Applied Categorical and Count Data Analysis

Applied Categorical and Count Data Analysis

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Features

  • Shows how statistical models for noncontinuous responses are applied to real studies, emphasizing difficult and overlooked issues along the pathway from models to data
  • Reviews classic concepts and models for categorical data analysis, such as log-linear models for contingency tables and exact inference for logistic regression
  • Offers an easy-to-follow presentation of modern concepts and approaches for count data, such as structural zeros and population mixtures
  • Covers useful topics in modern-day clinical trials and observation studies, including longitudinal data analysis, measure scales, and counterfactual outcomes
  • Presents a systematic treatment of instrumentation and measurement models for latent constructs, including measures of agreement and internal consistency
  • Compares popular models for clustered data, such as GLMM and GEE/WGEE
  • Gives an in-depth study of missing values and their impact on parametric and semiparametric (distribution-free) models
  • Includes exercises at the end of each chapter, many real data examples, and sample programming codes in SAS, SPSS, and STATA for model implementations
  • Provides the codes and their updates on a supporting website

Summary

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.

Table of Contents

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.

Author Bio(s)

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.

Editorial Reviews

"… 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