Applied Categorical and Count Data Analysis

Developed from the authors' graduate-level biostatistics course, Applied Categorical and Count Data Analysis, Second Edition explains how to perform the statistical analysis of discrete data, including categorical and count outcomes. The authors have been teaching categorical data analysis courses at the University of Rochester and Tulane University for more than a decade. This book embodies their decade-long experience and insight in teaching and applying statistical models for categorical and count data. 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 relying on rigorous mathematical arguments.

The second edition covers classic concepts and popular topics, such as contingency tables, logistic regression 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. As in the first edition, 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.

Features:

-Describes the basic ideas underlying each concept and model.

-Includes R, SAS, SPSS and Stata programming codes for all the examples

-Features significantly expanded Chapters 4, 5, and 8 (Chapters 4-6, and 9 in the second edition.

-Expands discussion for subtle issues in longitudinal and clustered data analysis such as time varying covariates and comparison of generalized linear mixed-effect models with GEE.

《應用類別和計數資料分析，第二版》是根據作者們的研究生級別生物統計學課程開發而成，該書解釋了如何對離散資料進行統計分析，包括類別和計數結果。作者們在羅徹斯特大學和杜蘭大學教授類別資料分析課程已有十多年的經驗。本書體現了他們十年來在教學和應用類別和計數資料的統計模型方面的經驗和見解。作者們描述了每個概念、模型和方法背後的基本思想，使讀者能夠對方法論的基礎有很好的理解，而不依賴於嚴格的數學論證。

第二版涵蓋了經典概念和熱門話題，如列聯表、羅吉斯回歸模型和泊松回歸模型，以及現代領域，包括用於零修正計數結果的模型、參數化和半參數化長期資料分析、可靠性分析以及處理缺失值的方法。與第一版一樣，書中提供了R、SAS、SPSS和Stata編程代碼，供所有示例使用，讀者可以立即對示例中的資料進行實驗，甚至適應或擴展代碼以適應自己研究的資料。

本書設計為生物統計學研究生和高年級本科生的一學期課程教材，也適合作為生物醫學和心理社會研究人員的自學指南。它將幫助讀者在廣泛的生物醫學和心理社會研究領域中分析具有離散變數的資料。

特點：
-描述了每個概念和模型背後的基本思想。
-提供了所有示例的R、SAS、SPSS和Stata編程代碼。
-顯著擴展了第4、5和8章（第二版中的第4-6和9章）。
-擴展了對長期和集群資料分析中的細微問題的討論，如時間變化的協變量和廣義線性混合效應模型與GEE的比較。

Wan Tang (Ph.D.) is a Clinical Professor in the Department of Biostatistics and Data Science, Tulane University School of Public Health and Tropical Medicine. Dr. Tang's research interests include longitudinal data analysis, missing data modeling, structural equation models, causal inference, and nonparametric smoothing methods. He has co-edited a book on modern clinical trials.

Hua He (Ph.D.) is an Associate Professor in Biostatistics in the Department of Epidemiology at Tulane University School of Public Health and Tropical Medicine. Dr. He is a highly experienced biostatistician with expertise in longitudinal data analysis, structural equation models, potential outcome based causal inference, semiparametric models, ROC analysis and their applications to observational studies, and randomized controlled trials across a range of disciplines, especially in the behavioral and social sciences. She has co-authored a series of publications in peer-reviewed journals, one textbook on categorical data analysis and co-edited a book on statistical causal inference and their applications in public health research.

Xin Tu (Ph.D.) is a Professor in the Division of Biostatistics and Bioinformatics, Department of Family Medicine and Public Health, UCSD. Dr. Tu is well versed in statistical methods and their applications to a range of disciplines, particularly within the fields of biomedical, behavioral and social sciences. He has co-authored over 300 peer-reviewed publications, two textbooks on categorical data and applied U-statistics, and co-edited books on modern clinical trials and social network data analysis. He has done important work in the areas of longitudinal data analysis, causal inference, U-statistics, survival analysis with interval censoring and truncation, pooled testing, semiparametric efficiency, and has successfully applied his novel development to addressing important methodological problems in biomedical and psychosocial research.

萬堂（博士）是圖蘭大學公共衛生暨熱帶醫學學院生物統計與數據科學系的臨床教授。萬堂博士的研究興趣包括長期數據分析、缺失數據建模、結構方程模型、因果推斷和非參數平滑方法。他曾共同編輯一本關於現代臨床試驗的書籍。

何華（博士）是圖蘭大學公共衛生暨熱帶醫學學院流行病學系生物統計學副教授。何華博士是一位經驗豐富的生物統計學家，專長包括長期數據分析、結構方程模型、基於潛在結果的因果推斷、半參數模型、ROC分析以及它們在觀察研究和隨機對照試驗中的應用，尤其在行為和社會科學領域。她曾在同行評審期刊上合著一系列的出版物，撰寫一本關於類別數據分析的教科書，並共同編輯一本關於統計因果推斷及其在公共衛生研究中的應用的書籍。

徐欣（博士）是加州大學聖地牙哥分校家庭醫學與公共衛生部生物統計與生物信息學分部的教授。徐欣博士精通統計方法及其在生物醫學、行為科學和社會科學等領域的應用。他曾合著超過300篇同行評審的出版物，撰寫兩本關於類別數據和應用U統計的教科書，並共同編輯了關於現代臨床試驗和社交網絡數據分析的書籍。他在長期數據分析、因果推斷、U統計、帶有區間截獲和截斷的生存分析、集體檢測、半參數效率等領域做出了重要的工作，並成功將其新方法應用於解決生物醫學和心理社會研究中的重要方法學問題。