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Applied Categorical and Count Data Analysis

By Wan Tang, Hua He, Xin M. Tu

Chapman and Hall/CRC – 2012 – 384 pages

Series: Chapman & Hall/CRC Texts in Statistical Science

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    978-1-43-980624-1
    June 4th 2012

Description

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.

Reviews

"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

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

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.

Name: Applied Categorical and Count Data Analysis (Hardback)Chapman and Hall/CRC 
Description: By Wan Tang, Hua He, Xin M. Tu. 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...
Categories: Quantitative Methods, Regression Analysis and Multivariate Statistics, Statistics for the Biological Sciences, Statistical Theory & Methods