiyan; Yang, Jing; Yin, TaiLang; Xu, Wangming

2011-01-01

To investigate factors that can affect compliance with treatment of polycystic ovary syndrome (PCOS) in infertile patients and to provide a basis for clinical treatment, specialist consultation and health education. Patient compliance was assessed via a questionnaire based on the Morisky-Green test and the treatment principles of PCOS. Then interviews were conducted with 99 infertile patients diagnosed with PCOS at Renmin Hospital of Wuhan University in China, from March to September 2009. Finally, these data were analyzed using logistic regression analysis. Logistic regression analysis revealed that a total of 23 (25.6%) of the participants showed good compliance. Factors that significantly (p < 0.05) affected compliance with treatment were the patient's body mass index, convenience of medical treatment and concerns about adverse drug reactions. Patients who are obese, experience inconvenient medical treatment or are concerned about adverse drug reactions are more likely to exhibit noncompliance. Treatment education and intervention aimed at these patients should be strengthened in the clinic to improve treatment compliance. Further research is needed to better elucidate the compliance behavior of patients with PCOS.

Widen NomoGram for multinomial logistic regression: an application to staging liver fibrosis in chronic hepatitis C patients.

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Ardoino, Ilaria; Lanzoni, Monica; Marano, Giuseppe; Boracchi, Patrizia; Sagrini, Elisabetta; Gianstefani, Alice; Piscaglia, Fabio; Biganzoli, Elia M

2017-04-01

The interpretation of regression models results can often benefit from the generation of nomograms, 'user friendly' graphical devices especially useful for assisting the decision-making processes. However, in the case of multinomial regression models, whenever categorical responses with more than two classes are involved, nomograms cannot be drawn in the conventional way. Such a difficulty in managing and interpreting the outcome could often result in a limitation of the use of multinomial regression in decision-making support. In the present paper, we illustrate the derivation of a non-conventional nomogram for multinomial regression models, intended to overcome this issue. Although it may appear less straightforward at first sight, the proposed methodology allows an easy interpretation of the results of multinomial regression models and makes them more accessible for clinicians and general practitioners too. Development of prediction model based on multinomial logistic regression and of the pertinent graphical tool is illustrated by means of an example involving the prediction of the extent of liver fibrosis in hepatitis C patients by routinely available markers.

Receipt of Post-Rape Medical Care in a National Sample of Female Victims

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Zinzow, Heidi M.; Resnick, Heidi S.; Barr, Simone C.; Danielson, Carla K.; Kilpatrick, Dean G.

2014-01-01

Background It is important for rape victims to receive medical care to prevent and treat rape-related diseases and injuries, access forensic exams, and connect to needed resources. Few victims seek care, and factors associated with post-rape medical care–seeking are poorly understood. Purpose The current study examined prevalence and factors associated with post-rape medical care–seeking in a national sample of women who reported a most-recent or only incident of forcible rape, and drug- or alcohol-facilitated/incapacitated rape when they were aged ≥14 years. Methods A national sample of U.S. adult women (N=3001) completed structured telephone interviews in 2006, and data for this study were analyzed in 2011. Logistic regression analyses examined demographic variables, health, rape characteristics, and post-rape concerns in relation to post-rape medical care–seeking among 445 female rape victims. Results A minority of rape victims (21%) sought post-rape medical attention following the incident. In the final multivariate model, correlates of medical care included black race, rape-related injury, concerns about sexually transmitted diseases, pregnancy concerns, and reporting the incident to police. Conclusions Women who experience rapes consistent with stereotypic scenarios, acknowledge the rape, report the rape, and harbor health concerns appear to be more likely to seek post-rape medical services. Education is needed to increase rape acknowledgment, awareness of post-rape services that do not require formal reporting, and recognition of the need to treat rape-related health problems. PMID:22813683

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

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

2014-09-17

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

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

International Nuclear Information System (INIS)

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

2015-01-01

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

Analysis of an environmental exposure health questionnaire in a metropolitan minority population utilizing logistic regression and Support Vector Machines.

Science.gov (United States)

Chen, Chau-Kuang; Bruce, Michelle; Tyler, Lauren; Brown, Claudine; Garrett, Angelica; Goggins, Susan; Lewis-Polite, Brandy; Weriwoh, Mirabel L; Juarez, Paul D; Hood, Darryl B; Skelton, Tyler

2013-02-01

The goal of this study was to analyze a 54-item instrument for assessment of perception of exposure to environmental contaminants within the context of the built environment, or exposome. This exposome was defined in five domains to include 1) home and hobby, 2) school, 3) community, 4) occupation, and 5) exposure history. Interviews were conducted with child-bearing-age minority women at Metro Nashville General Hospital at Meharry Medical College. Data were analyzed utilizing DTReg software for Support Vector Machine (SVM) modeling followed by an SPSS package for a logistic regression model. The target (outcome) variable of interest was respondent's residence by ZIP code. The results demonstrate that the rank order of important variables with respect to SVM modeling versus traditional logistic regression models is almost identical. This is the first study documenting that SVM analysis has discriminate power for determination of higher-ordered spatial relationships on an environmental exposure history questionnaire.

The Overall Odds Ratio as an Intuitive Effect Size Index for Multiple Logistic Regression: Examination of Further Refinements

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Le, Huy; Marcus, Justin

2012-01-01

This study used Monte Carlo simulation to examine the properties of the overall odds ratio (OOR), which was recently introduced as an index for overall effect size in multiple logistic regression. It was found that the OOR was relatively independent of study base rate and performed better than most commonly used R-square analogs in indexing model…

Desertification Susceptibility Mapping Using Logistic Regression Analysis in the Djelfa Area, Algeria

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Farid Djeddaoui

2017-10-01

Full Text Available The main goal of this work was to identify the areas that are most susceptible to desertification in a part of the Algerian steppe, and to quantitatively assess the key factors that contribute to this desertification. In total, 139 desertified zones were mapped using field surveys and photo-interpretation. We selected 16 spectral and geomorphic predictive factors, which a priori play a significant role in desertification. They were mainly derived from Landsat 8 imagery and Shuttle Radar Topographic Mission digital elevation model (SRTM DEM. Some factors, such as the topographic position index (TPI and curvature, were used for the first time in this kind of study. For this purpose, we adapted the logistic regression algorithm for desertification susceptibility mapping, which has been widely used for landslide susceptibility mapping. The logistic model was evaluated using the area under the receiver operating characteristic (ROC curve. The model accuracy was 87.8%. We estimated the model uncertainties using a bootstrap method. Our analysis suggests that the predictive model is robust and stable. Our results indicate that land cover factors, including normalized difference vegetation index (NDVI and rangeland classes, play a major role in determining desertification occurrence, while geomorphological factors have a limited impact. The predictive map shows that 44.57% of the area is classified as highly to very highly susceptible to desertification. The developed approach can be used to assess desertification in areas with similar characteristics and to guide possible actions to combat desertification.

Predicting Factors of INSURE Failure in Low Birth Weight Neonates with RDS; A Logistic Regression Model

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Bita Najafian

2015-02-01

Full Text Available Background:Respiratory Distress syndrome is the most common respiratory disease in premature neonate and the most important cause of death among them. We aimed to investigate factors to predict successful or failure of INSURE method as a therapeutic method of RDS.Methods:In a cohort study,45 neonates with diagnosed RDS and birth weight lower than 1500g were included and they underwent INSURE followed by NCPAP(Nasal Continuous Positive Airway Pressure. The patients were divided into failure or successful groups and factors which can predict success of INSURE were investigated by logistic regression in SPSS 16th version.Results:29 and16 neonates were observed in successful and failure groups, respectively. Birth weight was the only variable with significant difference between two groups (P=0.002. Finally logistic regression test showed that birth weight is only predicting factor for success (P: 0.001, EXP[β]: 0.009, CI [95%]: 1.003-0.014 and mortality (P: 0.029, EXP[β]: 0.993, CI [95%]: 0.987-0.999 of neonates treated with INSURE method.Conclusion:Predicting factors which affect on success rate of INSURE can be useful for treating and reducing charge of neonate with RDS and the birth weight is one of the effective factor on INSURE Success in this study.

Predicting Factors of INSURE Failure in Low Birth Weight Neonates with RDS; A Logistic Regression Model

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Bita Najafian

2015-02-01

Full Text Available Background:Respiratory Distress syndrome is the most common respiratory disease in premature neonate and the most important cause of death among them. We aimed to investigate factors to predict successful or failure of INSURE method as a therapeutic method of RDS. Methods:In a cohort study,45 neonates with diagnosed RDS and birth weight lower than 1500g were included and they underwent INSURE followed by NCPAP(Nasal Continuous Positive Airway Pressure. The patients were divided into failure or successful groups and factors which can predict success of INSURE were investigated by logistic regression in SPSS 16th version. Results:29 and16 neonates were observed in successful and failure groups, respectively. Birth weight was the only variable with significant difference between two groups (P=0.002. Finally logistic regression test showed that birth weight is only predicting factor for success (P: 0.001, EXP[β]: 0.009, CI [95%]: 1.003-0.014 and mortality (P: 0.029, EXP[β]: 0.993, CI [95%]: 0.987-0.999 of neonates treated with INSURE method. Conclusion:Predicting factors which affect on success rate of INSURE can be useful for treating and reducing charge of neonate with RDS and the birth weight is one of the effective factor on INSURE Success in this study.

An Alternative Flight Software Trigger Paradigm: Applying Multivariate Logistic Regression to Sense Trigger Conditions Using Inaccurate or Scarce Information

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Smith, Kelly M.; Gay, Robert S.; Stachowiak, Susan J.

2013-01-01

In late 2014, NASA will fly the Orion capsule on a Delta IV-Heavy rocket for the Exploration Flight Test-1 (EFT-1) mission. For EFT-1, the Orion capsule will be flying with a new GPS receiver and new navigation software. Given the experimental nature of the flight, the flight software must be robust to the loss of GPS measurements. Once the high-speed entry is complete, the drogue parachutes must be deployed within the proper conditions to stabilize the vehicle prior to deploying the main parachutes. When GPS is available in nominal operations, the vehicle will deploy the drogue parachutes based on an altitude trigger. However, when GPS is unavailable, the navigated altitude errors become excessively large, driving the need for a backup barometric altimeter to improve altitude knowledge. In order to increase overall robustness, the vehicle also has an alternate method of triggering the parachute deployment sequence based on planet-relative velocity if both the GPS and the barometric altimeter fail. However, this backup trigger results in large altitude errors relative to the targeted altitude. Motivated by this challenge, this paper demonstrates how logistic regression may be employed to semi-automatically generate robust triggers based on statistical analysis. Logistic regression is used as a ground processor pre-flight to develop a statistical classifier. The classifier would then be implemented in flight software and executed in real-time. This technique offers improved performance even in the face of highly inaccurate measurements. Although the logistic regression-based trigger approach will not be implemented within EFT-1 flight software, the methodology can be carried forward for future missions and vehicles.

The association between chronic bullying victimization with weight status and body self-image: a cross-national study in 39 countries.

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Lian, Qiguo; Su, Qiru; Li, Ruili; Elgar, Frank J; Liu, Zhihao; Zheng, Dongpeng

2018-01-01

Childhood obesity and school bullying are pervasive public health issues and known to co-occur in adolescents. However, the association between underweight or thinness and chronic bullying victimization is unclear. The current study examined whether chronic bullying victimization is associated with weight status and body self-image. A school-based, cross-sectional study in 39 North American and European countries and regions was conducted. A total of 213,595 adolescents aged 11, 13, and 15 years were surveyed in 2009/10. Chronic bullying victimization was identified using the Revised Olweus Bully/Victim Questionnaire. Weight status was determined using self-reported height and weight and the body mass index (BMI), and body self-image was based on perceived weight. We tested associations between underweight and bullying victimization using three-level logistic regression models. Of the 213,595 adolescents investigated, 11.28% adolescents reported chronic bullying victimization, 14.80% were classified as overweight/obese according to age- and sex-specific BMI criteria, 12.97% were underweight, and 28.36% considered themselves a little bit fat or too fat, 14.57% were too thin. Bullying victimization was less common in older adolescent boys and girls. Weight status was associated with chronic bullying victimization (adjusted OR underweight = 1.10, 95% CI = 1.05-1.16, p = 0.002; adjusted OR overweight = 1.40, 95% CI = 1.32-1.49, p self-image also related to chronic bullying victimization (adjusted OR too thin = 1.42, 95% CI = 1.36-1.49, p self-rated overweight are associated with chronic bullying victimization. Both overweight and underweight children are at risk of being chronically bullied.

Logistic quantile regression provides improved estimates for bounded avian counts: A case study of California Spotted Owl fledgling production

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Cade, Brian S.; Noon, Barry R.; Scherer, Rick D.; Keane, John J.

2017-01-01

Counts of avian fledglings, nestlings, or clutch size that are bounded below by zero and above by some small integer form a discrete random variable distribution that is not approximated well by conventional parametric count distributions such as the Poisson or negative binomial. We developed a logistic quantile regression model to provide estimates of the empirical conditional distribution of a bounded discrete random variable. The logistic quantile regression model requires that counts are randomly jittered to a continuous random variable, logit transformed to bound them between specified lower and upper values, then estimated in conventional linear quantile regression, repeating the 3 steps and averaging estimates. Back-transformation to the original discrete scale relies on the fact that quantiles are equivariant to monotonic transformations. We demonstrate this statistical procedure by modeling 20 years of California Spotted Owl fledgling production (0−3 per territory) on the Lassen National Forest, California, USA, as related to climate, demographic, and landscape habitat characteristics at territories. Spotted Owl fledgling counts increased nonlinearly with decreasing precipitation in the early nesting period, in the winter prior to nesting, and in the prior growing season; with increasing minimum temperatures in the early nesting period; with adult compared to subadult parents; when there was no fledgling production in the prior year; and when percentage of the landscape surrounding nesting sites (202 ha) with trees ≥25 m height increased. Changes in production were primarily driven by changes in the proportion of territories with 2 or 3 fledglings. Average variances of the discrete cumulative distributions of the estimated fledgling counts indicated that temporal changes in climate and parent age class explained 18% of the annual variance in owl fledgling production, which was 34% of the total variance. Prior fledgling production explained as much of

Impact of bullying victimization on suicide and negative health behaviors among adolescents in Latin America

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Matthew L. Romo

Full Text Available ABSTRACT Objective To compare the prevalence of bullying victimization, suicidal ideation, suicidal attempts, and negative health behaviors (current tobacco use, recent heavy alcohol use, truancy, involvement in physical fighting, and unprotected sexual intercourse in five different Latin American countries and determine the association of bullying victimization with these outcomes, exploring both bullying type and frequency. Methods Study data were from Global School–based Student Health Surveys from Bolivia, Costa Rica, Honduras, Peru, and Uruguay, which covered nationally representative samples of school-going adolescents. The surveys used a two-stage clustered sample design, sampling schools and then classrooms. Logistic regression models were run to determine the statistical significance of associations with bullying. Results Among the 14 560 school-going adolescents included in this study, the prevalence of any bullying victimization in the past 30 days was 37.8%. Bullying victimization was associated with greater odds of suicidal ideation with planning (adjusted odds ratio (AOR: 3.12; P < 0.0001 and at least one suicide attempt (AOR: 3.07; P < 0.0001. An increasing exposure–response effect of increasing days of bullying victimization on suicide outcomes was also observed. Bullying victimization was associated with higher odds of current tobacco use (AOR: 2.14; P < 0.0001; truancy (AOR: 1.76; P < 0.0001; physical fighting (AOR: 2.40; P < 0.0001; and unprotected sexual intercourse (AOR: 1.77; P < 0.0001. Conclusions Although the prevalence of bullying victimization varied by country, its association with suicidal ideation and behavior and negative health behaviors remained relatively consistent. Addressing bullying needs to be made a priority in Latin America, and an integrated approach that also includes mental and physical health promotion is needed.

ENHANCED PREDICTION OF STUDENT DROPOUTS USING FUZZY INFERENCE SYSTEM AND LOGISTIC REGRESSION

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A. Saranya

2016-01-01

Full Text Available Predicting college and school dropouts is a major problem in educational system and has complicated challenge due to data imbalance and multi dimensionality, which can affect the low performance of students. In this paper, we have collected different database from various colleges, among these 500 best real attributes are identified in order to identify the factor that affecting dropout students using neural based classification algorithm and different mining technique are implemented for data processing. We also propose a Dropout Prediction Algorithm (DPA using fuzzy logic and Logistic Regression based inference system because the weighted average will improve the performance of whole system. We are experimented our proposed work with all other classification systems and documented as the best outcomes. The aggregated data is given to the decision trees for better dropout prediction. The accuracy of overall system 98.6% it shows the proposed work depicts efficient prediction.

Applying additive logistic regression to data derived from sensors monitoring behavioral and physiological characteristics of dairy cows to detect lameness

NARCIS (Netherlands)

Kamphuis, C.; Frank, E.; Burke, J.; Verkerk, G.A.; Jago, J.

2013-01-01

The hypothesis was that sensors currently available on farm that monitor behavioral and physiological characteristics have potential for the detection of lameness in dairy cows. This was tested by applying additive logistic regression to variables derived from sensor data. Data were collected

Racial/ethnic disparities in history of incarceration, experiences of victimization, and associated health indicators among transgender women in the U.S.

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Reisner, Sari L; Bailey, Zinzi; Sevelius, Jae

2014-01-01

Limited national data document the prevalence of incarceration among transgender women, experiences of victimization while incarcerated, and associations of transgender status with health. Data were from the National Transgender Discrimination Survey (NTDS), a large convenience sample of transgender adults in the U.S., collected between September 2008 and March 2009. Respondents who indicated a transfeminine gender identity were included in the current study (n = 3,878). Multivariable logistic regression was used to model ever being incarcerated and experiencing victimization while incarcerated as a function of race/ethnicity and health-related indicators. Overall, 19.3% reported having ever been incarcerated. Black and Native American/Alaskan Native transgender women were more likely to report a history of incarceration than White (non-Hispanic) respondents, and those with a history of incarceration were more likely to report negative health-related indicators, including self-reporting as HIV-positive. Among previously incarcerated respondents, 47.0% reported victimization while incarcerated. Black, Latina, and mixed race transgender women were more likely to report experiences of victimization while incarcerated. Transgender women reported disproportionately high rates of incarceration and victimization while incarcerated, as well as associated negative health-related indicators. Interventions and policy changes are needed to support transgender women while incarcerated and upon release.

Targeted Victimization and Suicidality Among Trans People: A Web-Based Survey.

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Zeluf, Galit; Dhejne, Cecilia; Orre, Carolina; Mannheimer, Louise Nilunger; Deogan, Charlotte; Höijer, Jonas; Winzer, Regina; Thorson, Anna Ekéus

2018-04-01

The aim of this study was to investigate the associations between a series of empirically known risk and protective factors and suicidality among trans people in Sweden. Participants were self-selected anonymously to a web-based survey conducted in 2014. Univariable and multivariable logistic regression analyses were performed to assess associations between contributing factors and suicide ideation in the past 12 months and lifetime suicide attempts. The analysis included 796 trans individuals, between 15 and 94 years of age, who live in Sweden. A total of 37% of respondents reported that they have seriously considered suicide during the past 12 months and 32% had ever attempted a suicide. Offensive treatment during the past three months and lifetime exposure to trans-related violence were significantly associated with suicidality. Less satisfaction with contacts with friends and acquaintances and with one's own psychological wellbeing were associated with suicide ideation in the past 12 months. Lack of practical support was associated with lifetime suicide attempts. Our findings show that suicidality is directly correlated with trans-related victimization. Preventing targeted victimization is, therefore, a key preventive intervention against this elevated suicidality.

"Logits and Tigers and Bears, Oh My! A Brief Look at the Simple Math of Logistic Regression and How It Can Improve Dissemination of Results"

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Jason W. Osborne

2012-06-01

Full Text Available Logistic regression is slowly gaining acceptance in the social sciences, and fills an important niche in the researcher's toolkit: being able to predict important outcomes that are not continuous in nature. While OLS regression is a valuable tool, it cannot routinely be used to predict outcomes that are binary or categorical in nature. These outcomes represent important social science lines of research: retention in, or dropout from school, using illicit drugs, underage alcohol consumption, antisocial behavior, purchasing decisions, voting patterns, risky behavior, and so on. The goal of this paper is to briefly lead the reader through the surprisingly simple mathematics that underpins logistic regression: probabilities, odds, odds ratios, and logits. Anyone with spreadsheet software or a scientific calculator can follow along, and in turn, this knowledge can be used to make much more interesting, clear, and accurate presentations of results (especially to non-technical audiences. In particular, I will share an example of an interaction in logistic regression, how it was originally graphed, and how the graph was made substantially more user-friendly by converting the original metric (logits to a more readily interpretable metric (probability through three simple steps.

Victimization from bullying among school-attending adolescents in grades 7 to 10 in Zambia

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Emmanuel Rudatsikira

2012-01-01

Full Text Available BACKGROUND: Among school- attending adolescents, victimization from bullying is associated with anxiety, depression and poor academic performance. There are limited reports on victimization from bullying in Zambia; we therefore conducted this study to determine the prevalence and correlates for victimization from bullying among adolescents in grades 7 to 10 in the country in order to add information on the body of knowledge on victimization from bullying. METHODS: The 2004 Zambia Global School-based Health Survey (GSHS data among adolescents in grades 7 to 10 were obtained from the World Health Organization. We estimated the prevalence of victimization from bullying. We also conducted weighted multivariate logistic regression analysis to determine independent factors associated with victimization from bullying, and report adjusted odds ratios (AOR and their 95% confidence intervals (CI. RESULTS: Of 2136 students who participated in the 2004 Zambia GSHS, 1559 had information on whether they were bullied or not. Of these, 1559 students, 62.8% (60.0% of male and 65.0% of female participants reported having been bullied in the previous 30 days to the survey. We found that respondents of age less than 14 years were 7% (AOR=0.93; 95%CI [0.91, 0.95] less likely to have been bullied compared to those aged 16 years or older. Being a male (AOR=1.07; 95%CI [1.06, 1.09], lonely (AOR=1.24; 95%CI [1.22, 1.26], worried (AOR=1.12; 95%CI [1.11, 1.14], consuming alcohol (AOR=2.59; 95%CI [2.55, 2.64], missing classes (AOR=1.30; 95%CI [1.28, 1.32], and considering attempting suicide (AOR=1.20; 95%CI [1.18, 1.22] were significantly associated with bullying victimization. CONCLUSIONS: Victimization from bullying is prevalent among in-school adolescents in grades 7 to 10 in Zambia, and interventions to curtail it should consider the factors that have been identified in this study.

Victimization from bullying among school-attending adolescents in grades 7 to 10 in Zambia.

Science.gov (United States)

Siziya, Seter; Rudatsikira, Emmanuel; Muula, Adamson S

2012-01-01

Among school- attending adolescents, victimization from bullying is associated with anxiety, depression and poor academic performance. There are limited reports on victimization from bullying in Zambia; we therefore conducted this study to determine the prevalence and correlates for victimization from bullying among adolescents in grades 7 to 10 in the country in order to add information on the body of knowledge on victimization from bullying. The 2004 Zambia Global School-based Health Survey (GSHS) data among adolescents in grades 7 to 10 were obtained from the World Health Organization. We estimated the prevalence of victimization from bullying. We also conducted weighted multivariate logistic regression analysis to determine independent factors associated with victimization from bullying, and report adjusted odds ratios (AOR) and their 95% confidence intervals (CI). Of 2136 students who participated in the 2004 Zambia GSHS, 1559 had information on whether they were bullied or not. Of these, 1559 students, 62.8% (60.0% of male and 65.0% of female) participants reported having been bullied in the previous 30 days to the survey. We found that respondents of age less than 14 years were 7% (AOR=0.93; 95%CI [0.91, 0.95]) less likely to have been bullied compared to those aged 16 years or older. Being a male (AOR=1.07; 95%CI [1.06, 1.09]), lonely (AOR=1.24; 95%CI [1.22, 1.26]), worried (AOR=1.12; 95%CI [1.11, 1.14]), consuming alcohol (AOR=2.59; 95%CI [2.55, 2.64]), missing classes (AOR=1.30; 95%CI [1.28, 1.32]), and considering attempting suicide (AOR=1.20; 95%CI [1.18, 1.22]) were significantly associated with bullying victimization. Victimization from bullying is prevalent among in-school adolescents in grades 7 to 10 in Zambia, and interventions to curtail it should consider the factors that have been identified in this study.

Country of residence, gender equality and victim blaming attitudes about partner violence: a multilevel analysis in EU.

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Ivert, Anna-Karin; Merlo, Juan; Gracia, Enrique

2017-09-27

Intimate partner violence against women (IPVAW) is a global and preventable public health problem. Public attitudes, such as victim-blaming, are important for our understanding of differences in the occurrence of IPVAW, as they contribute to its justification. In this paper, we focus on victim-blaming attitudes regarding IPVAW within the EU and we apply multilevel analyses to identify contextual determinants of victim-blaming attitudes. We investigate both the general contextual effect of the country and the specific association between country level of gender equality and individual victim-blaming attitudes, as well as to what extend a possible general contextual effect was explained by county level gender equality. We analyzed data from 26 800 respondents from 27 member states of the European Union who responded to a survey on public perceptions of domestic violence. We applied multilevel logistic regression analysis and measures of variance (intra-class correlation (ICC)) were calculated, as well as the discriminatory accuracy by calculating the area under the receiver operator characteristic curve. Over and above individual characteristics, about 15% of the individual variance in the propensity for having victim-blaming attitudes was found at the country level, and country level of gender equality did not affect the general contextual effect (i.e. ICC) of the country on individual victim-blaming attitudes. The present study shows that there are important between-country differences in victim-blaming attitudes that cannot be explained by differences in individual-level demographics or in gender equality at the country level. More research on attitudes towards IPVAW is needed. © The Author 2017. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved.

A local equation for differential diagnosis of β-thalassemia trait and iron deficiency anemia by logistic regression analysis in Southeast Iran.

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Sargolzaie, Narjes; Miri-Moghaddam, Ebrahim

2014-01-01

The most common differential diagnosis of β-thalassemia (β-thal) trait is iron deficiency anemia. Several red blood cell equations were introduced during different studies for differential diagnosis between β-thal trait and iron deficiency anemia. Due to genetic variations in different regions, these equations cannot be useful in all population. The aim of this study was to determine a native equation with high accuracy for differential diagnosis of β-thal trait and iron deficiency anemia for the Sistan and Baluchestan population by logistic regression analysis. We selected 77 iron deficiency anemia and 100 β-thal trait cases. We used binary logistic regression analysis and determined best equations for probability prediction of β-thal trait against iron deficiency anemia in our population. We compared diagnostic values and receiver operative characteristic (ROC) curve related to this equation and another 10 published equations in discriminating β-thal trait and iron deficiency anemia. The binary logistic regression analysis determined the best equation for best probability prediction of β-thal trait against iron deficiency anemia with area under curve (AUC) 0.998. Based on ROC curves and AUC, Green & King, England & Frazer, and then Sirdah indices, respectively, had the most accuracy after our equation. We suggest that to get the best equation and cut-off in each region, one needs to evaluate specific information of each region, specifically in areas where populations are homogeneous, to provide a specific formula for differentiating between β-thal trait and iron deficiency anemia.

Logistic regression analysis of financial literacy implications for retirement planning in Croatia

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Dajana Barbić

2016-12-01

Full Text Available The relationship between financial literacy and financial behavior is important, as individuals are increasingly being asked to take responsibility for their financial wellbeing, especially their retirement. Analyzing of individual savings and attitudes towards retirement planning is important, as these types of investments are a way of preserving security during years of financial vulnerability. Research indicates that individuals who do not save adequately for their retirement, generally have a relatively low level of financial literacy. This research investigates the relationship between financial literacy and retirement planning in Croatia. To analyze the relationship between financial literacy and planning for retirement, maximum likelihood logistic regression analysis was used. The paper shows that those who answer financial literacy questions correctly are more likely to have a positive attitude towards retirement planning and are more likely to save for retirement, ensuring them of higher levels of financial security in retirement. The Goodness-of-Fit evaluation for the estimated logit model was performed using the Andrews and Hosmer-Lemeshow Tests.

Underwater Cylindrical Object Detection Using the Spectral Features of Active Sonar Signals with Logistic Regression Models

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Yoojeong Seo

2018-01-01

Full Text Available The issue of detecting objects bottoming on the sea floor is significant in various fields including civilian and military areas. The objective of this study is to investigate the logistic regression model to discriminate the target from the clutter and to verify the possibility of applying the model trained by the simulated data generated by the mathematical model to the real experimental data because it is not easy to obtain sufficient data in the underwater field. In the first stage of this study, when the clutter signal energy is so strong that the detection of a target is difficult, the logistic regression model is employed to distinguish the strong clutter signal and the target signal. Previous studies have found that if the clutter energy is larger, false detection occurs even for the various existing detection schemes. For this reason, the discrete Fourier transform (DFT magnitude spectrum of acoustic signals received by active sonar is applied to train the model to distinguish whether the received signal contains a target signal or not. The goodness of fit of the model is verified in terms of receiver operation characteristic (ROC, area under ROC curve (AUC, and classification table. The detection performance of the proposed model is evaluated in terms of detection rate according to target to clutter ratio (TCR. Furthermore, the real experimental data are employed to test the proposed approach. When using the experimental data to test the model, the logistic regression model is trained by the simulated data that are generated based on the mathematical model for the backscattering of the cylindrical object. The mathematical model is developed according to the size of the cylinder used in the experiment. Since the information on the experimental environment including the sound speed, the sediment type and such is not available, once simulated data are generated under various conditions, valid simulated data are selected using 70% of the

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

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

2017-06-01

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

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....

Logistic regression analysis to predict Medical Licensing Examination of Thailand (MLET) Step1 success or failure.

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Wanvarie, Samkaew; Sathapatayavongs, Boonmee

2007-09-01

The aim of this paper was to assess factors that predict students' performance in the Medical Licensing Examination of Thailand (MLET) Step1 examination. The hypothesis was that demographic factors and academic records would predict the students' performance in the Step1 Licensing Examination. A logistic regression analysis of demographic factors (age, sex and residence) and academic records [high school grade point average (GPA), National University Entrance Examination Score and GPAs of the pre-clinical years] with the MLET Step1 outcome was accomplished using the data of 117 third-year Ramathibodi medical students. Twenty-three (19.7%) students failed the MLET Step1 examination. Stepwise logistic regression analysis showed that the significant predictors of MLET Step1 success/failure were residence background and GPAs of the second and third preclinical years. For students whose sophomore and third-year GPAs increased by an average of 1 point, the odds of passing the MLET Step1 examination increased by a factor of 16.3 and 12.8 respectively. The minimum GPAs for students from urban and rural backgrounds to pass the examination were estimated from the equation (2.35 vs 2.65 from 4.00 scale). Students from rural backgrounds and/or low-grade point averages in their second and third preclinical years of medical school are at risk of failing the MLET Step1 examination. They should be given intensive tutorials during the second and third pre-clinical years.

Intimate Partner Violence during Pregnancy: Victim or Perpetrator? Does it make a difference?

Science.gov (United States)

Shneyderman, Yuliya; Kiely, Michele

2013-01-01

Objectives To differentiate between forms of intimate partner violence (IPV) (victim only, perpetrator only, or participating in reciprocal violence) and examine risk profiles and pregnancy outcomes. Design Prospective Setting Washington, DC, July 2001 to October 2003 Sample 1044 high-risk African-American pregnant women who participated in a randomized controlled trial to address IPV, depression, smoking, and environmental tobacco smoke exposure. Methods Multivariable linear and logistic regression Main outcome measures Low and very low birth weight, preterm and very preterm birth Results 5% of women were victims only, 12% were perpetrators only, 27% participated in reciprocal violence, and 55% reported no IPV. Women reporting reciprocal violence in the past year were more likely to drink, use illicit drugs, and experience environmental tobacco smoke exposure and were less likely to be very happy about their pregnancies. Women reporting any type of IPVwere more likely to be depressed than those reporting no IPV. Women experiencing reciprocal violence reported highest levels of depression. Women who were victims of IPV were more likely to give birth prior prematurely and deliver low and very low birth weight infants. Conclusions We conclude that women were at highest risk for pregnancy risk factors when they participated in reciprocal violence and thus might be at higher risk for long-term consequences, but women who were victims of intimate partner violence were more likely to show proximal negative outcomes like preterm birth and low birth weight. Different types of interventions may be needed for these two forms of intimate partner violence. PMID:23786367

Heart rate variability associated with posttraumatic stress disorder in victims' families of sewol ferry disaster.

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Lee, Sang Min; Han, Hyesung; Jang, Kuk-In; Huh, Seung; Huh, Hyu Jung; Joo, Ji-Young; Chae, Jeong-Ho

2018-01-01

Posttraumatic stress disorder (PTSD), which is caused by a major traumatic event, has been associated with autonomic nervous function. However, there have been few explorations of measuring biological stress in the victims' family members who have been indirectly exposed to the disaster. Therefore, this longitudinal study examined the heart rate variability (HRV) of the family members of victims of the Sewol ferry disaster. We recruited 112 family members of victims 18 months after the disaster. Sixty-seven participants were revisited at the 30 months postdisaster time point. HRV and psychiatric symptoms including PTSD, depression and anxiety were evaluated at each time point. Participants with PTSD had a higher low frequency to high frequency ratio (LF:HF ratio) than those without PTSD. Logistic regression analysis showed that the LF:HF ratio at 18 months postdisaster was associated with a PTSD diagnosis at 30 months postdisaster. These results suggest that disrupted autonomic nervous system functioning for longer than a year after trauma exposure contributes to predicting PTSD vulnerability. Our finding may contribute to understand neurophysiologic mechanisms underlying secondary traumatic stress. Future studies will be needed to clarify the interaction between autonomic regulation and trauma exposure. Copyright © 2017. Published by Elsevier B.V.

Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the Sultan Mountains (Aksehir, Turkey)

Science.gov (United States)

Ozdemir, Adnan

2011-07-01

SummaryThe purpose of this study is to produce a groundwater spring potential map of the Sultan Mountains in central Turkey, based on a logistic regression method within a Geographic Information System (GIS) environment. Using field surveys, the locations of the springs (440 springs) were determined in the study area. In this study, 17 spring-related factors were used in the analysis: geology, relative permeability, land use/land cover, precipitation, elevation, slope, aspect, total curvature, plan curvature, profile curvature, wetness index, stream power index, sediment transport capacity index, distance to drainage, distance to fault, drainage density, and fault density map. The coefficients of the predictor variables were estimated using binary logistic regression analysis and were used to calculate the groundwater spring potential for the entire study area. The accuracy of the final spring potential map was evaluated based on the observed springs. The accuracy of the model was evaluated by calculating the relative operating characteristics. The area value of the relative operating characteristic curve model was found to be 0.82. These results indicate that the model is a good estimator of the spring potential in the study area. The spring potential map shows that the areas of very low, low, moderate and high groundwater spring potential classes are 105.586 km 2 (28.99%), 74.271 km 2 (19.906%), 101.203 km 2 (27.14%), and 90.05 km 2 (24.671%), respectively. The interpretations of the potential map showed that stream power index, relative permeability of lithologies, geology, elevation, aspect, wetness index, plan curvature, and drainage density play major roles in spring occurrence and distribution in the Sultan Mountains. The logistic regression approach has not yet been used to delineate groundwater potential zones. In this study, the logistic regression method was used to locate potential zones for groundwater springs in the Sultan Mountains. The evolved model

Determining Effects of Genes, Environment, and Gene X Environment Interaction That Are Common to Breast and Ovarian Cancers Via Bivariate Logistic Regression

National Research Council Canada - National Science Library

Ramakrishnan, Viswanathan

2003-01-01

.... A generalized estimation equations (GEE) logistic regression model was used for the modeling. A shared trait is defined for two discrete traits based upon explicit patterns of trait concordance and discordance within twin pairs...

Predicting hyperketonemia by logistic and linear regression using test-day milk and performance variables in early-lactation Holstein and Jersey cows.

Science.gov (United States)

Chandler, T L; Pralle, R S; Dórea, J R R; Poock, S E; Oetzel, G R; Fourdraine, R H; White, H M

2018-03-01

Although cowside testing strategies for diagnosing hyperketonemia (HYK) are available, many are labor intensive and costly, and some lack sufficient accuracy. Predicting milk ketone bodies by Fourier transform infrared spectrometry during routine milk sampling may offer a more practical monitoring strategy. The objectives of this study were to (1) develop linear and logistic regression models using all available test-day milk and performance variables for predicting HYK and (2) compare prediction methods (Fourier transform infrared milk ketone bodies, linear regression models, and logistic regression models) to determine which is the most predictive of HYK. Given the data available, a secondary objective was to evaluate differences in test-day milk and performance variables (continuous measurements) between Holsteins and Jerseys and between cows with or without HYK within breed. Blood samples were collected on the same day as milk sampling from 658 Holstein and 468 Jersey cows between 5 and 20 d in milk (DIM). Diagnosis of HYK was at a serum β-hydroxybutyrate (BHB) concentration ≥1.2 mmol/L. Concentrations of milk BHB and acetone were predicted by Fourier transform infrared spectrometry (Foss Analytical, Hillerød, Denmark). Thresholds of milk BHB and acetone were tested for diagnostic accuracy, and logistic models were built from continuous variables to predict HYK in primiparous and multiparous cows within breed. Linear models were constructed from continuous variables for primiparous and multiparous cows within breed that were 5 to 11 DIM or 12 to 20 DIM. Milk ketone body thresholds diagnosed HYK with 64.0 to 92.9% accuracy in Holsteins and 59.1 to 86.6% accuracy in Jerseys. Logistic models predicted HYK with 82.6 to 97.3% accuracy. Internally cross-validated multiple linear regression models diagnosed HYK of Holstein cows with 97.8% accuracy for primiparous and 83.3% accuracy for multiparous cows. Accuracy of Jersey models was 81.3% in primiparous and 83

Predicting the aquatic toxicity mode of action using logistic regression and linear discriminant analysis.

Science.gov (United States)

Ren, Y Y; Zhou, L C; Yang, L; Liu, P Y; Zhao, B W; Liu, H X

2016-09-01

The paper highlights the use of the logistic regression (LR) method in the construction of acceptable statistically significant, robust and predictive models for the classification of chemicals according to their aquatic toxic modes of action. Essentials accounting for a reliable model were all considered carefully. The model predictors were selected by stepwise forward discriminant analysis (LDA) from a combined pool of experimental data and chemical structure-based descriptors calculated by the CODESSA and DRAGON software packages. Model predictive ability was validated both internally and externally. The applicability domain was checked by the leverage approach to verify prediction reliability. The obtained models are simple and easy to interpret. In general, LR performs much better than LDA and seems to be more attractive for the prediction of the more toxic compounds, i.e. compounds that exhibit excess toxicity versus non-polar narcotic compounds and more reactive compounds versus less reactive compounds. In addition, model fit and regression diagnostics was done through the influence plot which reflects the hat-values, studentized residuals, and Cook's distance statistics of each sample. Overdispersion was also checked for the LR model. The relationships between the descriptors and the aquatic toxic behaviour of compounds are also discussed.

“It must be me” or “It could be them?” : The impact of the social network position of bullies and victims on victims' adjustment

NARCIS (Netherlands)

Huitsing, Gijs; Veenstra, René; Sainio, Miia; Salmivalli, Christina

2012-01-01

It was examined in this study whether the association between victimization and psychological adjustment (depression and self-esteem) is moderated by the classroom network position of bullies and victims. Multivariate multilevel regression analysis was used on a large sample representative of grades

Modelling landscape change in paddy fields using logistic regression and GIS

Science.gov (United States)

Franjaya, E. E.; Syartinilia; Setiawan, Y.

2018-05-01

Paddy field in karawang district, as an important agricultural land in west java, has been decreased since 1994. From previous study, paddy fields dominantly turned into built area. The changes were almost occured in the middle area of the district where roadways, industries, settlements, and commercial buildings were existed. These were estimated as driving forces. But, we still need to prove it. This study aimed to construct the paddy field probability change model, subsequently the driving forces will be obtained. GIS combined with logistic regression using environmental variables were used as main method in this study. Ten environmental variables were elevation 0–500 m, elevation>500 m, slope8%, CBD, build up area, river, irrigation, toll and national roadway, and collector and local roadway. The result indicated that four variables were significantly played as driving forces (slope>8%, CBD area, build up area, and collector and local roadway). Paddy field has high, medium, and low probability to change which covered about 27.8%, 7.8%, and 64.4% area in Karawang respectively. Based on landscape ecology, the recommendation that suitable with landscape change is adaptive management.

Childhood bullying victimization is associated with use of mental health services over five decades: a longitudinal nationally representative cohort study.

Science.gov (United States)

Evans-Lacko, S; Takizawa, R; Brimblecombe, N; King, D; Knapp, M; Maughan, B; Arseneault, L

2017-01-01

Research supports robust associations between childhood bullying victimization and mental health problems in childhood/adolescence and emerging evidence shows that the impact can persist into adulthood. We examined the impact of bullying victimization on mental health service use from childhood to midlife. We performed secondary analysis using the National Child Development Study, the 1958 British Birth Cohort Study. We conducted analyses on 9242 participants with complete data on childhood bullying victimization and service use at midlife. We used multivariable logistic regression models to examine associations between childhood bullying victimization and mental health service use at the ages of 16, 23, 33, 42 and 50 years. We estimated incidence and persistence of mental health service use over time to the age of 50 years. Compared with participants who were not bullied in childhood, those who were frequently bullied were more likely to use mental health services in childhood and adolescence [odds ratio (OR) 2.53, 95% confidence interval (CI) 1.88-3.40] and also in midlife (OR 1.30, 95% CI 1.10-1.55). Disparity in service use associated with childhood bullying victimization was accounted for by both incident service use through to age 33 years by a subgroup of participants, and by persistent use up to midlife. Childhood bullying victimization adds to the pressure on an already stretched health care system. Policy and practice efforts providing support for victims of bullying could help contain public sector costs. Given constrained budgets and the long-term mental health impact on victims of bullying, early prevention strategies could be effective at limiting both individual distress and later costs.

Pre-offense alcohol intake in homicide offenders and victims: A forensic-toxicological case-control study.

Science.gov (United States)

Hedlund, Jonatan; Forsman, Jonas; Sturup, Joakim; Masterman, Thomas

2018-05-01

Alcohol is associated with violent behavior, although little is known regarding to what extent alcohol increases homicide risk. We aimed to estimate risks of homicide offending and victimization conferred by the presence of ethanol in blood by using toxicological data from homicide victims and offenders and from controls who had died in vehicle-related accidents. From nationwide governmental registries and databases, forensic-toxicological results were retrieved for victims (n = 200) and offenders (n = 105) of homicides committed during the years 2007-2009 and individuals killed in vehicle-related accidents (n = 1629) during the years 2006-2014. Ethanol levels in blood exceeding 0.01 g/100 ml were considered positive. Using logistic regression, we found that the presence of ethanol in blood conferred a significantly increased risk of homicide offending (age-adjusted odds ratio [aOR] = 3.6, 95% confidence interval [95% CI] = 2.3-5.6) and homicide victimization (aOR = 2.1, 95% CI = 1.4-3.0). After stratification by sex, risk estimates in females were about 3-fold greater than in males for both homicide offending ([aOR = 11.0, 95% CI = 2.4-49.8] versus [aOR = 3.1, 95% CI = 1.9-4.9]) and victimization ([aOR = 5.4, 95% CI = 2.4-12.2] versus [aOR = 1.7, 95% CI = 1.1-2.8]). Sensitivity analyses yielded similar estimates. The results of the present study are consistent with prior findings suggesting alcohol to be an important risk factor for homicide offending and victimization. Surprisingly, however, associations were more pronounced in females, although additional studies that control for potential confounders are warranted to facilitate speculations about causality. Copyright © 2018 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Logistic regression model for detecting radon prone areas in Ireland.

Science.gov (United States)

Elío, J; Crowley, Q; Scanlon, R; Hodgson, J; Long, S

2017-12-01

A new high spatial resolution radon risk map of Ireland has been developed, based on a combination of indoor radon measurements (n=31,910) and relevant geological information (i.e. Bedrock Geology, Quaternary Geology, soil permeability and aquifer type). Logistic regression was used to predict the probability of having an indoor radon concentration above the national reference level of 200Bqm -3 in Ireland. The four geological datasets evaluated were found to be statistically significant, and, based on combinations of these four variables, the predicted probabilities ranged from 0.57% to 75.5%. Results show that the Republic of Ireland may be divided in three main radon risk categories: High (HR), Medium (MR) and Low (LR). The probability of having an indoor radon concentration above 200Bqm -3 in each area was found to be 19%, 8% and 3%; respectively. In the Republic of Ireland, the population affected by radon concentrations above 200Bqm -3 is estimated at ca. 460k (about 10% of the total population). Of these, 57% (265k), 35% (160k) and 8% (35k) are in High, Medium and Low Risk Areas, respectively. Our results provide a high spatial resolution utility which permit customised radon-awareness information to be targeted at specific geographic areas. Copyright © 2017 Elsevier B.V. All rights reserved.

Modeling Typhoon Event-Induced Landslides Using GIS-Based Logistic Regression: A Case Study of Alishan Forestry Railway, Taiwan

Directory of Open Access Journals (Sweden)

Sheng-Chuan Chen

2013-01-01

Full Text Available This study develops a model for evaluating the hazard level of landslides at Alishan Forestry Railway, Taiwan, by using logistic regression with the assistance of a geographical information system (GIS. A typhoon event-induced landslide inventory, independent variables, and a triggering factor were used to build the model. The environmental factors such as bedrock lithology from the geology database; topographic aspect, terrain roughness, profile curvature, and distance to river, from the topographic database; and the vegetation index value from SPOT 4 satellite images were used as variables that influence landslide occurrence. The area under curve (AUC of a receiver operator characteristic (ROC curve was used to validate the model. Effects of parameters on landslide occurrence were assessed from the corresponding coefficient that appears in the logistic regression function. Thereafter, the model was applied to predict the probability of landslides for rainfall data of different return periods. Using a predicted map of probability, the study area was classified into four ranks of landslide susceptibility: low, medium, high, and very high. As a result, most high susceptibility areas are located on the western portion of the study area. Several train stations and railways are located on sites with a high susceptibility ranking.

Assessment of diagnostic value of various tumors markers (CEA, CA199, CA50) for colorectal neoplasm with logistic regression and ROC curve

International Nuclear Information System (INIS)

Gu Ping; Huang Gang; Han Yuan

2007-01-01

Objective: To assess the diagnostic value of CEA, CA199 and CA50 for colorectal neoplasm by logistic regression and ROC curve. Methods: Serum CEA (with CLIA), CA199 (with ECLIA) and CA50 (with IRMA) levels were measured in 75 patients with colorectal cancer, 35 patients with benign colorectal disorders and 49 controls. The area under the ROC curve (AUC)s of CEA, CA199, CA50 from logistic regression results were compared. Results: In the cancer-benign disorder group, the AUC of CA50 was larger than the AUC of CA199. AUC of combined CEA, CA50 was largest: not only larger than any AUC of CEA, CA50, CA199 alone but also larger than the AUC of the combined three markers (0.875 vs 0.604). In cancer-control group, the AUC of combination of CEA, CA199 and CA50 was larger than any AUC of CEA, CA199 or CA50 alone. Both in the cancer-benign disorder group or cancer-control group, the AUC of CEA was larger than the AUC of CA199 or CA50. Conclusion: CEA is of definite value in the diagnosis of colorectal cancer. For differential diagnosis, the combination of CEA and CA50 can give more information, while the combination of three tumor markers is less helpful. As an advanced statistical method, logistic regression can improve the diagnostic sensitivity and specificity. (authors)

Collapse susceptibility mapping in karstified gypsum terrain (Sivas basin - Turkey) by conditional probability, logistic regression, artificial neural network models

Science.gov (United States)

Yilmaz, Isik; Keskin, Inan; Marschalko, Marian; Bednarik, Martin

2010-05-01

This study compares the GIS based collapse susceptibility mapping methods such as; conditional probability (CP), logistic regression (LR) and artificial neural networks (ANN) applied in gypsum rock masses in Sivas basin (Turkey). Digital Elevation Model (DEM) was first constructed using GIS software. Collapse-related factors, directly or indirectly related to the causes of collapse occurrence, such as distance from faults, slope angle and aspect, topographical elevation, distance from drainage, topographic wetness index- TWI, stream power index- SPI, Normalized Difference Vegetation Index (NDVI) by means of vegetation cover, distance from roads and settlements were used in the collapse susceptibility analyses. In the last stage of the analyses, collapse susceptibility maps were produced from CP, LR and ANN models, and they were then compared by means of their validations. Area Under Curve (AUC) values obtained from all three methodologies showed that the map obtained from ANN model looks like more accurate than the other models, and the results also showed that the artificial neural networks is a usefull tool in preparation of collapse susceptibility map and highly compatible with GIS operating features. Key words: Collapse; doline; susceptibility map; gypsum; GIS; conditional probability; logistic regression; artificial neural networks.

Modeling data for pancreatitis in presence of a duodenal diverticula using logistic regression

Science.gov (United States)

Dineva, S.; Prodanova, K.; Mlachkova, D.

2013-12-01

The presence of a periampullary duodenal diverticulum (PDD) is often observed during upper digestive tract barium meal studies and endoscopic retrograde cholangiopancreatography (ERCP). A few papers reported that the diverticulum had something to do with the incidence of pancreatitis. The aim of this study is to investigate if the presence of duodenal diverticula predisposes to the development of a pancreatic disease. A total 3966 patients who had undergone ERCP were studied retrospectively. They were divided into 2 groups-with and without PDD. Patients with a duodenal diverticula had a higher rate of acute pancreatitis. The duodenal diverticula is a risk factor for acute idiopathic pancreatitis. A multiple logistic regression to obtain adjusted estimate of odds and to identify if a PDD is a predictor of acute or chronic pancreatitis was performed. The software package STATISTICA 10.0 was used for analyzing the real data.

Latent classes of childhood poly-victimization and associations with suicidal behavior among adult trauma victims: Moderating role of anger.

Science.gov (United States)

Charak, Ruby; Byllesby, Brianna M; Roley, Michelle E; Claycomb, Meredith A; Durham, Tory A; Ross, Jana; Armour, Cherie; Elhai, Jon D

2016-12-01

The aims of the present study were first to identify discrete patterns of childhood victimization experiences including crime, child maltreatment, peer/sibling victimization, sexual violence, and witnessing violence among adult trauma victims using latent class analysis; second, to examine the association between class-membership and suicidal behavior, and third to investigate the differential role of dispositional anger on the association between class-membership and suicidal behavior. We hypothesized that those classes with accumulating exposure to different types of childhood victimization (e.g., poly-victimization) would endorse higher suicidal behavior, than the other less severe classes, and those in the most severe class with higher anger trait would have stronger association with suicidal behavior. Respondents were 346 adults (N=346; M age =35.0years; 55.9% female) who had experienced a lifetime traumatic event. Sixty four percent had experienced poly-victimization (four or more victimization experiences) and 38.8% met the cut-off score for suicidal behavior. Three distinct classes emerged namely, the Least victimization (Class 1), the Predominantly crime and sibling/peer victimization (Class 2), and the Poly-victimization (Class 3) classes. Regression analysis controlling for age and gender indicated that only the main effect of anger was significantly associated with suicidal behavior. The interaction term suggested that those in the Poly-victimization class were higher on suicidal behavior as a result of a stronger association between anger and suicidal behavior in contrast to the association found in Class 2. Clinical implications of findings entail imparting anger management skills to facilitate wellbeing among adult with childhood poly-victimization experiences. Copyright © 2016 Elsevier Ltd. All rights reserved.

Logistic quantile regression provides improved estimates for bounded avian counts: a case study of California Spotted Owl fledgling production

Science.gov (United States)

Brian S. Cade; Barry R. Noon; Rick D. Scherer; John J. Keane

2017-01-01

Counts of avian fledglings, nestlings, or clutch size that are bounded below by zero and above by some small integer form a discrete random variable distribution that is not approximated well by conventional parametric count distributions such as the Poisson or negative binomial. We developed a logistic quantile regression model to provide estimates of the empirical...

Multiple logistic regression model of signalling practices of drivers on urban highways

Science.gov (United States)

Puan, Othman Che; Ibrahim, Muttaka Na'iya; Zakaria, Rozana

2015-05-01

Giving signal is a way of informing other road users, especially to the conflicting drivers, the intention of a driver to change his/her movement course. Other users are exposed to hazard situation and risks of accident if the driver who changes his/her course failed to give signal as required. This paper describes the application of logistic regression model for the analysis of driver's signalling practices on multilane highways based on possible factors affecting driver's decision such as driver's gender, vehicle's type, vehicle's speed and traffic flow intensity. Data pertaining to the analysis of such factors were collected manually. More than 2000 drivers who have performed a lane changing manoeuvre while driving on two sections of multilane highways were observed. Finding from the study shows that relatively a large proportion of drivers failed to give any signals when changing lane. The result of the analysis indicates that although the proportion of the drivers who failed to provide signal prior to lane changing manoeuvre is high, the degree of compliances of the female drivers is better than the male drivers. A binary logistic model was developed to represent the probability of a driver to provide signal indication prior to lane changing manoeuvre. The model indicates that driver's gender, type of vehicle's driven, speed of vehicle and traffic volume influence the driver's decision to provide a signal indication prior to a lane changing manoeuvre on a multilane urban highway. In terms of types of vehicles driven, about 97% of motorcyclists failed to comply with the signal indication requirement. The proportion of non-compliance drivers under stable traffic flow conditions is much higher than when the flow is relatively heavy. This is consistent with the data which indicates a high degree of non-compliances when the average speed of the traffic stream is relatively high.

Potential misinterpretation of treatment effects due to use of odds ratios and logistic regression in randomized controlled trials.

Directory of Open Access Journals (Sweden)

Mirjam J Knol

Full Text Available BACKGROUND: In randomized controlled trials (RCTs, the odds ratio (OR can substantially overestimate the risk ratio (RR if the incidence of the outcome is over 10%. This study determined the frequency of use of ORs, the frequency of overestimation of the OR as compared with its accompanying RR in published RCTs, and we assessed how often regression models that calculate RRs were used. METHODS: We included 288 RCTs published in 2008 in five major general medical journals (Annals of Internal Medicine, British Medical Journal, Journal of the American Medical Association, Lancet, New England Journal of Medicine. If an OR was reported, we calculated the corresponding RR, and we calculated the percentage of overestimation by using the formula . RESULTS: Of 193 RCTs with a dichotomous primary outcome, 24 (12.4% presented a crude and/or adjusted OR for the primary outcome. In five RCTs (2.6%, the OR differed more than 100% from its accompanying RR on the log scale. Forty-one of all included RCTs (n = 288; 14.2% presented ORs for other outcomes, or for subgroup analyses. Nineteen of these RCTs (6.6% had at least one OR that deviated more than 100% from its accompanying RR on the log scale. Of 53 RCTs that adjusted for baseline variables, 15 used logistic regression. Alternative methods to estimate RRs were only used in four RCTs. CONCLUSION: ORs and logistic regression are often used in RCTs and in many articles the OR did not approximate the RR. Although the authors did not explicitly misinterpret these ORs as RRs, misinterpretation by readers can seriously affect treatment decisions and policy making.

Serious Violence Victimization and Perpetration among Youth Living in the Slums of Kampala, Uganda.

Science.gov (United States)

Swahn, Monica H; Gressard, Lindsay; Palmier, Jane B; Kasirye, Rogers; Lynch, Catherine; Yao, Huang

2012-08-01

Violence among youth is a major public health issue globally. Despite these concerns, youth violence surveillance and prevention research are either scarce or non-existent, particularly in developing regions, such as sub-Saharan Africa. The purpose of this study is to quantitatively determine the prevalence of violence involving weapons in a convenience sample of service-seeking youth in Kampala. Moreover, the study will seek to determine the overlap between violence victimization and perpetration among these youth and the potentially shared risk factors for these experiences. We conducted this study of youth in May and June of 2011 to quantify and describe high-risk behaviors and exposures in a convenience sample (N=457) of urban youth, 14-24 years of age, living on the streets or in the slums and who were participating in a Uganda Youth Development Link drop-in center for disadvantaged street youth. We computed bivariate and multivariate logistic regression analyses to determine associations between psychosocial factors and violence victimization and perpetration. The overall prevalence of reporting violence victimization involving a weapon was 36%, and violence perpetration with a weapon was 19%. In terms of the overlap between victimization and perpetration, 16.6% of youth (11.6% of boys and 24.1% of girls) reported both. In multivariate analyses, parental neglect due to alcohol use (Adj.OR=2.28;95%CI: 1.12-4.62) and sadness (Adj.OR=4.36 ;95%CI: 1.81-10.53) were the statistically significant correlates of victimization only. Reporting hunger (Adj.OR=2.87 ;95%CI:1.30-6.33), any drunkenness (Adj.OR=2.35 ;95%CI:1.12-4.92) and any drug use (Adj.OR=3.02 ;95%CI:1.16-7.82) were significantly associated with both perpetration and victimization. The findings underscore the differential experiences associated with victimization and perpetration of violence involving weapons among these vulnerable youth. In particular, reporting hunger, drunkenness and drug use were

The mediating effect of depressive symptoms on the relationship between bullying victimization and non-suicidal self-injury among adolescents: Findings from community and inpatient mental health settings in Ontario, Canada.

Science.gov (United States)

Baiden, Philip; Stewart, Shannon L; Fallon, Barbara

2017-09-01

Although bullying victimization has been linked to a number of behavioral and emotional problems among adolescents, few studies have investigate the mechanism through which bullying victimization affect non-suicidal self-injury. The objectives of this study were to examine the effect of bullying victimization on non-suicidal self-injury and the mediating effect of depressive symptoms on the relationship between bullying victimization and non-suicidal self-injury among adolescents. Data for this study came from the interRAI Child and Youth Mental Health dataset. A total of 1650 adolescents aged 12-18 years (M =14.56; SD =1.79; 54.2% males) were analyzed. Binary logistic and Poisson regression models were conducted to identify the mediating effect of depressive symptoms on the relationship between bullying victimization and non-suicidal self-injury. Of the 1650 adolescents studied, 611 representing 37% engaged in non-suicidal self-injury and 26.7% were victims of bullying. The effect of bullying victimization on non-suicidal self-injury was partially mediated by depressive symptoms after adjusting for the effect of demographic characteristics, history of childhood abuse, social support, and mental health diagnoses. The contribution of bullying victimization and depression to non-suicidal self-injury adds to the case for the development of trauma-focused interventions in reducing the risk of non-suicidal self-injury among adolescents. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Differences Across Age Groups in Transgender and Gender Non-Conforming People's Experiences of Health Care Discrimination, Harassment, and Victimization.

Science.gov (United States)

Kattari, Shanna K; Hasche, Leslie

2016-03-01

Given the increasing diversity among older adults and changes in health policy, knowledge is needed on potential barriers to health care for transgender and gender non-conforming (GNC) individuals. Using the 2010 National Transgender Discrimination Survey (NTDS), logistic regression models test differences between age groups (below 35, 35-49, 50-64, and 65 and above) in lifetime experience of anti-transgender discrimination, harassment, and victimization within health care settings while considering the influences of insurance status, level of passing, time of transition, and other socio-demographic factors. Although more than one fifth of transgender and GNC individuals of all ages reported health discrimination, harassment, or victimization, significant age differences were found. Insurance status and level of passing were also influential. Medicare policy changes and this study's findings prompt further consideration for revising other health insurance policies. In addition, expanded cultural competency trainings that are specific to transgender and GNC individuals are crucial. © The Author(s) 2015.

Binary logistic regression modelling: Measuring the probability of relapse cases among drug addict

Science.gov (United States)

Ismail, Mohd Tahir; Alias, Siti Nor Shadila

2014-07-01

For many years Malaysia faced the drug addiction issues. The most serious case is relapse phenomenon among treated drug addict (drug addict who have under gone the rehabilitation programme at Narcotic Addiction Rehabilitation Centre, PUSPEN). Thus, the main objective of this study is to find the most significant factor that contributes to relapse to happen. The binary logistic regression analysis was employed to model the relationship between independent variables (predictors) and dependent variable. The dependent variable is the status of the drug addict either relapse, (Yes coded as 1) or not, (No coded as 0). Meanwhile the predictors involved are age, age at first taking drug, family history, education level, family crisis, community support and self motivation. The total of the sample is 200 which the data are provided by AADK (National Antidrug Agency). The finding of the study revealed that age and self motivation are statistically significant towards the relapse cases..

Beyond residential mobility: A broader conceptualization of instability and its impact on victimization risk among children☆

Science.gov (United States)

Merrick, Melissa T.; Henly, Megan; Turner, Heather A.; David-Ferdon, Corinne; Hamby, Sherry; Kacha-Ochana, Akadia; Simon, Thomas R.; Finkelhor, David

2018-01-01

Predictability in a child’s environment is a critical quality of safe, stable, nurturing relationships and environments, which promote wellbeing and protect against maltreatment. Research has focused on residential mobility’s effect on this predictability. This study augments such research by analyzing the impact of an instability index—including the lifetime destabilization factors (LDFs) of natural disasters, homelessness, child home removal, multiple moves, parental incarceration, unemployment, deployment, and multiple marriages–on childhood victimizations. The cross-sectional, nationally representative sample of 12,935 cases (mean age = 8.6 years) was pooled from 2008, 2011, and 2014 National Surveys of Children’s Exposure to Violence (NatSCEV). Logistic regression models controlling for demographics, socio-economic status, and family structure tested the association between excessive residential mobility, alone, and with LDFs, and past year childhood victimizations (sexual victimization, witnessing community or family violence, maltreatment, physical assault, property crime, and polyvictimization). Nearly 40% of the sample reported at least one LDF. Excessive residential mobility was significantly predictive of increased odds of all but two victimizations; almost all associations were no longer significant after other destabilizing factors were included. The LDF index without residential mobility was significantly predictive of increased odds of all victimizations (AOR’s ranged from 1.36 to 1.69), and the adjusted odds ratio indicated a 69% increased odds of polyvictimization for each additional LDF a child experienced. The LDF index thus provides a useful alternative to using residential moves as the sole indicator of instability. These findings underscore the need for comprehensive supports and services to support stability for children and families. PMID:29558715

Beyond residential mobility: A broader conceptualization of instability and its impact on victimization risk among children.

Science.gov (United States)

Merrick, Melissa T; Henly, Megan; Turner, Heather A; David-Ferdon, Corinne; Hamby, Sherry; Kacha-Ochana, Akadia; Simon, Thomas R; Finkelhor, David

2018-05-01

Predictability in a child's environment is a critical quality of safe, stable, nurturing relationships and environments, which promote wellbeing and protect against maltreatment. Research has focused on residential mobility's effect on this predictability. This study augments such research by analyzing the impact of an instability index-including the lifetime destabilization factors (LDFs) of natural disasters, homelessness, child home removal, multiple moves, parental incarceration, unemployment, deployment, and multiple marriages--on childhood victimizations. The cross-sectional, nationally representative sample of 12,935 cases (mean age = 8.6 years) was pooled from 2008, 2011, and 2014 National Surveys of Children's Exposure to Violence (NatSCEV). Logistic regression models controlling for demographics, socio-economic status, and family structure tested the association between excessive residential mobility, alone, and with LDFs, and past year childhood victimizations (sexual victimization, witnessing community or family violence, maltreatment, physical assault, property crime, and polyvictimization). Nearly 40% of the sample reported at least one LDF. Excessive residential mobility was significantly predictive of increased odds of all but two victimizations; almost all associations were no longer significant after other destabilizing factors were included. The LDF index without residential mobility was significantly predictive of increased odds of all victimizations (AOR's ranged from 1.36 to 1.69), and the adjusted odds ratio indicated a 69% increased odds of polyvictimization for each additional LDF a child experienced. The LDF index thus provides a useful alternative to using residential moves as the sole indicator of instability. These findings underscore the need for comprehensive supports and services to support stability for children and families. Copyright © 2018. Published by Elsevier Ltd.

Logits and Tigers and Bears, Oh My! A Brief Look at the Simple Math of Logistic Regression and How It Can Improve Dissemination of Results

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Osborne, Jason W.

2012-01-01

Logistic regression is slowly gaining acceptance in the social sciences, and fills an important niche in the researcher's toolkit: being able to predict important outcomes that are not continuous in nature. While OLS regression is a valuable tool, it cannot routinely be used to predict outcomes that are binary or categorical in nature. These…

Statistical learning techniques applied to epidemiology: a simulated case-control comparison study with logistic regression

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Land Walker H

2011-01-01

Full Text Available Abstract Background When investigating covariate interactions and group associations with standard regression analyses, the relationship between the response variable and exposure may be difficult to characterize. When the relationship is nonlinear, linear modeling techniques do not capture the nonlinear information content. Statistical learning (SL techniques with kernels are capable of addressing nonlinear problems without making parametric assumptions. However, these techniques do not produce findings relevant for epidemiologic interpretations. A simulated case-control study was used to contrast the information embedding characteristics and separation boundaries produced by a specific SL technique with logistic regression (LR modeling representing a parametric approach. The SL technique was comprised of a kernel mapping in combination with a perceptron neural network. Because the LR model has an important epidemiologic interpretation, the SL method was modified to produce the analogous interpretation and generate odds ratios for comparison. Results The SL approach is capable of generating odds ratios for main effects and risk factor interactions that better capture nonlinear relationships between exposure variables and outcome in comparison with LR. Conclusions The integration of SL methods in epidemiology may improve both the understanding and interpretation of complex exposure/disease relationships.

Attempted suicide among transgender persons: The influence of gender-based discrimination and victimization.

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Clements-Nolle, Kristen; Marx, Rani; Katz, Mitchell

2006-01-01

To determine the independent predictors of attempted suicide among transgender persons we interviewed 392 male-to-female (MTF) and 123 female-to-male (FTM) individuals. Participants were recruited through targeted sampling, respondent-driven sampling, and agency referrals in San Francisco. The prevalence of attempted suicide was 32% (95% CI = 28% to 36%). In multivariate logistic regression analysis younger age (discrimination, and gender-based victimization were independently associated with attempted suicide. Suicide prevention interventions for transgender persons are urgently needed, particularly for young people. Medical, mental health, and social service providers should address depression, substance abuse, and forced sex in an attempt to reduce suicidal behaviors among transgender persons. In addition, increasing societal acceptance of the transgender community and decreasing gender-based prejudice may help prevent suicide in this highly stigmatized population.

Interpretation of commonly used statistical regression models.

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Kasza, Jessica; Wolfe, Rory

2014-01-01

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

Social organization and social ties: their effects on sexual harassment victimization in the workplace.

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Snyder, Jamie A; Scherer, Heidi L; Fisher, Bonnie S

2012-01-01

Despite work organizations' attempts to reduce sexual harassment, it continues to be a salient issue for employers across all occupations. Extending social disorganization theory to the work environment, this study examines the relationship between workplace organization, social ties, and sexual harassment victimization. Survey responses to the 2002 and 2006 Quality of Working Life module from the General Social Survey by a sample of 3,530 adult men and women employees in the United States were used. Logistic regression models were estimated for men and women separately to estimate the effect of workplace characteristics on the risk of sexual harassment victimization. Employees who reported poor workplace relations between management and employees and lower coworker social ties were more likely to experience sexual harassment in their work environments. Specific workplace characteristics such as low productivity, poor time management, and inadequate administrative support were significantly related to increased sexual harassment risk. No significant gender differences were found across models suggesting that the predictors of sexual harassment are similar for men and women. This study demonstrates that workplace characteristics are related to sexual harassment risk in the workplace. Suggestions for sexual harassment prevention, including management and organizational strategies, are discussed.

The association between chronic bullying victimization with weight status and body self-image: a cross-national study in 39 countries

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Qiguo Lian

2018-01-01

Full Text Available Background Childhood obesity and school bullying are pervasive public health issues and known to co-occur in adolescents. However, the association between underweight or thinness and chronic bullying victimization is unclear. The current study examined whether chronic bullying victimization is associated with weight status and body self-image. Methods A school-based, cross-sectional study in 39 North American and European countries and regions was conducted. A total of 213,595 adolescents aged 11, 13, and 15 years were surveyed in 2009/10. Chronic bullying victimization was identified using the Revised Olweus Bully/Victim Questionnaire. Weight status was determined using self-reported height and weight and the body mass index (BMI, and body self-image was based on perceived weight. We tested associations between underweight and bullying victimization using three-level logistic regression models. Results Of the 213,595 adolescents investigated, 11.28% adolescents reported chronic bullying victimization, 14.80% were classified as overweight/obese according to age- and sex-specific BMI criteria, 12.97% were underweight, and 28.36% considered themselves a little bit fat or too fat, 14.57% were too thin. Bullying victimization was less common in older adolescent boys and girls. Weight status was associated with chronic bullying victimization (adjusted ORunderweight = 1.10, 95% CI = 1.05–1.16, p = 0.002; adjusted ORoverweight = 1.40, 95% CI = 1.32–1.49, p < 0.0001; adjusted ORobese = 1.91, 95% CI = 1.71–2.14, p < 0.0001. Body self-image also related to chronic bullying victimization (adjusted ORtoo thin = 1.42, 95% CI = 1.36–1.49, p < 0.0001; adjusted ORa little bit fat = 1.54, 95% CI = 1.48–1.61, p < 0.0001; adjusted ORtoo fat = 3.30, 95% CI = 2.96–3.68, p < 0.0001. Conclusion Both perceived weight and self-rated overweight are associated with chronic bullying victimization. Both overweight and underweight children are at risk of being

[Multivariate ordinal logistic regression analysis on the association between consumption of fried food and both esophageal cancer and precancerous lesions].

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Guo, L W; Liu, S Z; Zhang, M; Chen, Q; Zhang, S K; Sun, X B

2017-12-10

Objective: To investigate the effect of fried food intake on the pathogenesis of esophageal cancer and precancerous lesions. Methods: From 2005 to 2013, all the residents aged 40-69 years from 11 counties (cities) where cancer screening of upper gastrointestinal cancer had been conducted in rural areas of Henan province, were recruited as the subjects of study. Information on demography and lifestyle was collected. The residents under study were screened with iodine staining endoscopic examination and biopsy samples were diagnosed pathologically, under standardized criteria. Subjects with high risk were divided into the groups based on their different pathological degrees. Multivariate ordinal logistic regression analysis was used to analyze the relationship between the frequency of fried food intake and esophageal cancer and precancerous lesions. Results: A total number of 8 792 cases with normal esophagus, 3 680 with mild hyperplasia, 972 with moderate hyperplasia, 413 with severe hyperplasia carcinoma in situ, and 336 cases of esophageal cancer were recruited. Results from multivariate logistic regression analysis showed that, when compared with those who did not eat fried food, the intake of fried food (food appeared a risk factor for both esophageal cancer and precancerous lesions.

Logistic regression analysis of the risk factors of anastomotic fistula after radical resection of esophageal‐cardiac cancer

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Huang, Jinxi; Wang, Chenghu; Yuan, Weiwei; Zhang, Zhandong; Chen, Beibei; Zhang, Xiefu

2017-01-01

Background This study was conducted to investigate the risk factors of anastomotic fistula after the radical resection of esophageal‐cardiac cancer. Methods Five hundred and forty‐four esophageal‐cardiac cancer patients who underwent surgery and had complete clinical data were included in the study. Fifty patients diagnosed with postoperative anastomotic fistula were considered the case group and the remaining 494 subjects who did not develop postoperative anastomotic fistula were considered the control. The potential risk factors for anastomotic fistula, such as age, gender, diabetes history, smoking history, were collected and compared between the groups. Statistically significant variables were substituted into logistic regression to further evaluate the independent risk factors for postoperative anastomotic fistulas in esophageal‐cardiac cancer. Results The incidence of anastomotic fistulas was 9.2% (50/544). Logistic regression analysis revealed that female gender (P < 0.05), laparoscopic surgery (P < 0.05), decreased postoperative albumin (P < 0.05), and postoperative renal dysfunction (P < 0.05) were independent risk factors for anastomotic fistulas in patients who received surgery for esophageal‐cardiac cancer. Of the 50 anastomotic fistulas, 16 cases were small fistulas, which were only discovered by conventional imaging examination and not presenting clinical symptoms. All of the anastomotic fistulas occurred within seven days after surgery. Five of the patients with anastomotic fistulas underwent a second surgery and three died. Conclusion Female patients with esophageal‐cardiac cancer treated with endoscopic surgery and suffering from postoperative hypoproteinemia and renal dysfunction were susceptible to postoperative anastomotic fistula. PMID:28940985

Factors contributing to perceptions about policies regarding the electronic monitoring of sex offenders: the role of demographic characteristics, victimization experiences, and social disorganization.

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Button, Deeanna M; Tewksbury, Richard; Mustaine, Elizabeth E; Payne, Brian K

2013-01-01

The purpose of this article is to explore factors contributing to perceptions about electronic monitoring policies governing sex offenders. Guided by Tannenbaum's theory of attribution and Shaw and McKay's theory of social disorganization, the authors examine the influence of demographic characteristics, victimization experiences, and neighborhood characteristics on perceptions about policies regarding the electronic monitoring of sex offenders. Ordinary least squares regression and logistic regression analyses of stratified telephone survey data reveal that factors associated with favorable views on the use of global positioning satellite monitoring for registered sex offenders appear to stem primarily from individuals' demographic characteristics. Experiential and neighborhood factors do provide some influence over individuals' views of electronic monitoring policies for sex offenders. Theoretical and policy implications are discussed.

Propensity score matching of the gymnastics for diabetes mellitus using logistic regression

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Otok, Bambang Widjanarko; Aisyah, Amalia; Purhadi, Andari, Shofi

2017-12-01

Diabetes Mellitus (DM) is a group of metabolic diseases with characteristics shows an abnormal blood glucose level occurring due to pancreatic insulin deficiency, decreased insulin effectiveness or both. The report from the ministry of health shows that DMs prevalence data of East Java province is 2.1%, while the DMs prevalence of Indonesia is only 1,5%. Given the high cases of DM in East Java, it needs the preventive action to control factors causing the complication of DM. This study aims to determine the combination factors causing the complication of DM to reduce the bias by confounding variables using Propensity Score Matching (PSM) with the method of propensity score estimation is binary logistic regression. The data used in this study is the medical record from As-Shafa clinic consisting of 6 covariates and health complication as response variable. The result of PSM analysis showed that there are 22 of 126 DMs patients attending gymnastics paired with patients who didnt attend to diabetes gymnastics. The Average Treatment of Treated (ATT) estimation results showed that the more patients who didnt attend to gymnastics, the more likely the risk for the patients having DMs complications.

Factors Associated with the Persistence of Bullying Victimization From 10th grade to 13th Grade: A Longitudinal Study.

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Lien, Lars; Welander-Vatn, Audun

2013-01-01

Bullying among adolescents represents a major public health challenge. The aim of this study was to map the stability of bullying victimization across the transitional phase from lower to upper secondary school, and to describe the sociodemographic, academic and health-related characteristics of those bullied during the transition. 3674 Norwegian adolescents were followed longitudinally from the age of 15/16 until the age of 18/19, answering questionnaires about health, academic achievements, life events, lifestyle and sociodemography. The 337 participants reporting exposure to bullying victimization at age 15/16 were the target group, as we made comparisons between those reporting victimization only at the age of 15/16 (n=289) with the participants for whom the bullying had continued into later adolescence (n = 48). 14% of those victimized at age 15/16, reported continuation of bullying victimization into upper secondary school. These adolescents were significantly more likely to report having divorced parents, low parental educational level, poor self-perceived economy, muscle and skeletal pain, symptoms of mental distress, lower school marks in Norwegian and higher body-mass index (BMI) when group differences at age 18/19 were assessed through basic inferential statistical tests. However, the multivariate logistic regression analyses only revealed statistically significantly increased adjusted odds ratios for the variables mental distress and school-marks in Norwegian. The persistence of exposure to bullying from 10th grade to 13th grade is associated with mental health complaints and poor school performance. Preventive measures to take care of students being continuously bullied should be in place in secondary schools.

Tropical Forest Fire Susceptibility Mapping at the Cat Ba National Park Area, Hai Phong City, Vietnam, Using GIS-Based Kernel Logistic Regression

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Dieu Tien Bui

2016-04-01

Full Text Available The Cat Ba National Park area (Vietnam with its tropical forest is recognized as being part of the world biodiversity conservation by the United Nations Educational, Scientific and Cultural Organization (UNESCO and is a well-known destination for tourists, with around 500,000 travelers per year. This area has been the site for many research projects; however, no project has been carried out for forest fire susceptibility assessment. Thus, protection of the forest including fire prevention is one of the main concerns of the local authorities. This work aims to produce a tropical forest fire susceptibility map for the Cat Ba National Park area, which may be helpful for the local authorities in forest fire protection management. To obtain this purpose, first, historical forest fires and related factors were collected from various sources to construct a GIS database. Then, a forest fire susceptibility model was developed using Kernel logistic regression. The quality of the model was assessed using the Receiver Operating Characteristic (ROC curve, area under the ROC curve (AUC, and five statistical evaluation measures. The usability of the resulting model is further compared with a benchmark model, the support vector machine (SVM. The results show that the Kernel logistic regression model has a high level of performance in both the training and validation dataset, with a prediction capability of 92.2%. Since the Kernel logistic regression model outperforms the benchmark model, we conclude that the proposed model is a promising alternative tool that should also be considered for forest fire susceptibility mapping in other areas. The results of this study are useful for the local authorities in forest planning and management.

Risk of Recurrence in Operated Parasagittal Meningiomas: A Logistic Binary Regression Model.

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Escribano Mesa, José Alberto; Alonso Morillejo, Enrique; Parrón Carreño, Tesifón; Huete Allut, Antonio; Narro Donate, José María; Méndez Román, Paddy; Contreras Jiménez, Ascensión; Pedrero García, Francisco; Masegosa González, José

2018-02-01

Parasagittal meningiomas arise from the arachnoid cells of the angle formed between the superior sagittal sinus (SSS) and the brain convexity. In this retrospective study, we focused on factors that predict early recurrence and recurrence times. We reviewed 125 patients with parasagittal meningiomas operated from 1985 to 2014. We studied the following variables: age, sex, location, laterality, histology, surgeons, invasion of the SSS, Simpson removal grade, follow-up time, angiography, embolization, radiotherapy, recurrence and recurrence time, reoperation, neurologic deficit, degree of dependency, and patient status at the end of follow-up. Patients ranged in age from 26 to 81 years (mean 57.86 years; median 60 years). There were 44 men (35.2%) and 81 women (64.8%). There were 57 patients with neurologic deficits (45.2%). The most common presenting symptom was motor deficit. World Health Organization grade I tumors were identified in 104 patients (84.6%), and the majority were the meningothelial type. Recurrence was detected in 34 cases. Time of recurrence was 9 to 336 months (mean: 84.4 months; median: 79.5 months). Male sex was identified as an independent risk for recurrence with relative risk 2.7 (95% confidence interval 1.21-6.15), P = 0.014. Kaplan-Meier curves for recurrence had statistically significant differences depending on sex, age, histologic type, and World Health Organization histologic grade. A binary logistic regression was made with the Hosmer-Lemeshow test with P > 0.05; sex, tumor size, and histologic type were used in this model. Male sex is an independent risk factor for recurrence that, associated with other factors such tumor size and histologic type, explains 74.5% of all cases in a binary regression model. Copyright © 2017 Elsevier Inc. All rights reserved.

Logistic Regression Analysis on Factors Affecting Adoption of Rice-Fish Farming in North Iran

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Seyyed Ali NOORHOSSEINI-NIYAKI

2012-06-01

Full Text Available We evaluated the factors influencing the adoption of rice-fish farming in the Tavalesh region near the Caspian Sea in northern Iran. We conducted a survey with open-ended questions. Data were collected from 184 respondents (61 adopters and 123 non-adopters randomly sampled from selected villages and analyzed using logistic regression and multi-response analysis. Family size, number of contacts with an extension agent, participation in extension-education activities, membership in social institutions and the presence of farm workers were the most important socio-economic factors for the adoption of rice-fish farming system. In addition, economic problems were the most common issue reported by adopters. Other issues such as lack of access to appropriate fish food, losses of fish, lack of access to high quality fish fingerlings and dehydration and poor water quality were also important to a number of farmers.

SU-F-R-22: Malignancy Classification for Small Pulmonary Nodules with Radiomics and Logistic Regression

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Huang, W; Tu, S [Chang Gung University, Kwei-shan, Tao-Yuan, Taiwan (China)

2016-06-15

Purpose: We conducted a retrospective study of Radiomics research for classifying malignancy of small pulmonary nodules. A machine learning algorithm of logistic regression and open research platform of Radiomics, IBEX (Imaging Biomarker Explorer), were used to evaluate the classification accuracy. Methods: The training set included 100 CT image series from cancer patients with small pulmonary nodules where the average diameter is 1.10 cm. These patients registered at Chang Gung Memorial Hospital and received a CT-guided operation of lung cancer lobectomy. The specimens were classified by experienced pathologists with a B (benign) or M (malignant). CT images with slice thickness of 0.625 mm were acquired from a GE BrightSpeed 16 scanner. The study was formally approved by our institutional internal review board. Nodules were delineated and 374 feature parameters were extracted from IBEX. We first used the t-test and p-value criteria to study which feature can differentiate between group B and M. Then we implemented a logistic regression algorithm to perform nodule malignancy classification. 10-fold cross-validation and the receiver operating characteristic curve (ROC) were used to evaluate the classification accuracy. Finally hierarchical clustering analysis, Spearman rank correlation coefficient, and clustering heat map were used to further study correlation characteristics among different features. Results: 238 features were found differentiable between group B and M based on whether their statistical p-values were less than 0.05. A forward search algorithm was used to select an optimal combination of features for the best classification and 9 features were identified. Our study found the best accuracy of classifying malignancy was 0.79±0.01 with the 10-fold cross-validation. The area under the ROC curve was 0.81±0.02. Conclusion: Benign nodules may be treated as a malignant tumor in low-dose CT and patients may undergo unnecessary surgeries or treatments. Our

DETERMINATION OF FACTORS AFFECTING LENGTH OF STAY WITH MULTINOMIAL LOGISTIC REGRESSION IN TURKEY

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Öğr. Gör. Rukiye NUMAN TEKİN

2016-08-01

Full Text Available Length of stay (LOS has important implications in various aspects of health services, can vary according to a wide range of factors. It is noticed that LOS has been neglected mostly in both theoratical studies and practice of health care management in Turkey. The main purpose of this study is to identify factors related to LOS in Turkey. A retrospective analysis of 2.255.836 patients hospitalized to private, university, foundation university and other (municipality, association and foreigners/minority hospitals hospitals which have an agreement with Social Security Institution (SSI in Turkey, from January 1, 2010, until the December 31, 2010, was examined. Patient’s data were taken from MEDULA (National Electronic Invoice System and SPSS 18.0 was used to perform statistical analysis. In this study t-test, one way anova and multinomial logistic regression are used to determine variables that may affect to LOS. The average LOS of patients was 3,93 days (SD = 5,882. LOS showed a statistically significant difference according to all independent variables used in the study (age, gender, disease class, type of hospitalization, presence of comorbidity, type and number of surgery, season of hospitalization, hospital ownership/bed capacity/ geographical region/residential area/type of service. According to the results of the multinomial lojistic regression analysis, LOS was negatively affected in terms of gender, presence of comorbidity, geographical region of hospital and was positively affected in terms of age, season of hospitalization, hospital bed capacity/ ownership/type of service/residential area.

Depression as a mediator between family factors and peer-bullying victimization in Latino adolescents.

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Yabko, Brandon A; Hokoda, Audrey; Ulloa, Emilio C

2008-01-01

The purpose of this study was to assess the mediating role of depression in three different relationships: (a) sibling bullying and peer victimization, (b) mothers' power-assertive parenting and peer victimization, and (c) fathers' power-assertive parenting and peer victimization. Results from 242 Latino middle school adolescents from a large southwestern city bordering Mexico revealed that both boys' and girls' peer victimization were related to familial factors and depression. Regression analyses for boys revealed that depression mediated three relationships: (a) sibling bullying and peer victimization, (b) mothers' power-assertive parenting and peer victimization, and (c) fathers' power-assertive parenting and peer victimization. Depression also mediated the relationship between fathers' power-assertive parenting and girls' victimization by peers. The findings support the development of family-based interventions for peer victimization that include curriculum addressing depression.

The aflatoxin-affair: the invisible victims of crime in the food-sector

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Kerschke-Risch Pamela

2014-01-01

Full Text Available The aflatoxin affair is an example which can be assumed as a typical offence committed in the food sector in a globalized world. In 2013 mouldy Serbian feed was distributed by an international logistics company to Germany. The exceptional danger of aflatoxin infested feed is the carry over effect, which means that harmful substances devolve into animal products like milk. Generally speaking victims are identifiable persons who have been physically injured or suffer from financial losses or psychological damage. In contrast to e.g. victims of violence we know almost nothing about the effects of victimization as a result of offences committed in the food sector. The aim of this article is to show and discuss the possible effects of the aflatoxin scandal on consumers who have been victimized. As a result it suggests that victimization effects of offences related to food in general are ignored hitherto both by policy and criminologists.

Self-esteem in adolescent aggression perpetrators, victims and perpetrator-victims, and the moderating effects of depression and family support

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Peng-Wei Wang

2013-04-01

Full Text Available The aims of this study were (1 to examine differences in the level of self-esteem among adolescents with different roles in aggression involvement (aggression perpetrators, victims, perpetrator-victims and neutrals according to gender and (2 to examine the moderating effects of depression and family support on association between aggression involvement and self-esteem. A total of 8085 adolescents in Taiwan completed questionnaires. The relationships between self-esteem and aggression involvement were examined by multiple regression analysis. The moderating effects of depression and family support on the association between aggression involvement and self-esteem were examined. The results showed that in females, aggression victims had lower self-esteem than those in the other three groups (t=−2.940 to 2.173, p0.05. In males, self-esteem in victims and perpetrator-victims was lower than in neutrals and perpetrators (t=−3.339 to −2.704, p0.05 or between perpetrators and neutrals (t=−1.396, p>0.05. Family support had a moderating effect on the association between self-esteem and victimization in males. Depression had a moderating effect on the association between self-esteem and perpetration-victimization and victimization in males. The results indicate that self-esteem in adolescents with different patterns of involvement in aggression is not the same as in those without involvement. The moderating effects of depression and family support should be considered when developing intervention strategies to raise self-esteem in adolescents with aggression involvement.

Self-esteem in adolescent aggression perpetrators, victims and perpetrator-victims, and the moderating effects of depression and family support.

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Wang, Peng-Wei; Yang, Pin-Chen; Yeh, Yi-Chun; Lin, Huang-Chi; Ko, Chih-Hung; Liu, Tai-Ling; Yen, Cheng-Fang

2013-04-01

The aims of this study were (1) to examine differences in the level of self-esteem among adolescents with different roles in aggression involvement (aggression perpetrators, victims, perpetrator-victims and neutrals) according to gender and (2) to examine the moderating effects of depression and family support on association between aggression involvement and self-esteem. A total of 8085 adolescents in Taiwan completed questionnaires. The relationships between self-esteem and aggression involvement were examined by multiple regression analysis. The moderating effects of depression and family support on the association between aggression involvement and self-esteem were examined. The results showed that in females, aggression victims had lower self-esteem than those in the other three groups (t=-2.940 to 2.173, p0.05). In males, self-esteem in victims and perpetrator-victims was lower than in neutrals and perpetrators (t=-3.339 to -2.704, p0.05) or between perpetrators and neutrals (t=-1.396, p>0.05). Family support had a moderating effect on the association between self-esteem and victimization in males. Depression had a moderating effect on the association between self-esteem and perpetration-victimization and victimization in males. The results indicate that self-esteem in adolescents with different patterns of involvement in aggression is not the same as in those without involvement. The moderating effects of depression and family support should be considered when developing intervention strategies to raise self-esteem in adolescents with aggression involvement. Copyright © 2012. Published by Elsevier B.V.

Demand analysis of flood insurance by using logistic regression model and genetic algorithm

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Sidi, P.; Mamat, M. B.; Sukono; Supian, S.; Putra, A. S.

2018-03-01

Citarum River floods in the area of South Bandung Indonesia, often resulting damage to some buildings belonging to the people living in the vicinity. One effort to alleviate the risk of building damage is to have flood insurance. The main obstacle is not all people in the Citarum basin decide to buy flood insurance. In this paper, we intend to analyse the decision to buy flood insurance. It is assumed that there are eight variables that influence the decision of purchasing flood assurance, include: income level, education level, house distance with river, building election with road, flood frequency experience, flood prediction, perception on insurance company, and perception towards government effort in handling flood. The analysis was done by using logistic regression model, and to estimate model parameters, it is done with genetic algorithm. The results of the analysis shows that eight variables analysed significantly influence the demand of flood insurance. These results are expected to be considered for insurance companies, to influence the decision of the community to be willing to buy flood insurance.

A Comparative Study of Cox Regression vs. Log-Logistic ...

African Journals Online (AJOL)

Colorectal cancer is common and lethal disease with different incidence rate in different parts of the world which is taken into account as the third cause of cancer-related deaths. In the present study, using non-parametric Cox model and parametric Log-logistic model, factors influencing survival of patients with colorectal ...

College student engaging in cyberbullying victimization: cognitive appraisals, coping strategies, and psychological adjustments.

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Na, Hyunjoo; Dancy, Barbara L; Park, Chang

2015-06-01

The study's purpose was to explore whether frequency of cyberbullying victimization, cognitive appraisals, and coping strategies were associated with psychological adjustments among college student cyberbullying victims. A convenience sample of 121 students completed questionnaires. Linear regression analyses found frequency of cyberbullying victimization, cognitive appraisals, and coping strategies respectively explained 30%, 30%, and 27% of the variance in depression, anxiety, and self-esteem. Frequency of cyberbullying victimization and approach and avoidance coping strategies were associated with psychological adjustments, with avoidance coping strategies being associated with all three psychological adjustments. Interventions should focus on teaching cyberbullying victims to not use avoidance coping strategies. Copyright © 2015 Elsevier Inc. All rights reserved.

Discriminating between adaptive and carcinogenic liver hypertrophy in rat studies using logistic ridge regression analysis of toxicogenomic data: The mode of action and predictive models

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Liu, Shujie; Kawamoto, Taisuke; Morita, Osamu [R& D, Safety Science Research, Kao Corporation, Tochigi (Japan); Yoshinari, Kouichi [Department of Molecular Toxicology, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka (Japan); Honda, Hiroshi, E-mail: honda.hiroshi@kao.co.jp [R& D, Safety Science Research, Kao Corporation, Tochigi (Japan)

2017-03-01

Chemical exposure often results in liver hypertrophy in animal tests, characterized by increased liver weight, hepatocellular hypertrophy, and/or cell proliferation. While most of these changes are considered adaptive responses, there is concern that they may be associated with carcinogenesis. In this study, we have employed a toxicogenomic approach using a logistic ridge regression model to identify genes responsible for liver hypertrophy and hypertrophic hepatocarcinogenesis and to develop a predictive model for assessing hypertrophy-inducing compounds. Logistic regression models have previously been used in the quantification of epidemiological risk factors. DNA microarray data from the Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System were used to identify hypertrophy-related genes that are expressed differently in hypertrophy induced by carcinogens and non-carcinogens. Data were collected for 134 chemicals (72 non-hypertrophy-inducing chemicals, 27 hypertrophy-inducing non-carcinogenic chemicals, and 15 hypertrophy-inducing carcinogenic compounds). After applying logistic ridge regression analysis, 35 genes for liver hypertrophy (e.g., Acot1 and Abcc3) and 13 genes for hypertrophic hepatocarcinogenesis (e.g., Asns and Gpx2) were selected. The predictive models built using these genes were 94.8% and 82.7% accurate, respectively. Pathway analysis of the genes indicates that, aside from a xenobiotic metabolism-related pathway as an adaptive response for liver hypertrophy, amino acid biosynthesis and oxidative responses appear to be involved in hypertrophic hepatocarcinogenesis. Early detection and toxicogenomic characterization of liver hypertrophy using our models may be useful for predicting carcinogenesis. In addition, the identified genes provide novel insight into discrimination between adverse hypertrophy associated with carcinogenesis and adaptive hypertrophy in risk assessment. - Highlights: • Hypertrophy (H) and hypertrophic

Discriminating between adaptive and carcinogenic liver hypertrophy in rat studies using logistic ridge regression analysis of toxicogenomic data: The mode of action and predictive models

International Nuclear Information System (INIS)

Liu, Shujie; Kawamoto, Taisuke; Morita, Osamu; Yoshinari, Kouichi; Honda, Hiroshi

2017-01-01

Chemical exposure often results in liver hypertrophy in animal tests, characterized by increased liver weight, hepatocellular hypertrophy, and/or cell proliferation. While most of these changes are considered adaptive responses, there is concern that they may be associated with carcinogenesis. In this study, we have employed a toxicogenomic approach using a logistic ridge regression model to identify genes responsible for liver hypertrophy and hypertrophic hepatocarcinogenesis and to develop a predictive model for assessing hypertrophy-inducing compounds. Logistic regression models have previously been used in the quantification of epidemiological risk factors. DNA microarray data from the Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System were used to identify hypertrophy-related genes that are expressed differently in hypertrophy induced by carcinogens and non-carcinogens. Data were collected for 134 chemicals (72 non-hypertrophy-inducing chemicals, 27 hypertrophy-inducing non-carcinogenic chemicals, and 15 hypertrophy-inducing carcinogenic compounds). After applying logistic ridge regression analysis, 35 genes for liver hypertrophy (e.g., Acot1 and Abcc3) and 13 genes for hypertrophic hepatocarcinogenesis (e.g., Asns and Gpx2) were selected. The predictive models built using these genes were 94.8% and 82.7% accurate, respectively. Pathway analysis of the genes indicates that, aside from a xenobiotic metabolism-related pathway as an adaptive response for liver hypertrophy, amino acid biosynthesis and oxidative responses appear to be involved in hypertrophic hepatocarcinogenesis. Early detection and toxicogenomic characterization of liver hypertrophy using our models may be useful for predicting carcinogenesis. In addition, the identified genes provide novel insight into discrimination between adverse hypertrophy associated with carcinogenesis and adaptive hypertrophy in risk assessment. - Highlights: • Hypertrophy (H) and hypertrophic

Logistic regression analysis of prognostic factors in 106 acute-on-chronic liver failure patients with hepatic encephalopathy

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CUI Yanping

2014-10-01

Full Text Available ObjectiveTo analyze the prognostic factors in acute-on-chronic liver failure (ACLF patients with hepatic encephalopathy (HE and to explore the risk factors for prognosis. MethodsA retrospective analysis was performed on 106 ACLF patients with HE who were hospitalized in our hospital from January 2010 to July 2013. The patients were divided into improved group and deteriorated group. The univariate indicators including age, sex, laboratory indicators ［total bilirubin (TBil, albumin (Alb, alanine aminotransferase (ALT, aspartate amino-transferase (AST, and prothrombin time activity (PTA］, the stage of HE, complications ［persistent hyponatremia, digestive tract bleeding, hepatorenal syndrome (HRS, ascites, infection, and spontaneous bacterial peritonitis (SBP］, and plasma exchange were analyzed by chi-square test or t-test. Indicators with statistical significance were subsequently analyzed by binary logistic regression. ResultsUnivariate analysis showed that ALT (P=0.009, PTA (P=0.043, the stage of HE (P=0.000, and HRS (P=0.003 were significantly different between the two groups, whereas differences in age, sex, TBil, Alb, AST, persistent hyponatremia, digestive tract bleeding, ascites, infection, SBP, and plasma exchange were not statistically significant (P＞0.05. Binary logistic regression demonstrated that PTA (b=-0097, P=0.025, OR=0.908, HRS (b=2.279, P=0.007, OR=9.764, and the stage of HE (b=1873, P=0.000, OR=6.510 were prognostic factors in ACLF patients with HE. ConclusionThe stage of HE, HRS, and PTA are independent influential factors for the prognosis in ACLF patients with HE. Reduced PTA, advanced HE stage, and the presence of HRS indicate worse prognosis.

Use of geographically weighted logistic regression to quantify spatial variation in the environmental and sociodemographic drivers of leptospirosis in Fiji: a modelling study

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Helen J Mayfield, PhD

2018-05-01

Full Text Available Summary: Background: Leptospirosis is a globally important zoonotic disease, with complex exposure pathways that depend on interactions between human beings, animals, and the environment. Major drivers of outbreaks include flooding, urbanisation, poverty, and agricultural intensification. The intensity of these drivers and their relative importance vary between geographical areas; however, non-spatial regression methods are incapable of capturing the spatial variations. This study aimed to explore the use of geographically weighted logistic regression (GWLR to provide insights into the ecoepidemiology of human leptospirosis in Fiji. Methods: We obtained field data from a cross-sectional community survey done in 2013 in the three main islands of Fiji. A blood sample obtained from each participant (aged 1–90 years was tested for anti-Leptospira antibodies and household locations were recorded using GPS receivers. We used GWLR to quantify the spatial variation in the relative importance of five environmental and sociodemographic covariates (cattle density, distance to river, poverty rate, residential setting [urban or rural], and maximum rainfall in the wettest month on leptospirosis transmission in Fiji. We developed two models, one using GWLR and one with standard logistic regression; for each model, the dependent variable was the presence or absence of anti-Leptospira antibodies. GWLR results were compared with results obtained with standard logistic regression, and used to produce a predictive risk map and maps showing the spatial variation in odds ratios (OR for each covariate. Findings: The dataset contained location information for 2046 participants from 1922 households representing 81 communities. The Aikaike information criterion value of the GWLR model was 1935·2 compared with 1254·2 for the standard logistic regression model, indicating that the GWLR model was more efficient. Both models produced similar OR for the covariates, but

The Code of the Street and Violent Versus Property Crime Victimization.

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McNeeley, Susan; Wilcox, Pamela

2015-01-01

Previous research has shown that individuals who adopt values in line with the code of the street are more likely to experience violent victimization (e.g., Stewart, Schreck, & Simons, 2006). This study extends this literature by examining the relationship between the street code and multiple types of violent and property victimization. This research investigates the relationship between street code-related values and 4 types of victimization (assault, breaking and entering, theft, and vandalism) using Poisson-based multilevel regression models. Belief in the street code was associated with higher risk of experiencing assault, breaking and entering, and vandalism, whereas theft victimization was not related to the street code. The results suggest that the code of the street influences victimization broadly--beyond violence--by increasing behavior that provokes retaliation from others in various forms.

Serious Violence Victimization and Perpetration among Youth Living in the Slums of Kampala, Uganda

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Monica H. Swahn

2012-08-01

Full Text Available Introduction: Violence among youth is a major public health issue globally. Despite these concerns, youth violence surveillance and prevention research are either scarce or non-existent, particularly in developing regions, such as sub-Saharan Africa. The purpose of this study is to quantitatively determine the prevalence of violence involving weapons in a convenience sample of service-seeking youth in Kampala. Moreover, the study will seek to determine the overlap between violence victimization and perpetration among these youth and the potentially shared risk factors for these experiences.Methods: We conducted this study of youth in May and June of 2011 to quantify and describe high-risk behaviors and exposures in a convenience sample (N¼457 of urban youth, 14–24 years of age, living on the streets or in the slums and who were participating in a Uganda Youth Development Link drop-incenter for disadvantaged street youth. We computed bivariate and multivariate logistic regression analyses to determine associations between psychosocial factors and violence victimization and perpetration.Results: The overall prevalence of reporting violence victimization involving a weapon was 36%, and violence perpetration with a weapon was 19%. In terms of the overlap between victimization and perpetration, 16.6% of youth (11.6% of boys and 24.1% of girls reported both. In multivariate analyses, parental neglect due to alcohol use (Adj.OR¼2.28;95%CI: 1.12—4.62 and sadness (Adj.OR=4.36 ;95%CI: 1.81—10.53 were the statistically significant correlates of victimization only. Reportinghunger (Adj.OR=2.87 ;95%CI:1.30—6.33, any drunkenness (Adj.OR=2.35 ;95%CI:1.12—4.92 and any drug use (Adj.OR=3.02 ;95%CI:1.16—7.82 were significantly associated with both perpetration and victimization.Conclusion: The findings underscore the differential experiences associated with victimization and perpetration of violence involving weapons among these vulnerable youth. In

Computational procedures for probing interactions in OLS and logistic regression: SPSS and SAS implementations.

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Hayes, Andrew F; Matthes, Jörg

2009-08-01

Researchers often hypothesize moderated effects, in which the effect of an independent variable on an outcome variable depends on the value of a moderator variable. Such an effect reveals itself statistically as an interaction between the independent and moderator variables in a model of the outcome variable. When an interaction is found, it is important to probe the interaction, for theories and hypotheses often predict not just interaction but a specific pattern of effects of the focal independent variable as a function of the moderator. This article describes the familiar pick-a-point approach and the much less familiar Johnson-Neyman technique for probing interactions in linear models and introduces macros for SPSS and SAS to simplify the computations and facilitate the probing of interactions in ordinary least squares and logistic regression. A script version of the SPSS macro is also available for users who prefer a point-and-click user interface rather than command syntax.

Social networking sites and mental health problems in adolescents: The mediating role of cyberbullying victimization.

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Sampasa-Kanyinga, H; Hamilton, H A

2015-11-01

Previous research has suggested an association between the use of social networking sites (SNSs) and mental health problems such as psychological distress, suicidal ideation and attempts in adolescents. However, little is known about the factors that might mediate these relationships. The present study examined the link between the use of social networking sites and psychological distress, suicidal ideation and suicide attempts, and tested the mediating role of cyberbullying victimization on these associations in adolescents. The sample consisted of a group of 11-to-20-year-old individuals (n=5126, 48% females; mean±SD age: 15.2±1.9 years) who completed the mental health portion of the Ontario Student Drug Use and Health Survey (OSDUHS) in 2013. Multiple logistic regression analyses were used to test the mediation models. After adjustment for age, sex, ethnicity, subjective socioeconomic status (SES), and parental education, use of SNSs was associated with psychological distress (adjusted odds ratio, 95% confidence interval=2.03, 1.22-3.37), suicidal ideation (3.44, 1.54-7.66) and attempts (5.10, 1.45-17.88). Cyberbullying victimization was found to fully mediate the relationships between the use of SNSs with psychological distress and attempts; whereas, it partially mediated the link between the use of SNSs and suicidal ideation. Findings provide supporting evidence that addressing cyberbullying victimization and the use of SNSs among adolescents may help reduce the risk of mental health problems. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey

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Ozdemir, Adnan; Altural, Tolga

2013-03-01

This study evaluated and compared landslide susceptibility maps produced with three different methods, frequency ratio, weights of evidence, and logistic regression, by using validation datasets. The field surveys performed as part of this investigation mapped the locations of 90 landslides that had been identified in the Sultan Mountains of south-western Turkey. The landslide influence parameters used for this study are geology, relative permeability, land use/land cover, precipitation, elevation, slope, aspect, total curvature, plan curvature, profile curvature, wetness index, stream power index, sediment transportation capacity index, distance to drainage, distance to fault, drainage density, fault density, and spring density maps. The relationships between landslide distributions and these parameters were analysed using the three methods, and the results of these methods were then used to calculate the landslide susceptibility of the entire study area. The accuracy of the final landslide susceptibility maps was evaluated based on the landslides observed during the fieldwork, and the accuracy of the models was evaluated by calculating each model's relative operating characteristic curve. The predictive capability of each model was determined from the area under the relative operating characteristic curve and the areas under the curves obtained using the frequency ratio, logistic regression, and weights of evidence methods are 0.976, 0.952, and 0.937, respectively. These results indicate that the frequency ratio and weights of evidence models are relatively good estimators of landslide susceptibility in the study area. Specifically, the results of the correlation analysis show a high correlation between the frequency ratio and weights of evidence results, and the frequency ratio and logistic regression methods exhibit correlation coefficients of 0.771 and 0.727, respectively. The frequency ratio model is simple, and its input, calculation and output processes are

Victimization and psychopathic features in a population-based sample of Finnish adolescents.

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Saukkonen, Suvi; Aronen, Eeva T; Laajasalo, Taina; Salmi, Venla; Kivivuori, Janne; Jokela, Markus

2016-10-01

We examined different forms of victimization experiences in relation to psychopathic features and whether these associations differed in boys and girls among 4855 Finnish school adolescents aged 15-16 years. Psychopathic features were measured with the Antisocial Process Screening Device- Self Report (APSD-SR). Victimization was assessed with questions about violent and abusive experiences across lifetime and within the last 12 months. Results from linear regression analysis showed that victimization was significantly associated with higher APSD-SR total scores, more strongly in girls than boys. Recent (12-month) victimization showed significance in the relationship between victimization and psychopathic features; especially recent sexual abuse and parental corporal punishment were strong determinants of higher APSD-SR total scores. The present study demonstrates novel findings on how severe victimization experiences relate to psychopathic features in community youth, especially in girls. The findings underscore the need for comprehensive evaluation of victimization experiences when psychopathic features are present in youth. Copyright © 2016 Elsevier Ltd. All rights reserved.

The Relationship between Logistics Sophistication and Drivers of the Outsourcing of Logistics Activities

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Peter Wanke

2008-10-01

Full Text Available A strong link has been established between operational excellence and the degree of sophistication of logistics organization, a function of factors such as performance monitoring, investment in Information Technology [IT] and the formalization of logistics organization, as proposed in the Bowersox, Daugherty, Dröge, Germain and Rogers (1992 Leading Edge model. At the same time, shippers have been increasingly outsourcing their logistics activities to third party providers. This paper, based on a survey with large Brazilian shippers, addresses a gap in the literature by investigating the relationship between dimensions of logistics organization sophistication and drivers of logistics outsourcing. To this end, the dimensions behind the logistics sophistication construct were first investigated. Results from factor analysis led to the identification of six dimensions of logistics sophistication. By means of multivariate logistical regression analyses it was possible to relate some of these dimensions, such as the formalization of the logistics organization, to certain drivers of the outsourcing of logistics activities of Brazilian shippers, such as cost savings. These results indicate the possibility of segmenting shippers according to characteristics of their logistics organization, which may be particularly useful to logistics service providers.

mPLR-Loc: an adaptive decision multi-label classifier based on penalized logistic regression for protein subcellular localization prediction.

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Wan, Shibiao; Mak, Man-Wai; Kung, Sun-Yuan

2015-03-15

Proteins located in appropriate cellular compartments are of paramount importance to exert their biological functions. Prediction of protein subcellular localization by computational methods is required in the post-genomic era. Recent studies have been focusing on predicting not only single-location proteins but also multi-location proteins. However, most of the existing predictors are far from effective for tackling the challenges of multi-label proteins. This article proposes an efficient multi-label predictor, namely mPLR-Loc, based on penalized logistic regression and adaptive decisions for predicting both single- and multi-location proteins. Specifically, for each query protein, mPLR-Loc exploits the information from the Gene Ontology (GO) database by using its accession number (AC) or the ACs of its homologs obtained via BLAST. The frequencies of GO occurrences are used to construct feature vectors, which are then classified by an adaptive decision-based multi-label penalized logistic regression classifier. Experimental results based on two recent stringent benchmark datasets (virus and plant) show that mPLR-Loc remarkably outperforms existing state-of-the-art multi-label predictors. In addition to being able to rapidly and accurately predict subcellular localization of single- and multi-label proteins, mPLR-Loc can also provide probabilistic confidence scores for the prediction decisions. For readers' convenience, the mPLR-Loc server is available online (http://bioinfo.eie.polyu.edu.hk/mPLRLocServer). Copyright © 2014 Elsevier Inc. All rights reserved.

Logistic regression models for predicting physical and mental health-related quality of life in rheumatoid arthritis patients.

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Alishiri, Gholam Hossein; Bayat, Noushin; Fathi Ashtiani, Ali; Tavallaii, Seyed Abbas; Assari, Shervin; Moharamzad, Yashar

2008-01-01

The aim of this work was to develop two logistic regression models capable of predicting physical and mental health related quality of life (HRQOL) among rheumatoid arthritis (RA) patients. In this cross-sectional study which was conducted during 2006 in the outpatient rheumatology clinic of our university hospital, Short Form 36 (SF-36) was used for HRQOL measurements in 411 RA patients. A cutoff point to define poor versus good HRQOL was calculated using the first quartiles of SF-36 physical and mental component scores (33.4 and 36.8, respectively). Two distinct logistic regression models were used to derive predictive variables including demographic, clinical, and psychological factors. The sensitivity, specificity, and accuracy of each model were calculated. Poor physical HRQOL was positively associated with pain score, disease duration, monthly family income below 300 US$, comorbidity, patient global assessment of disease activity or PGA, and depression (odds ratios: 1.1; 1.004; 15.5; 1.1; 1.02; 2.08, respectively). The variables that entered into the poor mental HRQOL prediction model were monthly family income below 300 US$, comorbidity, PGA, and bodily pain (odds ratios: 6.7; 1.1; 1.01; 1.01, respectively). Optimal sensitivity and specificity were achieved at a cutoff point of 0.39 for the estimated probability of poor physical HRQOL and 0.18 for mental HRQOL. Sensitivity, specificity, and accuracy of the physical and mental models were 73.8, 87, 83.7% and 90.38, 70.36, 75.43%, respectively. The results show that the suggested models can be used to predict poor physical and mental HRQOL separately among RA patients using simple variables with acceptable accuracy. These models can be of use in the clinical decision-making of RA patients and to recognize patients with poor physical or mental HRQOL in advance, for better management.

Landslide susceptibility mapping for a part of North Anatolian Fault Zone (Northeast Turkey) using logistic regression model

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Demir, Gökhan; aytekin, mustafa; banu ikizler, sabriye; angın, zekai

2013-04-01

The North Anatolian Fault is know as one of the most active and destructive fault zone which produced many earthquakes with high magnitudes. Along this fault zone, the morphology and the lithological features are prone to landsliding. However, many earthquake induced landslides were recorded by several studies along this fault zone, and these landslides caused both injuiries and live losts. Therefore, a detailed landslide susceptibility assessment for this area is indispancable. In this context, a landslide susceptibility assessment for the 1445 km2 area in the Kelkit River valley a part of North Anatolian Fault zone (Eastern Black Sea region of Turkey) was intended with this study, and the results of this study are summarized here. For this purpose, geographical information system (GIS) and a bivariate statistical model were used. Initially, Landslide inventory maps are prepared by using landslide data determined by field surveys and landslide data taken from General Directorate of Mineral Research and Exploration. The landslide conditioning factors are considered to be lithology, slope gradient, slope aspect, topographical elevation, distance to streams, distance to roads and distance to faults, drainage density and fault density. ArcGIS package was used to manipulate and analyze all the collected data Logistic regression method was applied to create a landslide susceptibility map. Landslide susceptibility maps were divided into five susceptibility regions such as very low, low, moderate, high and very high. The result of the analysis was verified using the inventoried landslide locations and compared with the produced probability model. For this purpose, Area Under Curvature (AUC) approach was applied, and a AUC value was obtained. Based on this AUC value, the obtained landslide susceptibility map was concluded as satisfactory. Keywords: North Anatolian Fault Zone, Landslide susceptibility map, Geographical Information Systems, Logistic Regression Analysis.

Use of multilevel logistic regression to identify the causes of differential item functioning.

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Balluerka, Nekane; Gorostiaga, Arantxa; Gómez-Benito, Juana; Hidalgo, María Dolores

2010-11-01

Given that a key function of tests is to serve as evaluation instruments and for decision making in the fields of psychology and education, the possibility that some of their items may show differential behaviour is a major concern for psychometricians. In recent decades, important progress has been made as regards the efficacy of techniques designed to detect this differential item functioning (DIF). However, the findings are scant when it comes to explaining its causes. The present study addresses this problem from the perspective of multilevel analysis. Starting from a case study in the area of transcultural comparisons, multilevel logistic regression is used: 1) to identify the item characteristics associated with the presence of DIF; 2) to estimate the proportion of variation in the DIF coefficients that is explained by these characteristics; and 3) to evaluate alternative explanations of the DIF by comparing the explanatory power or fit of different sequential models. The comparison of these models confirmed one of the two alternatives (familiarity with the stimulus) and rejected the other (the topic area) as being a cause of differential functioning with respect to the compared groups.

Landslide Fissure Inference Assessment by ANFIS and Logistic Regression Using UAS-Based Photogrammetry

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Ozgun Akcay

2015-10-01

Full Text Available Unmanned Aerial Systems (UAS are now capable of gathering high-resolution data, therefore, landslides can be explored in detail at larger scales. In this research, 132 aerial photographs were captured, and 85,456 features were detected and matched automatically using UAS photogrammetry. The root mean square (RMS values of the image coordinates of the Ground Control Points (GPCs varied from 0.521 to 2.293 pixels, whereas maximum RMS values of automatically matched features was calculated as 2.921 pixels. Using the 3D point cloud, which was acquired by aerial photogrammetry, the raster datasets of the aspect, slope, and maximally stable extremal regions (MSER detecting visual uniformity, were defined as three variables, in order to reason fissure structures on the landslide surface. In this research, an Adaptive Neuro Fuzzy Inference System (ANFIS and a Logistic Regression (LR were implemented using training datasets to infer fissure data appropriately. The accuracy of the predictive models was evaluated by drawing receiver operating characteristic (ROC curves and by calculating the area under the ROC curve (AUC. The experiments exposed that high-resolution imagery is an indispensable data source to model and validate landslide fissures appropriately.

Characterization of breast masses by dynamic enhanced MR imaging. A logistic regression analysis

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Ikeda, O.; Morishita, S.; Kido, T.; Kitajima, M.; Yamashita, Y.; Takahashi, M.; Okamura, K.; Fukuda, S.

1999-01-01

Purpose: To identify features useful for differentiation between malignant and benign breast neoplasms using multivariate analysis of findings by MR imaging. Material and Methods: In a retrospective analysis, 61 patients with 64 breast masses underwent MR imaging and the time-signal intensity curves for precontrast dynamic postcontrast images were quantitatively analyzed. Statistical analysis was performed using a logistic regression model, which was prospectively tested in another 34 patients with suspected breast masses. Results: Univariate analysis revealed that the reliable indicators for malignancy were first the appearance of the tumor border, followed by the washout ratio, internal architecture after contrast enhancement, and peak time. The factors significantly associated with malignancy were irregular tumor border, followed by washout ratio, internal architecture, and peak time. For differentiation between benignity and malignancy, the maximum cut-off point was to be found between 0.47 and 0.51. In a prospective application of this model, 91% of the lesions were accurately discriminated as benign or malignant lesions. Conclusion: Combination of contrast-enhanced dynamic and postcontrast-enhanced MR imaging provided accurate data for the diagnosis of malignant neoplasms of the breast. The model had an accuracy of 91% (sensitivity 90%, specificity 93%). (orig.)

The comparison of landslide ratio-based and general logistic regression landslide susceptibility models in the Chishan watershed after 2009 Typhoon Morakot

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WU, Chunhung

2015-04-01

The research built the original logistic regression landslide susceptibility model (abbreviated as or-LRLSM) and landslide ratio-based ogistic regression landslide susceptibility model (abbreviated as lr-LRLSM), compared the performance and explained the error source of two models. The research assumes that the performance of the logistic regression model can be better if the distribution of landslide ratio and weighted value of each variable is similar. Landslide ratio is the ratio of landslide area to total area in the specific area and an useful index to evaluate the seriousness of landslide disaster in Taiwan. The research adopted the landside inventory induced by 2009 Typhoon Morakot in the Chishan watershed, which was the most serious disaster event in the last decade, in Taiwan. The research adopted the 20 m grid as the basic unit in building the LRLSM, and six variables, including elevation, slope, aspect, geological formation, accumulated rainfall, and bank erosion, were included in the two models. The six variables were divided as continuous variables, including elevation, slope, and accumulated rainfall, and categorical variables, including aspect, geological formation and bank erosion in building the or-LRLSM, while all variables, which were classified based on landslide ratio, were categorical variables in building the lr-LRLSM. Because the count of whole basic unit in the Chishan watershed was too much to calculate by using commercial software, the research took random sampling instead of the whole basic units. The research adopted equal proportions of landslide unit and not landslide unit in logistic regression analysis. The research took 10 times random sampling and selected the group with the best Cox & Snell R2 value and Nagelkerker R2 value as the database for the following analysis. Based on the best result from 10 random sampling groups, the or-LRLSM (lr-LRLSM) is significant at the 1% level with Cox & Snell R2 = 0.190 (0.196) and Nagelkerke R2

Testing a model of research intention among U.K. clinical psychologists: a logistic regression analysis.

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Eke, Gemma; Holttum, Sue; Hayward, Mark

2012-03-01

Previous research highlights barriers to clinical psychologists conducting research, but has rarely examined U.K. clinical psychologists. The study investigated U.K. clinical psychologists' self-reported research output and tested part of a theoretical model of factors influencing their intention to conduct research. Questionnaires were mailed to 1,300 U.K. clinical psychologists. Three hundred and seventy-four questionnaires were returned (29% response-rate). This study replicated in a U.K. sample the finding that the modal number of publications was zero, highlighted in a number of U.K. and U.S. studies. Research intention was bimodally distributed, and logistic regression classified 78% of cases successfully. Outcome expectations, perceived behavioral control and normative beliefs mediated between research training environment and intention. Further research should explore how research is negotiated in clinical roles, and this issue should be incorporated into prequalification training. © 2012 Wiley Periodicals, Inc.

Mixture model-based clustering and logistic regression for automatic detection of microaneurysms in retinal images

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Sánchez, Clara I.; Hornero, Roberto; Mayo, Agustín; García, María

2009-02-01

Diabetic Retinopathy is one of the leading causes of blindness and vision defects in developed countries. An early detection and diagnosis is crucial to avoid visual complication. Microaneurysms are the first ocular signs of the presence of this ocular disease. Their detection is of paramount importance for the development of a computer-aided diagnosis technique which permits a prompt diagnosis of the disease. However, the detection of microaneurysms in retinal images is a difficult task due to the wide variability that these images usually present in screening programs. We propose a statistical approach based on mixture model-based clustering and logistic regression which is robust to the changes in the appearance of retinal fundus images. The method is evaluated on the public database proposed by the Retinal Online Challenge in order to obtain an objective performance measure and to allow a comparative study with other proposed algorithms.

Use of geographically weighted logistic regression to quantify spatial variation in the environmental and sociodemographic drivers of leptospirosis in Fiji: a modelling study.

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Mayfield, Helen J; Lowry, John H; Watson, Conall H; Kama, Mike; Nilles, Eric J; Lau, Colleen L

2018-05-01

Leptospirosis is a globally important zoonotic disease, with complex exposure pathways that depend on interactions between human beings, animals, and the environment. Major drivers of outbreaks include flooding, urbanisation, poverty, and agricultural intensification. The intensity of these drivers and their relative importance vary between geographical areas; however, non-spatial regression methods are incapable of capturing the spatial variations. This study aimed to explore the use of geographically weighted logistic regression (GWLR) to provide insights into the ecoepidemiology of human leptospirosis in Fiji. We obtained field data from a cross-sectional community survey done in 2013 in the three main islands of Fiji. A blood sample obtained from each participant (aged 1-90 years) was tested for anti-Leptospira antibodies and household locations were recorded using GPS receivers. We used GWLR to quantify the spatial variation in the relative importance of five environmental and sociodemographic covariates (cattle density, distance to river, poverty rate, residential setting [urban or rural], and maximum rainfall in the wettest month) on leptospirosis transmission in Fiji. We developed two models, one using GWLR and one with standard logistic regression; for each model, the dependent variable was the presence or absence of anti-Leptospira antibodies. GWLR results were compared with results obtained with standard logistic regression, and used to produce a predictive risk map and maps showing the spatial variation in odds ratios (OR) for each covariate. The dataset contained location information for 2046 participants from 1922 households representing 81 communities. The Aikaike information criterion value of the GWLR model was 1935·2 compared with 1254·2 for the standard logistic regression model, indicating that the GWLR model was more efficient. Both models produced similar OR for the covariates, but GWLR also detected spatial variation in the effect of each

Patterns, Characteristics, and Correlates of Adolescent Bully-Victims in Urban Tanzania

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Benjamin A. Kamala

2013-10-01

Full Text Available Bullying is an understudied issue of public health importance in low-income countries. In the present study, we aimed to explore social and demographic factors associated with bullying among adolescents in a low-income country urban setting. We divided a sample of 2,154 school-attending adolescents into two groups, those who had been bullied during a 30-day period and those who were not. We considered age, sex, mental health, parent-relationship, hunger and social deprivation and truancy in our comparison of these two groups using logistic regression. Multinomial regression was also used to determine if there was a dose response relationship between bullying frequency and the aforementioned selected variables. We found that school-attending adolescents in Dar es Salaam were more likely to be truant, suffer from mental health problems and have experienced hunger. Adolescents who had parents which were more aware of their free time activities, were less likely to report being bullied. There were also significant differences in bullying frequency and certain variables, most notably with truancy, economic and social deprivation, and signs of depression. School settings in Dar es Salaam offer a potential for intervening in what are potentially harmful effects of bullying behavior among bully victims.

Effects of perpetrator identity on suicidality and nonsuicidal self-injury in sexually victimized female adolescents

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Unlu G

2016-06-01

Full Text Available Gulsen Unlu, Burcu Cakaloz Department of Child and Adolescent Psychiatry, Faculty of Medicine, Pamukkale University, Denizli, Turkey Purpose: Child sexual abuse and sexual dating violence victimization are common problems that are known to have long-term negative consequences. This study aimed to compare the sociodemographic, abuse-related, and clinical features of female adolescents who were sexually abused by different perpetrators, and identify the factors associated with suicidality and nonsuicidal self-injury (NSSI in these cases. Patients and methods: Data of 254 sexually abused female adolescents between the ages of 12–18 years were evaluated. The cases were classified into three groups, namely “sexual dating violence”, “incest”, and “other child sexual abuse”, according to the identity of the perpetrator. The three groups were compared in terms of sociodemographic, abuse-related, and clinical features. Results: Major depressive disorder was the most common psychiatric diagnosis, which was present in 44.9% of the cases. Among all victims, 25.6% had attempted suicide, 52.0% had suicidal ideation, and 23.6% had NSSI during the postabuse period. A logistic regression analysis revealed that attempted suicide was predicted by dating violence victimization (adjusted odds ratio [AOR] =3.053; 95% confidence interval [CI] =1.473, 6.330 and depression (AOR =2.238; 95% CI =1.226, 4.086. Dating violence victimization was also the strongest predictor of subsequent suicidal ideation (AOR =3.500; 95% CI =1.817, 6.741. In addition, revictimization was determined to be an important risk factor for both suicidal ideation (AOR =2.897; 95% CI =1.276, 6.574 and NSSI (AOR =3.847; 95% CI =1.899, 7.794. Conclusion: Perpetrator identity and revictimization are associated with negative mental health outcomes in sexually victimized female adolescents. Increased risk of suicidality and NSSI should be borne in mind while assessing cases with dating

Using logistic regression to improve the prognostic value of microarray gene expression data sets: application to early-stage squamous cell carcinoma of the lung and triple negative breast carcinoma.

Science.gov (United States)

Mount, David W; Putnam, Charles W; Centouri, Sara M; Manziello, Ann M; Pandey, Ritu; Garland, Linda L; Martinez, Jesse D

2014-06-10

Numerous microarray-based prognostic gene expression signatures of primary neoplasms have been published but often with little concurrence between studies, thus limiting their clinical utility. We describe a methodology using logistic regression, which circumvents limitations of conventional Kaplan Meier analysis. We applied this approach to a thrice-analyzed and published squamous cell carcinoma (SQCC) of the lung data set, with the objective of identifying gene expressions predictive of early death versus long survival in early-stage disease. A similar analysis was applied to a data set of triple negative breast carcinoma cases, which present similar clinical challenges. Important to our approach is the selection of homogenous patient groups for comparison. In the lung study, we selected two groups (including only stages I and II), equal in size, of earliest deaths and longest survivors. Genes varying at least four-fold were tested by logistic regression for accuracy of prediction (area under a ROC plot). The gene list was refined by applying two sliding-window analyses and by validations using a leave-one-out approach and model building with validation subsets. In the breast study, a similar logistic regression analysis was used after selecting appropriate cases for comparison. A total of 8594 variable genes were tested for accuracy in predicting earliest deaths versus longest survivors in SQCC. After applying the two sliding window and the leave-one-out analyses, 24 prognostic genes were identified; most of them were B-cell related. When the same data set of stage I and II cases was analyzed using a conventional Kaplan Meier (KM) approach, we identified fewer immune-related genes among the most statistically significant hits; when stage III cases were included, most of the prognostic genes were missed. Interestingly, logistic regression analysis of the breast cancer data set identified many immune-related genes predictive of clinical outcome. Stratification of