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.
Outline of the Book
Review of Key Statistical Results
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
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
Parametric Models for Clustered Count Response
Loglinear Models for Contingency Tables
Analysis of Loglinear Models
Two-Way Contingency Tables
Three-Way Contingency Tables
Analyses of Discrete Survival Time
Special Features of Survival Data
Life Table Methods
Longitudinal Data Analysis
Data Preparation and Exploration
Generalized Linear Mixed-Effects Model
Evaluation of Instruments
Analysis of Incomplete Data
Incomplete Data and Associated Impact
Missing Data Mechanism
Methods for Incomplete Data
Exercises appear at the end of each chapter.
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.
"… the book is well-written and for a mathematically oriented reader it should be quite easy to understand the methods introduced. Exercises, combined with practical data analyses, will certainly facilitate the adoption of the material."
—Tapio Nummi, International Statistical Review, 2014
"The combination of more advanced and mathematical explanations, newer topics, and sample code from all major software platforms makes this book a valuable addition to the literature on categorical data analysis."
—Russell L. Zaretzki, Journal of the American Statistical Association, September 2013
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