Logistic regression for potential modeling

Sample size determination for logistic regression

The problem of sample size estimation is important in medical applications, especially in cases of expensive measurements of immune biomarkers. This paper describes the problem of logistic regression analysis with the sample size determination algorithms, namely the methods of univariate statistics, logistics regression, cross-validation and Bayesian inference. The authors, treating the regr...

Comparison of ordinary logistic regression and robust logistic regression models in modeling of pre-diabetes risk factors

Background: Regarding the increased risk of developing type 2 diabetes in pre-diabetic people, identifying pre-diabetes and determining of its risk factors seems so necessary. In this study, it is aimed to compare ordinary logistic regression and robust logistic regression models in modeling pre-diabetes risk factors. Methods: This is a cross-sectional study and conducted on 6460 people, over ...

Log - Linear Modeling and Logistic Regression

Categorical Data Modeling: Logistic Regression Software

A Matlab based software for logistic regression is developed to enhance the process of teaching quantitative topics and assist researchers with analyzing wide area of applications where categorical data is involved. The software offers an option of performing stepwise logistic regression to select the most significant predictors. The software includes a feature to detect influential observation...

Hierarchical Logistic Regression Modeling with SAS GLIMMIX

Data often have hierarchical or clustered structures, such as patients clustered within hospitals or students nested within schools. Hierarchical models are statistical models that are used to analyze hierarchical or multilevel data. SAS GLIMMIX procedure is a new and highly useful tool for hierarchical modeling with discrete responses. This paper is focused on hierarchical logistic regression ...