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Applied Logistic Regression, Third Edition

David W. Hosmer, Jr., Stanley Lemeshow, and Rodney X. Sturdivant
Publisher: Wiley
Copyright: 2013
ISBN-13: 978-0-470-58247-3
Pages: 528; hardcover
Price: $92.50

Comment from the Stata technical group

The third edition of Applied Logistic Regression, by David W. Hosmer, Jr., Stanley Lemeshow, and Rodney X. Sturdivant, is the definitive reference on logistic regression models.

The book begins with chapters on fitting and interpreting binary logistic models as well as chapters on assessing model fit and selecting the appropriate covariates and transformations. Despite the modest title, however, the book goes much further. One chapter discusses different sampling schemes, including case–control data, cohort studies, and complex survey data; another chapter is dedicated to matched case–control studies. Later chapters discuss multinomial and ordinal logistic models for multiple-outcome responses and the analysis of correlated longitudinal data using population-average and cluster-specific models. The final chapter broaches advanced topics, including the use of logistic regression in propensity score methods, exact methods, Bayesian estimation, and mediation.

Most of the analyses in the book were performed using Stata and can be replicated using Stata and the data from the text. Also noteworthy is the book's use of multinomial fractional polynomial models that can be fit using Stata's mfp command.

Table of contents

Preface to the Third Edition
1 Introduction to the Logistic Regression Model
1.1 Introduction
1.2 Fitting the Logistic Regression Model
1.3 Testing for the Significance of the Coefficients
1.4 Confidence Interval Estimation
1.5 Other Estimation Methods
1.6 Data Sets Used in Examples and Exercises
1.6.1 The ICU Study
1.6.2 The Low Birth Weight Study
1.6.3 The Global Longitudinal Study of Osteoporosis in Women
1.6.4 The Adolescent Placement Study
1.6.5 The Burn Injury Study
1.6.6 The Myopia Study
1.6.7 The NHANES Study
1.6.8 The Polypharmacy Study
2 The Multiple Logistic Regression Model
2.1 Introduction
2.2 The Multiple Logistic Regression Model
2.3 Fitting the Multiple Logistic Regression Model
2.4 Testing for the Significance of the Model
2.5 Confidence Interval Estimation
2.6 Other Estimation Methods
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
4 Model-Building Strategies and Methods for Logistic Regression
4.1 Introduction
4.2 Purposeful Selection of Covariates
4.2.1 Methods to Examine the Scale of a Continuous Covariate in the Logit
4.2.2 Examples of Purposeful Selection
4.3 Other Methods for Selecting Covariates
4.3.1 Stepwise Selection of Covariates
4.3.2 Best Subsets Logistic Regression
4.3.3 Selecting Covariates and Checking their Scale Using Multivariable Fractional Polynomials
4.4 Numerical Problems
5 Assessing the Fit of the Model
5.1 Introduction
5.2 Summary Measures of Goodness of Fit
5.2.1 Pearson Chi-Square Statistic, Deviance, and Sum-of-Squares
5.2.2 The Hosmer–Lemeshow Tests
5.2.3 Classification Tables
5.2.4 Area Under the Receiver Operating Characteristic Curve
5.2.5 Other Summary Measures
5.3 Logistic Regression Diagnostics
5.4 Assessment of Fit via External Validation
5.5 Interpretation and Presentation of the Results from a Fitted Logistic Regression Model
6 Application of Logistic Regression with Different Sampling Models
6.1 Introduction
6.2 Cohort Studies
6.3 Case-Control Studies
6.4 Fitting Logistic Regression Models to Data from Complex Sample Surveys
7 Logistic Regression for Matched Case-Control Studies
7.1 Introduction
7.2 Methods For Assessment of Fit in a 1–M Matched Study
7.3 An Example Using the Logistic Regression Model in a 1–1 Matched Study
7.4 An Example Using the Logistic Regression Model in a 1–M Matched Study
8 Logistic Regression Models for Multinomial and Ordinal Outcomes
8.1 The Multinomial Logistic Regression Model
8.1.1 Introduction to the Model and Estimation of Model Parameters
8.1.2 Interpreting and Assessing the Significance of the Estimated Coefficients
8.1.3 Model-Building Strategies for Multinomial Logistic Regression
8.1.4 Assessment of Fit and Diagnostic Statistics for the Multinomial Logistic Regression Model
8.2 Ordinal Logistic Regression Models
8.2.1 Introduction to the Models, Methods for Fitting, and Interpretation of Model Parameters
8.2.2 Model Building Strategies for Ordinal Logistic Regression Models
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
9.4.1 Population Average Model
9.4.2 Cluster-Specific Model
9.4.3 Alternative Estimation Methods for the Cluster-Specific Model
9.4.4 Comparison of Population Average and Cluster-Specific Model
9.5 An Example of Logistic Regression Modeling with Correlated Data
9.5.1 Choice of Model for Correlated Data Analysis
9.5.2 Population Average Model
9.5.3 Cluster-Specific Model
9.5.4 Additional Points to Consider when Fitting Logistic Regression Models to Correlated Data
9.6 Assessment of Model Fit
9.6.1 Assessment of Population Average Model Fit
9.6.2 Assessment of Cluster-Specific Model Fit
9.6.3 Conclusions
10 Special Topics
10.1 Introduction
10.2 Application of Propensity Score Methods in Logistics 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
10.6.1 The Bayesian Logistic Regression Model
10.6.2 MCMC Simulation
10.6.3 An Example of a Bayesian Analysis and Its Interpretation
10.7 Other Link Functions for Binary Regression Models
10.8 Mediation
10.8.1 Distinguishing Mediators from Confounders
10.8.2 Implications for the Interpretation of an Adjusted Logistic Regression Coefficient
10.8.3 Why Adjust for a Mediator
10.8.4 Using Logistic Regression to Assess Mediation: Assumptions
10.9 More About Statistical Interaction
10.9.1 Additive versus Multiplicative Scale–Risk Difference versus Odds Ratios
10.9.2 Estimating and Testing Additive Interaction
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