Applied Categorical and Count Data Analysis

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.

Wan Tang is a research assistant professor in the Department of Biostatistics and Computational Biology at the University of Rochester Medical Center. Dr. Tang’s research interests include longitudinal data analysis, missing data modeling, structural equation models, and smoothing methods.
Hua He is an assistant professor in the Department of Biostatistics and Computational Biology at the University of Rochester Medical Center. Dr. He’s research interests include ROC analysis, nonparametric curve estimation, longitudinal data analysis, psychosocial and behavior statistics, causal inference, and the analysis of missing data.
Xin M. Tu is a professor of biostatistics and psychiatry in the Department of Biostatistics and Computational Biology and Department of Psychiatry at the University of Rochester Medical Center. He is also the director of the Statistical Consulting Center and director of the Psychiatric Statistics Division. Dr. Tu’s research areas include U-statistics, longitudinal data analysis, survival analysis, pooled testing, and the biological, behavioral, and societal factors involved in the study of disease etiology and treatment.

Introduction Discrete Outcomes Data Source Outline of the BookReview of Key Statistical ResultsSoftware

Contingency Tables Inference for One-Way Frequency TableInference for 2 x 2 TableInference for 2 x r TablesInference for s x r TableMeasures of Association

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

Regression Models for Categorical Response Logistic Regression for Binary ResponseInference about Model ParametersGoodness of FitGeneralized Linear ModelsRegression Models for Polytomous Response

Regression Models for Count Response Poisson Regression Model for Count ResponseGoodness of FitOverdispersionParametric Models for Clustered Count Response

Loglinear Models for Contingency Tables Analysis of Loglinear ModelsTwo-Way Contingency TablesThree-Way Contingency TablesIrregular TablesModel Selection

Analyses of Discrete Survival Time Special Features of Survival DataLife Table MethodsRegression Models

Longitudinal Data Analysis Data Preparation and Exploration Marginal ModelsGeneralized Linear Mixed-Effects ModelModel Diagnostics

Evaluation of Instruments Diagnostic-abilityCriterion Validity Internal ReliabilityTest-Retest Reliability

Analysis of Incomplete DataIncomplete Data and Associated ImpactMissing Data MechanismMethods for Incomplete DataApplications

References
Index

Exercises appear at the end of each chapter.