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## Details

Genre/Form: | Electronic books |
---|---|

Additional Physical Format: | Print version: Tang, Wan. Applied Categorical and Count Data Analysis. Hoboken : CRC Press, ©2012 |

Material Type: | Document, Internet resource |

Document Type: | Internet Resource, Computer File |

All Authors / Contributors: |
Wan Tang; Hua He; Xin M Tu |

ISBN: | 9781439897935 143989793X |

OCLC Number: | 908078598 |

Description: | 1 online resource (380 pages). |

Contents: | Front Cover; Contents; List of Tables; List of Figures; Preface; 1. Introduction; 2. Contingency Tables; 3. Sets of Contingency Tables; 4. Regression Models for Categorical Response; 5. Regression Models for Count Response; 6. Log-Linear Models for Contingency Tables; 7. Analyses of Discrete Survival Time; 8. Longitudinal Data Analysis; 9. Evaluation of Instruments; 10. Analysis of Incomplete Data; References; Back Cover. |

Series Title: | Chapman & Hall/CRC Texts in Statistical Science. |

### Abstract:

## Reviews

*Editorial reviews*

Publisher Synopsis

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. Introduction Discrete Outcomes Data Source Outline of the BookReview of Key Statistical ResultsSoftwareContingency Tables Inference for One-Way Frequency TableInference for 2 x 2 TableInference for 2 x r TablesInference for s x r TableMeasures of AssociationSets of Contingency Tables Confounding Effects Sets of 2 x 2 TablesSets of s x r TablesRegression Models for Categorical Response Logistic Regression for Binary ResponseInference about Model ParametersGoodness of FitGeneralized Linear ModelsRegression Models for Polytomous ResponseRegression Models for Count Response Poisson Regression Model for Count ResponseGoodness of FitOverdispersionParametric Models for Clustered Count ResponseLoglinear Models for Contingency Tables Analysis of Loglinear ModelsTwo-Way Contingency TablesThree-Way Contingency TablesIrregular TablesModel SelectionAnalyses of Discrete Survival Time Special Features of Survival DataLife Table MethodsRegression ModelsLongitudinal Data Analysis Data Preparation and Exploration Marginal ModelsGeneralized Linear Mixed-Effects ModelModel DiagnosticsEvaluation of Instruments Diagnostic-abilityCriterion Validity Internal ReliabilityTest-Retest ReliabilityAnalysis of Incomplete DataIncomplete Data and Associated ImpactMissing Data MechanismMethods for Incomplete DataApplicationsReferencesIndexExercises appear at the end of each chapter. "There is a lot to like about this book. The topics are well written and the issues are clearly explained. â ¦ It covers very well topics that are not traditionally discussed in CDA books and for this reason it certainly is a valuable addition to one's bookshelf. For those who are looking for a book with a focus on applied data analysis (especially from a biostatistics perspective), this is a must-have book. For those who are interested in expanding their knowledge of recent advances in a broad range of CDA tools, [it] will serve you very well."-Australian & New Zealand Journal of Statistics, 2015"â ¦ 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 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 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. 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. 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. Read more...

*User-contributed reviews*