Overview

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.

Full Product Details

Author:   Wan Tang ,  Hua He ,  Xin M. Tu
Publisher:   Taylor & Francis Ltd
Imprint:   Chapman & Hall/CRC
Dimensions:   Width: 15.60cm , Height: 2.70cm , Length: 23.50cm
Weight:   0.688kg
ISBN:  

9781439806241

ISBN 10:   1439806241
Pages:   384
Publication Date:   04 July 2012
Audience:   College/higher education ,  Postgraduate, Research & Scholarly
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print  
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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.

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Author Information

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.

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