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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…mehr
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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.
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.
Produktdetails
- Produktdetails
- Wiley Series in Probability and Statistics
- Verlag: Wiley & Sons
- 3. Aufl.
- Seitenzahl: 528
- Erscheinungstermin: 1. April 2013
- Englisch
- Abmessung: 240mm x 161mm x 33mm
- Gewicht: 856g
- ISBN-13: 9780470582473
- ISBN-10: 0470582472
- Artikelnr.: 36951170
- Wiley Series in Probability and Statistics
- Verlag: Wiley & Sons
- 3. Aufl.
- Seitenzahl: 528
- Erscheinungstermin: 1. April 2013
- Englisch
- Abmessung: 240mm x 161mm x 33mm
- Gewicht: 856g
- ISBN-13: 9780470582473
- ISBN-10: 0470582472
- Artikelnr.: 36951170
DAVID W. HOSMER, Jr., PhD, is Professor Emeritus of Biostatistics at the School of Public Health and Health Sciences at the University of Massachusetts Amherst. STANLEY LEMESHOW, PhD, is Professor of Biostatistics and Founding Dean of the College of Public Health at The Ohio State University, Columbus, Ohio. RODNEY X. STURDIVANT, PhD, is Associate Professor and Founding Director of the Center for Data Analysis and Statistics at the United States Military Academy at West Point, New York.
Preface to the Third Edition xiii 1 Introduction to the Logistic Regression Model 1 1.1Introduction
1 1.2 Fitting the Logistic Regression Model
8 1.3 Testing for the Significance of the Coefficients
10 1.4 Confidence Interval Estimation
15 1.5 Other Estimation Methods
20 1.6 Data Sets Used in Examples and Exercises
22 1.6.1 The ICU Study
22 1.6.2 The Low Birth Weight Study
24 1.6.3 The Global Longitudinal Study of Osteoporosis in Women
24 1.6.4 The Adolescent Placement Study
26 1.6.5 The Burn Injury Study
27 1.6.6 The Myopia Study
29 1.6.7 The NHANES Study
31 1.6.8 The Polypharmacy Study
31 Exercises
32 2 The Multiple Logistic Regression Model 35 2.1 Introduction
35 2.2 The Multiple Logistic Regression Model
35 2.3 Fitting the Multiple Logistic Regression Model
37 2.4 Testing for the Significance of the Model
39 2.5 Confidence Interval Estimation
42 2.6 Other Estimation Methods
45 Exercises
46 3 Interpretation of the Fitted Logistic Regression Model 49 3.1 Introduction
49 3.2 Dichotomous Independent Variable
50 3.3 Polychotomous Independent Variable
56 3.4 Continuous Independent Variable
62 3.5 Multivariable Models
64 3.6 Presentation and Interpretation of the Fitted Values
77 3.7 A Comparison of Logistic Regression and Stratified Analysis for 2 × 2 Tables
82 Exercises
87 4 Model-Building Strategies and Methods for Logistic Regression 89 4.1 Introduction
89 4.2 Purposeful Selection of Covariates
89 4.2.1 Methods to Examine the Scale of a Continuous Covariate in the Logit
94 4.2.2 Examples of Purposeful Selection
107 4.3 Other Methods for Selecting Covariates
124 4.3.1 Stepwise Selection of Covariates
125 4.3.2 Best Subsets Logistic Regression
133 4.3.3 Selecting Covariates and Checking their Scale Using Multivariable Fractional Polynomials
139 4.4 Numerical Problems
145 Exercises
150 5 Assessing the Fit of the Model 153 5.1 Introduction
153 5.2 Summary Measures of Goodness of Fit
154 5.2.1 Pearson Chi-Square Statistic
Deviance
and Sum-of-Squares
155 5.2.2 The Hosmer-Lemeshow Tests
157 5.2.3 Classification Tables
169 5.2.4 Area Under the Receiver Operating Characteristic Curve
173 5.2.5 Other Summary Measures
182 5.3 Logistic Regression Diagnostics
186 5.4 Assessment of Fit via External Validation
202 5.5 Interpretation and Presentation of the Results from a Fitted Logistic Regression Model
212 Exercises
223 6 Application of Logistic Regression with Different Sampling Models 227 6.1 Introduction
227 6.2 Cohort Studies
227 6.3 Case-Control Studies
229 6.4 Fitting Logistic Regression Models to Data from Complex Sample Surveys
233 Exercises
242 7 Logistic Regression for Matched Case-Control Studies 243 7.1 Introduction
243 7.2 Methods For Assessment of Fit in a 1-M Matched Study
248 7.3 An Example Using the Logistic Regression Model in a 1-1 Matched Study
251 7.4 An Example Using the Logistic Regression Model in a 1-M Matched Study
260 Exercises
267 8 Logistic Regression Models for Multinomial and Ordinal Outcomes 269 8.1 The Multinomial Logistic Regression Model
269 8.1.1 Introduction to the Model and Estimation of Model Parameters
269 8.1.2 Interpreting and Assessing the Significance of the Estimated Coefficients
272 8.1.3 Model-Building Strategies for Multinomial Logistic Regression
278 8.1.4 Assessment of Fit and Diagnostic Statistics for the Multinomial Logistic Regression Model
283 8.2 Ordinal Logistic Regression Models
289 8.2.1 Introduction to the Models
Methods for Fitting
and Interpretation of Model Parameters
289 8.2.2 Model Building Strategies for Ordinal Logistic Regression Models
305 Exercises
310 9 Logistic Regression Models for the Analysis of Correlated Data 313 9.1 Introduction
313 9.2 Logistic Regression Models for the Analysis of Correlated Data
315 9.3 Estimation Methods for Correlated Data Logistic Regression Models
318 9.4 Interpretation of Coefficients from Logistic Regression Models for the Analysis of Correlated Data
323 9.4.1 Population Average Model
324 9.4.2 Cluster-Specific Model
326 9.4.3 Alternative Estimation Methods for the Cluster-Specific Model
333 9.4.4 Comparison of Population Average and Cluster-Specific Model
334 9.5 An Example of Logistic Regression Modeling with Correlated Data
337 9.5.1 Choice of Model for Correlated Data Analysis
338 9.5.2 Population Average Model
339 9.5.3 Cluster-Specific Model
344 9.5.4 Additional Points to Consider when Fitting Logistic Regression Models to Correlated Data
351 9.6 Assessment of Model Fit
354 9.6.1 Assessment of Population Average Model Fit
354 9.6.2 Assessment of Cluster-Specific Model Fit
365 9.6.3 Conclusions
374 Exercises
375 10 Special Topics 377 10.1 Introduction
377 10.2 Application of Propensity Score Methods in Logistic Regression Modeling
377 10.3 Exact Methods for Logistic Regression Models
387 10.4 Missing Data
395 10.5 Sample Size Issues when Fitting Logistic Regression Models
401 10.6 Bayesian Methods for Logistic Regression
408 10.6.1 The Bayesian Logistic Regression Model
410 10.6.2 MCMC Simulation
411 10.6.3 An Example of a Bayesian Analysis and Its Interpretation
419 10.7 Other Link Functions for Binary Regression Models
434 10.8 Mediation
441 10.8.1 Distinguishing Mediators from Confounders
441 10.8.2 Implications for the Interpretation of an Adjusted Logistic Regression Coefficient
443 10.8.3 Why Adjust for a Mediator? 444 10.8.4 Using Logistic Regression to Assess Mediation: Assumptions
445 10.9 More About Statistical Interaction
448 10.9.1 Additive versus Multiplicative Scale-Risk Difference versus Odds Ratios
448 10.9.2 Estimating and Testing Additive Interaction
451 Exercises
456 References 459 Index 479
1 1.2 Fitting the Logistic Regression Model
8 1.3 Testing for the Significance of the Coefficients
10 1.4 Confidence Interval Estimation
15 1.5 Other Estimation Methods
20 1.6 Data Sets Used in Examples and Exercises
22 1.6.1 The ICU Study
22 1.6.2 The Low Birth Weight Study
24 1.6.3 The Global Longitudinal Study of Osteoporosis in Women
24 1.6.4 The Adolescent Placement Study
26 1.6.5 The Burn Injury Study
27 1.6.6 The Myopia Study
29 1.6.7 The NHANES Study
31 1.6.8 The Polypharmacy Study
31 Exercises
32 2 The Multiple Logistic Regression Model 35 2.1 Introduction
35 2.2 The Multiple Logistic Regression Model
35 2.3 Fitting the Multiple Logistic Regression Model
37 2.4 Testing for the Significance of the Model
39 2.5 Confidence Interval Estimation
42 2.6 Other Estimation Methods
45 Exercises
46 3 Interpretation of the Fitted Logistic Regression Model 49 3.1 Introduction
49 3.2 Dichotomous Independent Variable
50 3.3 Polychotomous Independent Variable
56 3.4 Continuous Independent Variable
62 3.5 Multivariable Models
64 3.6 Presentation and Interpretation of the Fitted Values
77 3.7 A Comparison of Logistic Regression and Stratified Analysis for 2 × 2 Tables
82 Exercises
87 4 Model-Building Strategies and Methods for Logistic Regression 89 4.1 Introduction
89 4.2 Purposeful Selection of Covariates
89 4.2.1 Methods to Examine the Scale of a Continuous Covariate in the Logit
94 4.2.2 Examples of Purposeful Selection
107 4.3 Other Methods for Selecting Covariates
124 4.3.1 Stepwise Selection of Covariates
125 4.3.2 Best Subsets Logistic Regression
133 4.3.3 Selecting Covariates and Checking their Scale Using Multivariable Fractional Polynomials
139 4.4 Numerical Problems
145 Exercises
150 5 Assessing the Fit of the Model 153 5.1 Introduction
153 5.2 Summary Measures of Goodness of Fit
154 5.2.1 Pearson Chi-Square Statistic
Deviance
and Sum-of-Squares
155 5.2.2 The Hosmer-Lemeshow Tests
157 5.2.3 Classification Tables
169 5.2.4 Area Under the Receiver Operating Characteristic Curve
173 5.2.5 Other Summary Measures
182 5.3 Logistic Regression Diagnostics
186 5.4 Assessment of Fit via External Validation
202 5.5 Interpretation and Presentation of the Results from a Fitted Logistic Regression Model
212 Exercises
223 6 Application of Logistic Regression with Different Sampling Models 227 6.1 Introduction
227 6.2 Cohort Studies
227 6.3 Case-Control Studies
229 6.4 Fitting Logistic Regression Models to Data from Complex Sample Surveys
233 Exercises
242 7 Logistic Regression for Matched Case-Control Studies 243 7.1 Introduction
243 7.2 Methods For Assessment of Fit in a 1-M Matched Study
248 7.3 An Example Using the Logistic Regression Model in a 1-1 Matched Study
251 7.4 An Example Using the Logistic Regression Model in a 1-M Matched Study
260 Exercises
267 8 Logistic Regression Models for Multinomial and Ordinal Outcomes 269 8.1 The Multinomial Logistic Regression Model
269 8.1.1 Introduction to the Model and Estimation of Model Parameters
269 8.1.2 Interpreting and Assessing the Significance of the Estimated Coefficients
272 8.1.3 Model-Building Strategies for Multinomial Logistic Regression
278 8.1.4 Assessment of Fit and Diagnostic Statistics for the Multinomial Logistic Regression Model
283 8.2 Ordinal Logistic Regression Models
289 8.2.1 Introduction to the Models
Methods for Fitting
and Interpretation of Model Parameters
289 8.2.2 Model Building Strategies for Ordinal Logistic Regression Models
305 Exercises
310 9 Logistic Regression Models for the Analysis of Correlated Data 313 9.1 Introduction
313 9.2 Logistic Regression Models for the Analysis of Correlated Data
315 9.3 Estimation Methods for Correlated Data Logistic Regression Models
318 9.4 Interpretation of Coefficients from Logistic Regression Models for the Analysis of Correlated Data
323 9.4.1 Population Average Model
324 9.4.2 Cluster-Specific Model
326 9.4.3 Alternative Estimation Methods for the Cluster-Specific Model
333 9.4.4 Comparison of Population Average and Cluster-Specific Model
334 9.5 An Example of Logistic Regression Modeling with Correlated Data
337 9.5.1 Choice of Model for Correlated Data Analysis
338 9.5.2 Population Average Model
339 9.5.3 Cluster-Specific Model
344 9.5.4 Additional Points to Consider when Fitting Logistic Regression Models to Correlated Data
351 9.6 Assessment of Model Fit
354 9.6.1 Assessment of Population Average Model Fit
354 9.6.2 Assessment of Cluster-Specific Model Fit
365 9.6.3 Conclusions
374 Exercises
375 10 Special Topics 377 10.1 Introduction
377 10.2 Application of Propensity Score Methods in Logistic Regression Modeling
377 10.3 Exact Methods for Logistic Regression Models
387 10.4 Missing Data
395 10.5 Sample Size Issues when Fitting Logistic Regression Models
401 10.6 Bayesian Methods for Logistic Regression
408 10.6.1 The Bayesian Logistic Regression Model
410 10.6.2 MCMC Simulation
411 10.6.3 An Example of a Bayesian Analysis and Its Interpretation
419 10.7 Other Link Functions for Binary Regression Models
434 10.8 Mediation
441 10.8.1 Distinguishing Mediators from Confounders
441 10.8.2 Implications for the Interpretation of an Adjusted Logistic Regression Coefficient
443 10.8.3 Why Adjust for a Mediator? 444 10.8.4 Using Logistic Regression to Assess Mediation: Assumptions
445 10.9 More About Statistical Interaction
448 10.9.1 Additive versus Multiplicative Scale-Risk Difference versus Odds Ratios
448 10.9.2 Estimating and Testing Additive Interaction
451 Exercises
456 References 459 Index 479
Preface to the Third Edition xiii 1 Introduction to the Logistic Regression Model 1 1.1Introduction
1 1.2 Fitting the Logistic Regression Model
8 1.3 Testing for the Significance of the Coefficients
10 1.4 Confidence Interval Estimation
15 1.5 Other Estimation Methods
20 1.6 Data Sets Used in Examples and Exercises
22 1.6.1 The ICU Study
22 1.6.2 The Low Birth Weight Study
24 1.6.3 The Global Longitudinal Study of Osteoporosis in Women
24 1.6.4 The Adolescent Placement Study
26 1.6.5 The Burn Injury Study
27 1.6.6 The Myopia Study
29 1.6.7 The NHANES Study
31 1.6.8 The Polypharmacy Study
31 Exercises
32 2 The Multiple Logistic Regression Model 35 2.1 Introduction
35 2.2 The Multiple Logistic Regression Model
35 2.3 Fitting the Multiple Logistic Regression Model
37 2.4 Testing for the Significance of the Model
39 2.5 Confidence Interval Estimation
42 2.6 Other Estimation Methods
45 Exercises
46 3 Interpretation of the Fitted Logistic Regression Model 49 3.1 Introduction
49 3.2 Dichotomous Independent Variable
50 3.3 Polychotomous Independent Variable
56 3.4 Continuous Independent Variable
62 3.5 Multivariable Models
64 3.6 Presentation and Interpretation of the Fitted Values
77 3.7 A Comparison of Logistic Regression and Stratified Analysis for 2 × 2 Tables
82 Exercises
87 4 Model-Building Strategies and Methods for Logistic Regression 89 4.1 Introduction
89 4.2 Purposeful Selection of Covariates
89 4.2.1 Methods to Examine the Scale of a Continuous Covariate in the Logit
94 4.2.2 Examples of Purposeful Selection
107 4.3 Other Methods for Selecting Covariates
124 4.3.1 Stepwise Selection of Covariates
125 4.3.2 Best Subsets Logistic Regression
133 4.3.3 Selecting Covariates and Checking their Scale Using Multivariable Fractional Polynomials
139 4.4 Numerical Problems
145 Exercises
150 5 Assessing the Fit of the Model 153 5.1 Introduction
153 5.2 Summary Measures of Goodness of Fit
154 5.2.1 Pearson Chi-Square Statistic
Deviance
and Sum-of-Squares
155 5.2.2 The Hosmer-Lemeshow Tests
157 5.2.3 Classification Tables
169 5.2.4 Area Under the Receiver Operating Characteristic Curve
173 5.2.5 Other Summary Measures
182 5.3 Logistic Regression Diagnostics
186 5.4 Assessment of Fit via External Validation
202 5.5 Interpretation and Presentation of the Results from a Fitted Logistic Regression Model
212 Exercises
223 6 Application of Logistic Regression with Different Sampling Models 227 6.1 Introduction
227 6.2 Cohort Studies
227 6.3 Case-Control Studies
229 6.4 Fitting Logistic Regression Models to Data from Complex Sample Surveys
233 Exercises
242 7 Logistic Regression for Matched Case-Control Studies 243 7.1 Introduction
243 7.2 Methods For Assessment of Fit in a 1-M Matched Study
248 7.3 An Example Using the Logistic Regression Model in a 1-1 Matched Study
251 7.4 An Example Using the Logistic Regression Model in a 1-M Matched Study
260 Exercises
267 8 Logistic Regression Models for Multinomial and Ordinal Outcomes 269 8.1 The Multinomial Logistic Regression Model
269 8.1.1 Introduction to the Model and Estimation of Model Parameters
269 8.1.2 Interpreting and Assessing the Significance of the Estimated Coefficients
272 8.1.3 Model-Building Strategies for Multinomial Logistic Regression
278 8.1.4 Assessment of Fit and Diagnostic Statistics for the Multinomial Logistic Regression Model
283 8.2 Ordinal Logistic Regression Models
289 8.2.1 Introduction to the Models
Methods for Fitting
and Interpretation of Model Parameters
289 8.2.2 Model Building Strategies for Ordinal Logistic Regression Models
305 Exercises
310 9 Logistic Regression Models for the Analysis of Correlated Data 313 9.1 Introduction
313 9.2 Logistic Regression Models for the Analysis of Correlated Data
315 9.3 Estimation Methods for Correlated Data Logistic Regression Models
318 9.4 Interpretation of Coefficients from Logistic Regression Models for the Analysis of Correlated Data
323 9.4.1 Population Average Model
324 9.4.2 Cluster-Specific Model
326 9.4.3 Alternative Estimation Methods for the Cluster-Specific Model
333 9.4.4 Comparison of Population Average and Cluster-Specific Model
334 9.5 An Example of Logistic Regression Modeling with Correlated Data
337 9.5.1 Choice of Model for Correlated Data Analysis
338 9.5.2 Population Average Model
339 9.5.3 Cluster-Specific Model
344 9.5.4 Additional Points to Consider when Fitting Logistic Regression Models to Correlated Data
351 9.6 Assessment of Model Fit
354 9.6.1 Assessment of Population Average Model Fit
354 9.6.2 Assessment of Cluster-Specific Model Fit
365 9.6.3 Conclusions
374 Exercises
375 10 Special Topics 377 10.1 Introduction
377 10.2 Application of Propensity Score Methods in Logistic Regression Modeling
377 10.3 Exact Methods for Logistic Regression Models
387 10.4 Missing Data
395 10.5 Sample Size Issues when Fitting Logistic Regression Models
401 10.6 Bayesian Methods for Logistic Regression
408 10.6.1 The Bayesian Logistic Regression Model
410 10.6.2 MCMC Simulation
411 10.6.3 An Example of a Bayesian Analysis and Its Interpretation
419 10.7 Other Link Functions for Binary Regression Models
434 10.8 Mediation
441 10.8.1 Distinguishing Mediators from Confounders
441 10.8.2 Implications for the Interpretation of an Adjusted Logistic Regression Coefficient
443 10.8.3 Why Adjust for a Mediator? 444 10.8.4 Using Logistic Regression to Assess Mediation: Assumptions
445 10.9 More About Statistical Interaction
448 10.9.1 Additive versus Multiplicative Scale-Risk Difference versus Odds Ratios
448 10.9.2 Estimating and Testing Additive Interaction
451 Exercises
456 References 459 Index 479
1 1.2 Fitting the Logistic Regression Model
8 1.3 Testing for the Significance of the Coefficients
10 1.4 Confidence Interval Estimation
15 1.5 Other Estimation Methods
20 1.6 Data Sets Used in Examples and Exercises
22 1.6.1 The ICU Study
22 1.6.2 The Low Birth Weight Study
24 1.6.3 The Global Longitudinal Study of Osteoporosis in Women
24 1.6.4 The Adolescent Placement Study
26 1.6.5 The Burn Injury Study
27 1.6.6 The Myopia Study
29 1.6.7 The NHANES Study
31 1.6.8 The Polypharmacy Study
31 Exercises
32 2 The Multiple Logistic Regression Model 35 2.1 Introduction
35 2.2 The Multiple Logistic Regression Model
35 2.3 Fitting the Multiple Logistic Regression Model
37 2.4 Testing for the Significance of the Model
39 2.5 Confidence Interval Estimation
42 2.6 Other Estimation Methods
45 Exercises
46 3 Interpretation of the Fitted Logistic Regression Model 49 3.1 Introduction
49 3.2 Dichotomous Independent Variable
50 3.3 Polychotomous Independent Variable
56 3.4 Continuous Independent Variable
62 3.5 Multivariable Models
64 3.6 Presentation and Interpretation of the Fitted Values
77 3.7 A Comparison of Logistic Regression and Stratified Analysis for 2 × 2 Tables
82 Exercises
87 4 Model-Building Strategies and Methods for Logistic Regression 89 4.1 Introduction
89 4.2 Purposeful Selection of Covariates
89 4.2.1 Methods to Examine the Scale of a Continuous Covariate in the Logit
94 4.2.2 Examples of Purposeful Selection
107 4.3 Other Methods for Selecting Covariates
124 4.3.1 Stepwise Selection of Covariates
125 4.3.2 Best Subsets Logistic Regression
133 4.3.3 Selecting Covariates and Checking their Scale Using Multivariable Fractional Polynomials
139 4.4 Numerical Problems
145 Exercises
150 5 Assessing the Fit of the Model 153 5.1 Introduction
153 5.2 Summary Measures of Goodness of Fit
154 5.2.1 Pearson Chi-Square Statistic
Deviance
and Sum-of-Squares
155 5.2.2 The Hosmer-Lemeshow Tests
157 5.2.3 Classification Tables
169 5.2.4 Area Under the Receiver Operating Characteristic Curve
173 5.2.5 Other Summary Measures
182 5.3 Logistic Regression Diagnostics
186 5.4 Assessment of Fit via External Validation
202 5.5 Interpretation and Presentation of the Results from a Fitted Logistic Regression Model
212 Exercises
223 6 Application of Logistic Regression with Different Sampling Models 227 6.1 Introduction
227 6.2 Cohort Studies
227 6.3 Case-Control Studies
229 6.4 Fitting Logistic Regression Models to Data from Complex Sample Surveys
233 Exercises
242 7 Logistic Regression for Matched Case-Control Studies 243 7.1 Introduction
243 7.2 Methods For Assessment of Fit in a 1-M Matched Study
248 7.3 An Example Using the Logistic Regression Model in a 1-1 Matched Study
251 7.4 An Example Using the Logistic Regression Model in a 1-M Matched Study
260 Exercises
267 8 Logistic Regression Models for Multinomial and Ordinal Outcomes 269 8.1 The Multinomial Logistic Regression Model
269 8.1.1 Introduction to the Model and Estimation of Model Parameters
269 8.1.2 Interpreting and Assessing the Significance of the Estimated Coefficients
272 8.1.3 Model-Building Strategies for Multinomial Logistic Regression
278 8.1.4 Assessment of Fit and Diagnostic Statistics for the Multinomial Logistic Regression Model
283 8.2 Ordinal Logistic Regression Models
289 8.2.1 Introduction to the Models
Methods for Fitting
and Interpretation of Model Parameters
289 8.2.2 Model Building Strategies for Ordinal Logistic Regression Models
305 Exercises
310 9 Logistic Regression Models for the Analysis of Correlated Data 313 9.1 Introduction
313 9.2 Logistic Regression Models for the Analysis of Correlated Data
315 9.3 Estimation Methods for Correlated Data Logistic Regression Models
318 9.4 Interpretation of Coefficients from Logistic Regression Models for the Analysis of Correlated Data
323 9.4.1 Population Average Model
324 9.4.2 Cluster-Specific Model
326 9.4.3 Alternative Estimation Methods for the Cluster-Specific Model
333 9.4.4 Comparison of Population Average and Cluster-Specific Model
334 9.5 An Example of Logistic Regression Modeling with Correlated Data
337 9.5.1 Choice of Model for Correlated Data Analysis
338 9.5.2 Population Average Model
339 9.5.3 Cluster-Specific Model
344 9.5.4 Additional Points to Consider when Fitting Logistic Regression Models to Correlated Data
351 9.6 Assessment of Model Fit
354 9.6.1 Assessment of Population Average Model Fit
354 9.6.2 Assessment of Cluster-Specific Model Fit
365 9.6.3 Conclusions
374 Exercises
375 10 Special Topics 377 10.1 Introduction
377 10.2 Application of Propensity Score Methods in Logistic Regression Modeling
377 10.3 Exact Methods for Logistic Regression Models
387 10.4 Missing Data
395 10.5 Sample Size Issues when Fitting Logistic Regression Models
401 10.6 Bayesian Methods for Logistic Regression
408 10.6.1 The Bayesian Logistic Regression Model
410 10.6.2 MCMC Simulation
411 10.6.3 An Example of a Bayesian Analysis and Its Interpretation
419 10.7 Other Link Functions for Binary Regression Models
434 10.8 Mediation
441 10.8.1 Distinguishing Mediators from Confounders
441 10.8.2 Implications for the Interpretation of an Adjusted Logistic Regression Coefficient
443 10.8.3 Why Adjust for a Mediator? 444 10.8.4 Using Logistic Regression to Assess Mediation: Assumptions
445 10.9 More About Statistical Interaction
448 10.9.1 Additive versus Multiplicative Scale-Risk Difference versus Odds Ratios
448 10.9.2 Estimating and Testing Additive Interaction
451 Exercises
456 References 459 Index 479