Applied Logistic Regression

ISBN-10: 0470582472

ISBN-13: 9780470582473

Edition: 3rd 2013

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Description: 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. It presents expanded coverage on random effects models, estimation in the presence of interaction, and fractional polynomials; offers discussions on Bayesian logistic regression, likelihood based confidence interval estimates, tests for non-nested models, and multivariable fractional polynomials; includes R language and updated SAS, STATA, and BUGS computer code for analyzing data sets; and more.

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Book details

List price: $115.00
Edition: 3rd
Copyright year: 2013
Publisher: John Wiley & Sons, Limited
Publication date: 4/26/2013
Binding: Hardcover
Pages: 528
Size: 6.50" wide x 9.50" long x 1.50" tall
Weight: 2.024
Language: English

Preface to the Third Edition
Introduction to the Logistic Regression Model
Introduction
Fitting the Logistic Regression Model
Testing for the Significance of the Coefficients
Confidence Interval Estimation
Other Estimation Methods
Data Sets Used in Examples and Exercises
The ICU Study
The Low Birth Weight Study
The Global Longitudinal Study of Osteoporosis in Women
The Adolescent Placement Study
The Burn Injury Study
The Myopia Study
The NHANES Study
The Polypharmacy Study
Exercises
The Multiple Logistic Regression Model
Introduction
The Multiple Logistic Regression Model
Fitting the Multiple Logistic Regression Model
Testing for the Significance of the Model
Confidence Interval Estimation
Other Estimation Methods
Exercises
Interpretation of the Fitted Logistic Regression Model
Introduction
Dichotomous Independent Variable
Polychotomous Independent Variable
Continuous Independent Variable
Multivariable Models
Presentation and Interpretation of the Fitted Values
A Comparison of Logistic Regression and Stratified Analysis for 2 � 2 Tables
Exercises
Model-Building Strategies and Methods for Logistic Regression
Introduction
Purposeful Selection of Covariates
Methods to Examine the Scale of a Continuous Covariate in the Logit
Examples of Purposeful Selection
Other Methods for Selecting Covariates
Stepwise Selection of Covariates
Best Subsets Logistic Regression
Selecting Covariates and Checking their Scale Using Multivariable Fractional Polynomials
Numerical Problems
Exercises
Assessing the Fit of the Model
Introduction
Summary Measures of Goodness of Fit
Pearson Chi-Square Statistic, Deviance, and Sum-of-Squares
The Hosmer-Lemeshow Tests
Classification Tables
Area Under the Receiver Operating Characteristic Curve
Other Summary Measures
Logistic Regression Diagnostics
Assessment of Fit via External Validation
Interpretation and Presentation of the Results from a Fitted Logistic Regression Model
Exercises
Application of Logistic Regression with Different Sampling Models
Introduction
Cohort Studies
Case-Control Studies
Fitting Logistic Regression Models to Data from Complex Sample Surveys
Exercises
Logistic Regression for Matched Case-Control Studies
Introduction
Methods For Assessment of Fit in a 1-M Matched Study
An Example Using the Logistic Regression Model in a 1-1 Matched Study
An Example Using the Logistic Regression Model in a l-M Matched Study
Exercises
Logistic Regression Models for Multinomial and Ordinal Outcomes
The Multinomial Logistic Regression Model
Introduction to the Model and Estimation of Model Parameters
Interpreting and Assessing the Significance of the Estimated Coefficients
Model-Building Strategies for Multinomial Logistic Regression
Assessment of Fit and Diagnostic Statistics for the Multinomial Logistic Regression Model
Ordinal Logistic Regression Models
Introduction to the Models, Methods for Fitting, and Interpretation of Model Parameters
Model Building Strategies for Ordinal Logistic Regression Models
Exercises
Logistic Regression Models for the Analysis of Correlated Data
Introduction
Logistic Regression Models for the Analysis of Correlated Data
Estimation Methods for Correlated Data Logistic Regression Models
Interpretation of Coefficients from Logistic Regression Models for the Analysis of Correlated Data
Population Average Model
Cluster-Specific Model
Alternative Estimation Methods for the Cluster-Specific Model
Comparison of Population Average and Cluster-Specific Model
An Example of Logistic Regression Modeling with Correlated Data
Choice of Model for Correlated Data Analysis
Population Average Model
Cluster-Specific Model
Additional Points to Consider when Fitting Logistic Regression Models to Correlated Data
Assessment of Model Fit
Assessment of Population Average Model Fit
Assessment of Cluster-Specific Model Fit
Conclusions
Exercises
Special Topics
Introduction
Application of Propensity Score Methods in Logistic Regression Modeling
Exact Methods for Logistic Regression Models
Missing Data
Sample Size Issues when Fitting Logistic Regression Models
Bayesian Methods for Logistic Regression
The Bayesian Logistic Regression Model
MCMC Simulation
An Example of a Bayesian Analysis and Its Interpretation
Other Link Functions for Binary Regression Models
Mediation
Distinguishing Mediators from Confounders
Implications for the Interpretation of an Adjusted Logistic Regression Coefficient
Why Adjust for a Mediator?
Using Logistic Regression to Assess Mediation: Assumptions
More About Statistical Interaction
Additive versus Multiplicative Scale-Risk Difference versus Odds Ratios
Estimating and Testing Additive Interaction
Exercises
References
Index
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