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 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.
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: Model-Building 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 Case-Control 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
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