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Applied Logistic Regression [electronic resource]

Hosmer, David W; Lemeshow, Stanley; Sturdivant, Rodney X
Format
EBook; Book; Online
Published
Chicester : Wiley, 2013.
Edition
3rd ed
Language
English
Related Title
Virtual Library of Virginia EBL DDA purchased title
Series
Wiley Series in Probability and Statistics
Wiley Series in Probability and Statistics
ISBN
9781118548356, 1118548353
Summary
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-
Contents
  • Cover; Series; Title Page; Copyright; Dedication; Preface to the Third Edition; Chapter 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; Exercises; Chapter 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 Estimation2.6 Other Estimation Methods; Exercises; Chapter 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; Exercises; Chapter 4: Model-Building Strategies and Methods for Logistic Regression; 4.1 Introduction; 4.2 Purposeful Selection of Covariates.
  • 4.3 Other Methods for Selecting Covariates4.4 Numerical Problems; Exercises; Chapter 5: Assessing the Fit of the Model; 5.1 Introduction; 5.2 Summary Measures of Goodness of Fit; 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; Exercises; Chapter 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.
  • Chapter 7: Logistic Regression for Matched Case-Control Studies7.1 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 Matched Study; 7.4 An Example Using the Logistic Regression Model in a Matched Study; Exercises; Chapter 8: Logistic Regression Models for Multinomial and Ordinal Outcomes; 8.1 The Multinomial Logistic Regression Model; 8.2 Ordinal Logistic Regression Models; Exercises; Chapter 9: Logistic Regression Models for the Analysis of Correlated Data; 9.1 Introduction.
  • 9.2 Logistic Regression Models for the Analysis of Correlated Data9.3 Estimation Methods for Correlated Data Logistic Regression Models; 9.4 Interpretation of Coefficients From Logistic Regression Models for the Analysis of Correlated Data; 9.5 An Example of Logistic Regression Modeling with Correlated Data; 9.6 Assessment of Model Fit; Exercises; Chapter 10: Special Topics; 10.1 Introduction; 10.2 Application of Propensity Score Methods in Logistic Regression Modeling; 10.3 Exact Methods for Logistic Regression Models; 10.4 Missing Data.
Description
1 online resource (767 pages).
Notes
10.5 Sample Size Issues When Fitting Logistic Regression Models.
Copyright Not EvaluatedCopyright Not Evaluated
Technical Details
  • Staff View

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    a| 9.2 Logistic Regression Models for the Analysis of Correlated Data9.3 Estimation Methods for Correlated Data Logistic Regression Models; 9.4 Interpretation of Coefficients From Logistic Regression Models for the Analysis of Correlated Data; 9.5 An Example of Logistic Regression Modeling with Correlated Data; 9.6 Assessment of Model Fit; Exercises; Chapter 10: Special Topics; 10.1 Introduction; 10.2 Application of Propensity Score Methods in Logistic Regression Modeling; 10.3 Exact Methods for Logistic Regression Models; 10.4 Missing Data.
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