Book description
A new edition of the definitive guide to logistic regression modeling for health science and other applications
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 stateoftheart 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 realworld 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 musthave 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.
Table of contents
 Cover
 Series
 Title Page
 Copyright
 Dedication
 Preface to the Third Edition
 Chapter 1: Introduction to the Logistic Regression Model
 Chapter 2: The Multiple Logistic Regression Model

Chapter 3: Interpretation of the Fitted Logistic Regression Model
 3.1 Introduction
 3.2 Dichotomous Independent Variable
 3.3 Polychotomous Independent Variable
 3.4 Continuous Independent Variable
 3.5 Multivariable Models
 3.6 Presentation and Interpretation of the Fitted Values
 3.7 A Comparison of Logistic Regression and Stratified Analysis for 2 × 2 Tables
 Exercises
 Chapter 4: ModelBuilding Strategies and Methods for Logistic Regression
 Chapter 5: Assessing the Fit of the Model
 Chapter 6: Application of Logistic Regression with Different Sampling Models
 Chapter 7: Logistic Regression for Matched CaseControl Studies
 Chapter 8: Logistic Regression Models for Multinomial and Ordinal Outcomes

Chapter 9: Logistic Regression Models for the Analysis of Correlated Data
 9.1 Introduction
 9.2 Logistic Regression Models for the Analysis of Correlated Data
 9.3 Estimation Methods for Correlated Data Logistic Regression Models
 9.4 Interpretation of Coefficients From Logistic Regression Models for the Analysis of Correlated Data
 9.5 An Example of Logistic Regression Modeling with Correlated Data
 9.6 Assessment of Model Fit
 Exercises

Chapter 10: Special Topics
 10.1 Introduction
 10.2 Application of Propensity Score Methods in Logistic Regression Modeling
 10.3 Exact Methods for Logistic Regression Models
 10.4 Missing Data
 10.5 Sample Size Issues When Fitting Logistic Regression Models
 10.6 Bayesian Methods for Logistic Regression
 10.7 Other Link Functions for Binary Regression Models
 10.8 Mediation ‡
 10.9 More About Statistical Interaction
 Exercises
 References
 Index
Product information
 Title: Applied Logistic Regression, 3rd Edition
 Author(s):
 Release date: April 2013
 Publisher(s): Wiley
 ISBN: 9780470582473
You might also like
book
Introduction to Linear Regression Analysis, 5th Edition
Praise for the Fourth Edition "As with previous editions, the authors have produced a leading textbook …
book
Introduction to Probability
Developed from celebrated Harvard statistics lectures, Introduction to Probability provides essential language and tools for understanding …
book
HandsOn Machine Learning with ScikitLearn, Keras, and TensorFlow, 2nd Edition
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. …
book
Data Science from Scratch, 2nd Edition
To really learn data science, you should not only master the tools—data science libraries, frameworks, modules, …