Developed from the authors’ graduate-level biostatistics course, Applied Categorical and Count Data Analysis explains how to perform the statistical analysis of discrete data, including categorical and count outcomes. The authors describe the basic ideas underlying each concept, model, and approach to give readers a good grasp of the fundamentals of the methodology without using rigorous mathematical arguments.

The text covers classic concepts and popular topics, such as contingency tables, logistic models, and Poisson regression models, along with modern areas that include models for zero-modified count outcomes, parametric and semiparametric longitudinal data analysis, reliability analysis, and methods for dealing with missing values. R, SAS, SPSS, and Stata programming codes are provided for all the examples, enabling readers to immediately experiment with the data in the examples and even adapt or extend the codes to fit data from their own studies.

Designed for a one-semester course for graduate and senior undergraduate students in biostatistics, this self-contained text is also suitable as a self-learning guide for biomedical and psychosocial researchers. It will help readers analyze data with discrete variables in a wide range of biomedical and psychosocial research fields.

This self-contained text explains how to perform the statistical analysis of discrete data. It covers classic concepts and popular topics, such as contingency tables, logistic models, and Poisson regression models, along with modern areas that include models for zero-modified count outcomes, parametric and semiparametric longitudinal data analysis, reliability analysis, and methods for dealing with missing values. R, SAS, SPSS, and Stata programming codes are provided for all the examples, enabling readers to immediately experiment with the data in the examples.

Introduction
Discrete Outcomes
Data Source
Outline of the Book
Review of Key Statistical Results
Software

Contingency Tables
Inference for One-Way Frequency Table
Inference for 2 x 2 Table
Inference for 2 x r Tables
Inference for s x r Table
Measures of Association

Sets of Contingency Tables
Confounding Effects
Sets of 2 x 2 Tables
Sets of s x r Tables

Regression Models for Categorical Response
Logistic Regression for Binary Response
Inference about Model Parameters
Goodness of Fit
Generalized Linear Models
Regression Models for Polytomous Response

Regression Models for Count Response
Poisson Regression Model for Count Response
Goodness of Fit
Overdispersion
Parametric Models for Clustered Count Response

Loglinear Models for Contingency Tables
Analysis of Loglinear Models
Two-Way Contingency Tables
Three-Way Contingency Tables
Irregular Tables
Model Selection

Analyses of Discrete Survival Time
Special Features of Survival Data
Life Table Methods
Regression Models

Longitudinal Data Analysis
Data Preparation and Exploration
Marginal Models
Generalized Linear Mixed-Effects Model
Model Diagnostics

Evaluation of Instruments
Diagnostic-ability
Criterion Validity
Internal Reliability
Test-Retest Reliability

Analysis of Incomplete Data
Incomplete Data and Associated Impact
Missing Data Mechanism
Methods for Incomplete Data
Applications

References

Index

Exercises appear at the end of each chapter.