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Categorical Data Analysis Using SAS, Third Edition, 3rd Edition

Book Description

Statisticians and researchers will find Categorical Data Analysis Using SAS, Third Edition, by Maura Stokes, Charles Davis, and Gary Koch, to be a useful discussion of categorical data analysis techniques as well as an invaluable aid in applying these methods with SAS. Practical examples from a broad range of applications illustrate the use of the FREQ, LOGISTIC, GENMOD, NPAR1WAY, and CATMOD procedures in a variety of analyses. 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, generalized estimating equations, and bioassay analysis.

The third edition updates the use of SAS/STAT software to SAS/STAT 12.1 and incorporates ODS Graphics. Many additional SAS statements and options are employed, and graphs such as effect plots, odds ratio plots, regression diagnostic plots, and agreement plots are discussed. The material has also been revised and reorganized to reflect the evolution of categorical data analysis strategies. Additional techniques include such topics as exact Poisson regression, partial proportional odds models, Newcombe confidence intervals, incidence density ratios, and so on.

Table of Contents

  1. Cover Page
  2. Title Page
  3. Copyright Page
  4. Contents
  5. Preface to the Third Edition
  6. Chapter 1. Introduction
    1. 1.1 Overview
    2. 1.2 Scale of Measurement
    3. 1.3 Sampling Frameworks
    4. 1.4 Overview of Analysis Strategies
    5. 1.5 Working with Tables in SAS Software
    6. 1.6 Using This Book
  7. Chapter 2. The 2 × 2 Table
    1. 2.1 Introduction
    2. 2.2 Chi-Square Statistics
    3. 2.3 Exact Tests
    4. 2.4 Difference in Proportions
    5. 2.5 Odds Ratio and Relative Risk
    6. 2.6 Sensitivity and Specificity
    7. 2.7 McNemar's Test
    8. 2.8 Incidence Densities
    9. 2.9 Sample Size and Power Computations
  8. Chapter 3. Sets of 2 × 2 Tables
    1. 3.1 Introduction
    2. 3.2 Mantel-Haenszel Test
    3. 3.3 Measures of Association
    4. 3.4 Exact Confidence Intervals for the Common Odds Ratio
  9. Chapter 4. 2 × r and s × 2 Tables
    1. 4.1 Introduction
    2. 4.2 The 2 × r Table
    3. 4.3 Sets of 2 × r Tables
    4. 4.4 The s × 2 Table
    5. 4.5 Sets of s × 2 Tables
    6. 4.6 Relationships between Sets of Tables
    7. 4.7 Exact Analysis of Association for the s × 2 Table
  10. Chapter 5. The s × r Table
    1. 5.1 Introduction
    2. 5.2 Association
    3. 5.3 Exact Tests for Association
    4. 5.4 Measures of Association
    5. 5.5 Observer Agreement
    6. 5.6 Test for Ordered Differences
  11. Chapter 6. Sets of s × r Tables
    1. 6.1 Introduction
    2. 6.2 General Mantel-Haenszel Methodology
    3. 6.3 Mantel-Haenszel Applications
    4. 6.4 Advanced Topic: Application to Repeated Measures
  12. Chapter 7. Nonparametric Methods
    1. 7.1 Introduction
    2. 7.2 Kruskal-Wallis Test
    3. 7.3 Friedman's Chi-Square Test
    4. 7.4 Aligned Ranks Test for Randomized Complete Blocks
    5. 7.5 Analyzing Incomplete Data
    6. 7.6 Rank Analysis of Covariance
  13. Chapter 8. Logistic Regression I: Dichotomous Response
    1. 8.1 Introduction
    2. 8.2 Dichotomous Explanatory Variable
    3. 8.3 Using the CLASS Statement
    4. 8.4 Qualitative Explanatory Variables
    5. 8.5 Continuous and Ordinal Explanatory Variables
    6. 8.6 A Note on Diagnostics
    7. 8.7 Alternatives to Maximum Likelihood Estimation
    8. 8.8 Using the GENMOD Procedure for Logistic Regression
    9. Appendix A: Statistical Methodology for Dichotomous Logistic Regression
  14. Chapter 9. Logistic Regression II: Polytomous Response
    1. 9.1 Introduction
    2. 9.2 Ordinal Response: Proportional Odds Model
    3. 9.3 Nominal Response: Generalized Logits Model
  15. Chapter 10. Conditional Logistic Regression
    1. 10.1 Introduction
    2. 10.2 Paired Observations from a Highly Stratified Cohort Study
    3. 10.3 Clinical Trials Study Analysis
    4. 10.4 Crossover Design Studies
    5. 10.5 General Conditional Logistic Regression
    6. 10.6 Paired Observations in a Retrospective Matched Study
    7. 10.7 1:m Conditional Logistic Regression
    8. 10.8 Exact Conditional Logistic Regression in the Stratified Setting
    9. 10.9 Appendix A: Theory for the Case-Control Retrospective Setting
    10. 10.10 Appendix B: Theory for General Conditional Logistic Regression
    11. 10.11 Appendix C: Theory for Exact Conditional Inference
    12. 10.12 Appendix D: ODS Macro
  16. Chapter 11. Quantal Response Data Analysis
    1. 11.1 Introduction
    2. 11.2 Estimating Tolerance Distributions
    3. 11.3 Comparing Two Drugs
    4. 11.4 Analysis of Pain Study
    5. 11.5 Estimating Tolerance Distributions
    6. Appendix A: SAS/IML Macro for Confidence Intervals of Ratios Using Fieller's Theorem
  17. Chapter 12. Poisson Regression and Related Loglinear Models
    1. 12.1 Introduction
    2. 12.2 Methodology for Poisson Regression
    3. 12.3 Simple Poisson Counts Example
    4. 12.4 Poisson Regression for Incidence Densities
    5. 12.5 Overdispersion in Lower Respiratory Infection Example
    6. 12.6 Exact Poisson Regression
    7. 12.7 Loglinear Models
    8. 12.8 Analyzing Three-Way Cross-Classification Data with a Loglinear Model
    9. 12.9 Correspondence between Logistic Models and Loglinear Models
  18. Chapter 13. Categorized Time-to-Event Data
    1. 13.1 Introduction
    2. 13.2 Life Table Estimation of Survival Rates
    3. 13.3 Mantel-Cox Test
    4. 13.4 Piecewise Exponential Models
  19. Chapter 14. Weighted Least Squares
    1. 14.1 Introduction
    2. 14.2 Weighted Least Squares Methodology
    3. 14.3 Using PROC CATMOD for Weighted Least Squares Analysis
    4. 14.4 Obstetrical Pain Data: Advanced Modeling of Means
    5. 14.5 Analysis of Survey Sample Data
    6. 14.6 Modeling Rank Measures of Association Statistics
    7. 14.7 Repeated Measurements Analysis
    8. Appendix A: Statistical Methodology for Weighted Least Squares
    9. Appendix B: CONTRAST statements for Obstetrical Pain
  20. Chapter 15. Generalized Estimating Equations
    1. 15.1 Introduction
    2. 15.2 Methodology
    3. 15.3 Summary of the GEE Methodology
    4. 15.4 Passive Smoking Example
    5. 15.5 Using a Modified Wald Statistic to Assess Model Effects
    6. 15.6 Crossover Example
    7. 15.7 Respiratory Data
    8. 15.8 Diagnostic Data
    9. 15.9 Using GEE for Count Data
    10. 15.10 Fitting the Proportional Odds Model
    11. 15.11 GEE Analyses for Data with Missing Values
    12. 15.12 Alternating Logistic Regression
    13. 15.13 Using GEE to Account for Overdispersion: Univariate Outcome
    14. Appendix A: Steps to Find the GEE Solution
    15. 15.14 Appendix B: Macro for Adjusted Wald Statistic
  21. References
  22. Index
  23. Accelerate Your SAS Knowledge with SAS Books