Maura E. Stokes is Senior Manager of Statistical Applications Research and Development at SAS Institute. She received
her DrPH in Biostatistics from the University of North Carolina at Chapel Hill and has taught and written about
categorical data analysis for over fifteen years.
Davis, Charles S. : University of Iowa
Charles S. Davis is Professor of Biostatistics at the University of Iowa. He received his PhD in Biostatistics
from the University of Michigan. His research and teaching interests include categorical data analysis and methods
for the analysis of repeated measures.
Koch, Gary G. : University of North Carolina at Chapel Hill
Gary G. Koch is Professor of Biostatistics and Director of the Biometrics Consulting Laboratory at the University
of North Carolina at Chapel Hill. He has had a prominent role in the field of categorical data analysis for the
last thirty years. He teaches classes and s eminars in categorical data analysis, consults in areas of statistical
practice, conducts research, and trains many Biostatistics students.
Summary
Statisticians and researchers will find this book a useful discussion of categorical data analysis techniques
as well as an invaluable aid in applying these methods with the SAS System. Practical examples from a broad range
of applications illustrate the use of the FREQ, LOGISTIC, GENMOD, and CATMOD procedures
in a variety of analyses. Other procedures discussed include the PHREG and NPAR1WAY procedures. Topics discussed
include assessing association in contingency tables and sets of tables, logistic regression and conditional logistic
regression, weighted least squares modeling, repeated measurements analyses, loglinear models, and bioassay analysis.
The second edition has been revised for use with Version 8 of
the SAS System. New topics include additional exact tests, generalized estimating equations, use of the CLASS statement
in the LOGISTIC procedure, exact logistic regression using the LOGISTIC procedure, and comparisons of the use of
subject-specific models versus population-averaged models.
Table of Contents
Chapter 1: Introduction
Overview
Scale of Measurement
Sampling Frameworks
Overview of Analysis Strategies
Working with Tables in the SAS System
Using This Book
Chapter 2: The 2 x 2 Table
Introduction
Chi-Square Statistics
Exact Tests
Difference in Proportions
Odds Ratio and Relative Risk
Sensitivity and Specificity
McNemar's Test
Chapter 3: Sets of 2 x 2 Tables
Introduction
Mantel-Haenszel Test
Measures of Association
Chapter 4: Sets of 2 x r and s x 2 Tables
Introduction
Sets of 2 x r Tables
Sets of s x 2 Tables
Relationships Between Sets of Tables
Chapter 5: The s x r Tables
Introduction
Association
Exact Tests for Association
Measures of Association
Observer Agreement
Test for Ordered Differences
Chapter 6: Sets of s x r Tables
Introduction
General Mantel-Haenszel Methodology
Mantel-Haenszel Applications
Advanced Topic: Application to Repeated Measures
Chapter 7: Nonparametric Methods
Introduction
Wilcoxon-Mann-Whitney Test
Kruskal-Wallis Test
Friedman's Chi-Square Test
Aligned Ranks Test for Randomized Complete Blocks
Durbin's Test for Balanced Incomplete Blocks
Rank Analysis of Covariance
Introduction
Dichotomous Explanatory Variables
Using the CLASS Statement
Qualitative Explanatory Variables
Continuous and Ordinal Explanatory Variables
A Note on Diagnostics
Maximum Likelihood Estimation Problems and Alternatives
Exact Methods in Logistic Regression
Using the CATMOD and GENMOD Procedures for Logistic Regression
Appendix A: Statistical Methodology for Dichotomous Logistic Regression
Introduction
Ordinal Response: Proportional Odds Model
Nominal Response: Generalized Logits Model
Chapter 10: Conditional Logistic Regression
Introduction
Paired Observations from a Highly Stratified Cohort Study
Clinical Trials Study Analysis
Crossover Design Studies
General Conditional Logistic Regression
Paired Observations in a Retrospective Matched Study
l:m Conditional Logistic Regression
Exact Conditional Logistic Regression in the Stratified Setting
Appendix A: Theory for the Case-Control Retrospective Setting
Appendix B: Theory for Exact Conditional Inference
Appendix C: ODS Macros
Chapter 11: Quantal Bioassay Analysis
Introduction
Estimating Tolerance Distributions
Comparing Two Drugs
Analysis of Pain Study
Chapter 12: Poisson Regression
Introduction
Methodology for Poisson Regression
Simple Poisson Counts Example
Poisson Regression for Incidence Densities
Overdispersion in Lower Respiratoy Infection Example
Chapter 13: Weighted Least Squares
Introduction
Weighted Least Squares Methodology
Using PROC CATMOD for Weighted Least Squares Analysis
Analysis of Means: Performing Contrast Tests
Analysis of Proportions: Occupational Data
Obstetrical Pain Data: Advanced Modeling of Means
Analysis of Survey Sample Data
Modeling Rank Measures of Association Statistics
Appendix A: Statistical Methodology for Weighted Least Squares
Chapter 14: Modeling Repeated Measurements Data with WLS
Introduction
Weighted Least Squares
Advanced Topic: Further Weighted Least Square Applications
Chapter 15: Generalized Estimating Equations
Introduction
Methodology
Summary of the GEE Methodology
Passive Smoking Example
Crossover Example
Respiratory Data
Using a Modified Wald Statistic to Assess Model Effects
Diagnostic Data
Using GEE for Count Data
Fitting the Proportional Odds Model
GEE Analysis for Data with Missing Values
Alternating Logistic Regression
Using GEE to Fit a Partial Proportional Odds Model: Univariate Outcome
Using GEE to Account for Overdispersion: Univariate Outcome
Appendix A: Steps to Find the GEE Solution
Appendix B: Macros for Adjusted Wald Statistic
Chapter 16: Loglinear Models
Introduction
Two-Way Contingency Tables
Three-Way Contingency Tables
Higher-Order Contingency Tables
Correspondence Between Logistic Models and Loglinear Models
Appendix A: Equivalence of the Loglinear and Poisson Regression Models
Chapter 17: Categorized Time-to-Event Data
Introduction
Life Table Estimation of Survival Rates
Mantel-Cox Test
Piecewise Exponential Models
