Applied Logistic Regression

Informationen zum Autor 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. Klappentext A new edition of the definitive guide to logistic regression modeling for health science and other applicationsThis 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 throughoutApplied 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. "In conclusion, the index was mercifully complete, and all items searched for were found (nice cross-referencing too) In summary: Highly recommended." ( Scientific Computing , 1 May 2013) Zusammenfassung This new edition provides a focused introduction to the LR model and its use in methods for modeling the relationship between a dichotomous outcome variable and a set of covariables. Inhaltsverzeichnis Preface to the Third Edition xiii 1 Introduction to the Logistic Regression Model 1 1.1 Introduction 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...

List of contents

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
 

Report

"In conclusion, the index was mercifully complete, and all items searched for were found (nice cross-referencing too) In summary: Highly recommended." (Scientific Computing, 1 May 2013)

Product details

• Authors: Hosmer, David W Hosmer, David W. Hosmer, David W. (University of Massachusetts Hosmer, David W. Lemeshow Hosmer, Lemeshow, Stanley Lemeshow, Stanley (The Ohio State University (OSU) Lemeshow, Sturdivant, R Sturdivant, Rodney X Sturdivant, Rodney X. Sturdivant, Rodney X. (Center for Data Analysis and Statistics at the United States Military Academy in West Point Sturdivant
• Publisher: Blackwell Scientific Publishers Ltd
• Languages: English
• Product format: Hardback
• Released: 26.04.2013
• EAN: 9780470582473
• ISBN: 978-0-470-58247-3
• Dimensions: 162 mm x 240 mm x 33 mm
• Series: Wiley Series in Probability and Statistics; Wiley Series in Probability &; Wiley Series in Probability and Statistics
• Subject: Natural sciences, medicine, IT, technology > Mathematics > Probability theory, stochastic theory, mathematical statistics