
Categorical Data Analysis Using SAS, Third Edition
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Content
- Intro
- Contents
- Computing Details
- For More Information
- Acknowledgments
- Introduction
- Overview
- Scale of Measurement
- Sampling Frameworks
- Overview of Analysis Strategies
- Randomization Methods
- Modeling Strategies
- Working with Tables in SAS Software
- Using This Book
- The 2x2 Table
- Introduction
- Chi-Square Statistics
- Exact Tests
- Exact p-values for Chi-Square Statistics
- Difference in Proportions
- Odds Ratio and Relative Risk
- Exact Confidence Limits for the Odds Ratio
- Sensitivity and Specificity
- McNemar's Test
- Incidence Densities
- Sample Size and Power Computations
- Sets of 2x2 Tables
- Introduction
- Mantel-Haenszel Test
- Respiratory Data Example
- Health Policy Data
- Soft Drink Example
- Measures of Association
- Homogeneity of Odds Ratios
- Coronary Artery Disease Data Example
- Exact Confidence Intervals for the Common Odds Ratio
- 2 x r and s x 2 Tables
- Introduction
- The 2 x r Table
- Sets of 2 x r Tables
- Choosing Scores
- Analyzing the Arthritis Data
- Rank Statistics for Ordered Data
- Colds Example
- The s x 2 Table
- Sets of s x 2 Tables
- Correlation Statistic
- Analysis of Smokeless Tobacco Data
- Pain Data Analysis
- Relationships between Sets of Tables
- Exact Analysis of Association for the s x 2 Table
- The s by r Table
- Introduction
- Association
- Tests for General Association
- Mean Score Test
- Correlation Test
- Exact Tests for Association
- General Association
- Test of Correlation
- Measures of Association
- Ordinal Measures of Association
- Exact Tests for Ordinal Measures of Association
- Nominal Measures of Association
- Observer Agreement
- Computing the Kappa Statistic
- Exact p-values for the Kappa Statistic
- Test for Ordered Differences
- Sets of s by r Tables
- Introduction
- General Mantel-Haenszel Methodology
- General Association Statistic
- Mean Score Statistic
- Correlation Statistic
- Summary
- Mantel-Haenszel Applications
- Dumping Syndrome Data
- Shoulder Harness Data
- Learning Preference Data
- Advanced Topic: Application to Repeated Measures
- Introduction
- Dichotomous Response: Two Time Points (McNemar's Test)
- Dichotomous Response: Three Repeated Measurements
- Ordinal Response
- Ordinal Response with Missing Data
- Nonparametric Methods
- Introduction
- Kruskal-Wallis Test
- Friedman's Chi-Square Test
- Aligned Ranks Test for Randomized Complete Blocks
- Analyzing Incomplete Data
- Rank Analysis of Covariance
- Logistic Regression I: Dichotomous Response
- Introduction
- Dichotomous Explanatory Variables
- Logistic Model
- Model Fitting
- Goodness of Fit
- Using PROC LOGISTIC
- Interpretation of Main Effects Model
- Alternative Methods of Assessing Goodness of Fit
- Overdispersion
- Using the CLASS Statement
- Analysis of Sentencing Data
- Goodness-of-Fit Statistics for Single Main Effect Model
- Deviation from the Mean Parameterization
- Qualitative Explanatory Variables
- Model Fitting
- PROC LOGISTIC for Nominal Effects
- Testing Hypotheses about the Parameters
- Additional Graphics
- Fitting Models with Interactions
- Continuous and Ordinal Explanatory Variables
- Goodness of Fit
- Fitting a Main Effects Model
- A Note on Diagnostics
- Alternatives to Maximum Likelihood Estimation
- Analyzing the Pre-Clinical Study Data
- Analysis of Completely Separated Data
- Analysis of Liver Function Data
- Exact Confidence Limits for Common Odds Ratios for 2 x 2 Tables
- Using the GENMOD Procedure for Logistic Regression
- Performing Logistic Regression with the GENMOD Procedure
- Fitting Logistic Regression Models with PROC GENMOD
- Appendix A: Statistical Methodology for Dichotomous Logistic Regression
- Logistic Regression II: Polytomous Response
- Introduction
- Ordinal Response: Proportional Odds Model
- Methodology
- Fitting the Proportional Odds Model with PROC LOGISTIC
- Multiple Qualitative Explanatory Variables
- Partial Proportional Odds Model
- Nominal Response: Generalized Logits Model
- Methodology
- Fitting Models to Generalized Logits with PROC LOGISTIC
- Generalized Logit Model with Continuous Explanatory Variable
- Exact Methods for Generalized Logits Model
- Conditional Logistic Regression
- Introduction
- Paired Observations from a Highly Stratified Cohort Study
- Clinical Trials Study Analysis
- Analysis Using the LOGISTIC Procedure
- Crossover Design Studies
- Two-Period Crossover Design
- Three-Period Crossover Study
- General Conditional Logistic Regression
- Analyzing Diagnostic Data
- Paired Observations in a Retrospective Matched Study
- 1:1 Conditional Logistic Regression
- 1:m Conditional Logistic Regression
- Exact Conditional Logistic Regression in the Stratified Setting
- Printing More Digits
- Appendix A: Theory for the Case-Control Retrospective Setting
- Appendix B: Theory for General Conditional Logistic Regression
- Appendix C: Theory for Exact Conditional Inference
- Appendix D: ODS Macro
- Quantal Response Data Analysis
- Introduction
- Estimating Tolerance Distributions
- Analyzing the Bacterial Challenge Data
- Comparing Two Drugs
- Analysis of the Peptide Data
- Analysis of Pain Study
- Estimating Tolerance Distributions
- Appendix A: SAS/IML Macro for Confidence Intervals of Ratios Using Fieller's Theorem
- Poisson Regression and Related Loglinear Models
- Introduction
- Methodology for Poisson Regression
- Simple Poisson Counts Example
- Poisson Regression for Incidence Densities
- Overdispersion in Lower Respiratory Infection Example
- Exact Poisson Regression
- Loglinear Models
- Analyzing Three-Way Cross-Classification Data with a Loglinear Model
- Analyzing Loglinear Models with the CATMOD Procedure
- Correspondence between Logistic Models and Loglinear Models
- Categorized Time-to-Event Data
- Introduction
- Life Table Estimation of Survival Rates
- Computing Survival Estimates with the LIFETEST Procedure
- Mantel-Cox Test
- Piecewise Exponential Models
- An Application of the Proportional Hazards Piecewise Exponential Model
- Using PROC LOGISTIC to Fit the Piecewise Exponential Model
- Weighted Least Squares
- Introduction
- Weighted Least Squares Methodology
- Weighted Least Squares Framework
- Weighted Least Squares Estimation
- Model Parameterization
- Using PROC CATMOD for Weighted Least Squares Analysis
- Obstetrical Pain Data: Advanced Modeling of Means
- Performing the Analysis with PROC CATMOD
- Analysis of Survey Sample Data
- HANES Data
- Direct Input of Response Functions
- The FACTOR Statement
- Preliminary Analysis
- Inputting the Model Matrix Directly
- Modeling Rank Measures of Association Statistics
- Repeated Measurements Analysis
- WLS Methodology for Repeated Measurements
- One Population, Dichotomous Response
- Two Populations, Polytomous Response
- One Population Regression Analysis of Logits
- Appendix A: Statistical Methodology for Weighted Least Squares
- Appendix B: CONTRAST statements for Obstetrical Pain
- Generalized Estimating Equations
- Introduction
- Methodology
- Motivation
- Generalized Linear Models
- Generalized Estimating Equations Methodology
- Summary of the GEE Methodology
- Marginal Model
- Passive Smoking Example
- Using a Modified Wald Statistic to Assess Model Effects
- Crossover Example
- Respiratory Data
- Diagnostic Data
- Using GEE for Count Data
- Fitting the Proportional Odds Model
- GEE Analyses for Data with Missing Values
- Crossover Study with Missing Data
- Alternating Logistic Regression
- Respiratory Data
- Using GEE to Account for Overdispersion: Univariate Outcome
- Appendix A: Steps to Find the GEE Solution
- Appendix B: Macro for Adjusted Wald Statistic
- References
- Index
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