Applied Logistic Regression, Second Edition
Authors:
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David W. Hosmer, Jr. and Stanley Lemeshow |
| Publisher: |
Wiley |
| Copyright: |
2000 |
| ISBN-13: |
978-0-471-35632-5 |
| Pages: |
373; hardcover |
| Price: |
$114.00 |
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Comment from the Stata technical group
The second edition of Applied Logistic Regression, by David W. Hosmer
and Stanley Lemeshow, provides an excellent updated reference to the
advances in methodology in logistic regression that have taken place over
the last 10 years.
While the first edition has served as one of the few comprehensive
treatments of logistic regression available, the second edition introduces
many enhancements in the areas of assessing model fit, estimation using data
from complex survey samples, regression models for multinomial data, ordinal
data, and data with correlated responses. Also, the text now covers exact
tests and sample size calculations.
Many of the analyses in the book were performed in Stata and can be
replicated in Stata with data from the text. In particular, Stata’s
svy: logit command can be used to fit logistic regression models
with survey data. To download the data in Stata format, click
here.
Table of contents
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 Methods of Estimation
1.6 Data Sets
1.6.1 The ICU Study
1.6.2 The Low Birth Weight Study
1.6.3 The Prostate Cancer Study
1.6.4 The UMARU IMPACT Study
Exercises
2. Multiple Logistic Regression
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 Methods of Estimation
Exercises
3. Interpretation of the Fitted Logistic Regression Model
3.1 Introduction
3.2 Dichotomous Independent Variable
3.3 Polytomous Independent Variable
3.4 Continuous Independent Variable
3.5 The Multivariate Model
3.6 Interaction and Confounding
3.7 Estimation of Odds Ratios in the Presence of Interaction
3.8 A Comparison of Logistic Regression and Stratified Analysis for 2×2 Tables
Exercises
4. Model-Building Strategies and Methods for Logistic Regression
4.1 Introduction
4.2 Variable Selection
4.3 Stepwise Logistic Regression
4.4 Best Subsets Logistic Regression
4.5 Numerical Problems
Exercises
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 and Deviance
5.2.2 The Hosmer–Lemeshow Tests
5.2.3 Classification Tables
5.2.4 Area Under the ROC 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 Results from a Fitted Logistic Regression Model
Exercises
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
Exercises
7. Logistic Regression for Matched Case-Control Studies
7.1 Introduction
7.2 Logistic Regression Analysis for the 1-1 Matched Study
7.3 An Example of the Use of the Logistic Regression Model in a 1-1 Matched Study
7.4 Assessment of Fit in a 1-1 Matched Study
7.5 An Example of the Use of the Logistic Regression Model in a 1-M Matched Study
7.6 Methods for Assessment of Fit in a 1-M Matched Study
7.7 An Example of Assessment of Fit in a 1-M Matched Study
Exercises
8. Special Topics
8.1 The Multinomial Logistic Regression Model
8.1.1 Introduction to the Model and Estimation of the 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 Diagnostics 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 Models for the Analysis of Correlated Data
8.3 Logistic Regression Models for the Analysis of Correlated Data
8.4 Exact Methods for Logistic Regression Models
8.5 Sample Size Issues When Fitting Logistic Regression Models
Exercises
References
Index
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