Applied Logistic Regression

This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables.

Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-the-art techniques for building, interpreting, and assessing the performance of LR models. New and updated features include:

• A chapter on the analysis of correlated outcome data
• A wealth of additional material for topics ranging from Bayesian methods to assessing model fit
• Rich data sets from real-world studies that demonstrate each method under discussion
• Detailed examples and interpretation of the presented results as well as exercises throughout

Applied Logistic Regression, Third Edition is a must-have guide for professionals and researchers who need to model nominal or ordinal scaled outcome variables in public health, medicine, and the social sciences as well as a wide range of other fields and disciplines.

• An Introduction to Categorical Data Analysis
• Categorical Data Analysis
• Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis (Springer Series in Statistics)
• Applied Survival Analysis: Regression Modeling of Time-to-Event Data
• Logistic Regression: A Self-Learning Text (Statistics for Biology and Health)
• Regression Models for Categorical Dependent Variables Using Stata, Third Edition
• Applied Predictive Modeling
• Applied Regression Analysis and Other Multivariable Methods
• An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
• Modern Epidemiology