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Paris Champerret
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This book provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. It emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The text provides a state-of-the-art techniques for building, interpreting, and assessing the performance of LR models.
TABLE OF CONTENTS -- 1 Introduction to the Logistic Regression Model 1 -- 2 The Multiple Logistic Regression Model 35 -- 3 Interpretation of the Fitted Logistic Regression Model 49 -- 4 Model-Building Strategies and Methods for Logistic Regression 89 -- 5 Assessing the Fit of the Model 153 -- 6 Application of Logistic Regression with Different Sampling Models 227--7 Logistic Regression for Matched Case-Control Studies 243 -- 8 Logistic Regression Models for Multinomial and Ordinal Outcomes 269 -- 9 Logistic Regression Models for the Analysis of Correlated Data 313 -- 10 Special Topics 377.
