Applied Logistic Regression

David W. Hosmer , Jr. 지음

Wiley 2013년 02월 26일 출간

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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 dataA wealth of additional material for topics ranging from Bayesian methods to assessing model fitRich data sets from real-world studies that demonstrate each method under discussionDetailed 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.

목차

Preface to the Third Edition xiii1 Introduction to the Logistic Regression Model 11.1Introduction, 11.2 Fitting the Logistic Regression Model, 81.3 Testing for the Significance of the Coefficients, 101.4 Confidence Interval Estimation, 151.5 Other Estimation Methods, 201.6 Data Sets Used in Examples and Exercises, 221.6.1 The ICU Study, 221.6.2 The Low Birth Weight Study, 241.6.3 The Global Longitudinal Study of Osteoporosis in Women, 241.6.4 The Adolescent Placement Study, 261.6.5 The Burn Injury Study, 271.6.6 The Myopia Study, 291.6.7 The NHANES Study, 311.6.8 The Polypharmacy Study, 31Exercises, 322 The Multiple Logistic Regression Model 352.1 Introduction, 352.2 The Multiple Logistic Regression Model, 352.3 Fitting the Multiple Logistic Regression Model, 372.4 Testing for the Significance of the Model, 392.5 Confidence Interval Estimation, 422.6 Other Estimation Methods, 45Exercises, 463 Interpretation of the Fitted Logistic Regression Model 493.1 Introduction, 493.2 Dichotomous Independent Variable, 503.3 Polychotomous Independent Variable, 563.4 Continuous Independent Variable, 623.5 Multivariable Models, 643.6 Presentation and Interpretation of the Fitted Values, 773.7 A Comparison of Logistic Regression and Stratified Analysis for 2 × 2 Tables, 82Exercises, 874 Model-Building Strategies and Methods for Logistic Regression 894.1 Introduction, 894.2 Purposeful Selection of Covariates, 894.2.1 Methods to Examine the Scale of a Continuous Covariate in the Logit, 944.2.2 Examples of Purposeful Selection, 1074.3 Other Methods for Selecting Covariates, 1244.3.1 Stepwise Selection of Covariates, 1254.3.2 Best Subsets Logistic Regression, 1334.3.3

출판사 서평

“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)

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저자(글) David W. Hosmer

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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.

저자(글) Jr.