Applied Meta-Analysis with R and Stata

Applied Meta-Analysis Using R and Stata, Second Edition provides a thorough presentation of statistical meta-analyses (MA) with step-by-step implementations using R/Stata. The authors develop analysis step-by-step using appropriate R/Stata functions, which enables readers to gain an understanding of meta-analysis methods and R/Stata implementation so that they can use these two popular software packages to analyze their own meta-data. Each chapter gives examples of real studies compiled from the literature. After presenting the data and necessary background for understanding the applications, various methods for analyzing meta-data are introduced. The authors then develop analysis code using the appropriate R/Stata packages and functions.

What's New in the Second Edition

• Adds Stata programs along with the R programs for Meta-Analysis.
• Updates all the statistical Meta-Analyses with R/Stata programs.
• Covers fixed-effects and random-effects MA, meta-regression, MA with rare-event, and MA-IPD vs. MA-SS.
• Adds five new chapters on multivariate MA, publication bias, missing data in MA, MA in evaluating diagnostic accuracy, and network MA.

Suitable as a graduate-level text for a meta-data analysis course, the book is also a valuable reference for practitioners and biostatisticians (even those with little or no experience in using R or Stata) in public health, medical research, governmental agencies, and the pharmaceutical industry.

《應用R和Stata進行元分析，第二版》提供了對統計元分析（MA）的全面介紹，並使用R/Stata進行逐步實施。作者使用適當的R/Stata函數逐步開發分析，使讀者能夠瞭解元分析方法和R/Stata的實施，以便使用這兩個流行的軟件包來分析自己的元數據。每章都提供了從文獻中編譯的真實研究示例。在介紹數據和必要的背景以理解應用程序之後，介紹了分析元數據的各種方法。然後，作者使用適當的R/Stata包和函數開發分析代碼。

第二版的新內容包括：
- 添加了Stata程序，以及用於元分析的R程序。
- 使用R/Stata程序更新了所有統計元分析。
- 包括固定效應和隨機效應的MA、元回歸、罕見事件的MA和MA-IPD vs. MA-SS。
- 添加了五個新章節，包括多變量MA、發表偏差、MA中的缺失數據、評估診斷準確性的MA和網絡MA。

這本書適合作為研究生水平的元數據分析課程教材，也是公共衛生、醫學研究、政府機構和製藥行業的從業人員和生物統計學家（即使對使用R或Stata沒有或很少經驗的人）的寶貴參考資料。

Ding-Geng (Din) Chen is a fellow of American Statistical Association and currently the Wallace H. Kuralt Distinguished Professor at the University of North Carolina-Chapel Hill, USA. Formerly, he was a Professor of Biostatistics at the University of Rochester, New York, USA, the Karl E. Peace Endowed Eminent Scholar Chair and professor in Biostatistics in the Jiann-Ping Hsu College of Public Health at Georgia Southern University, USA, and a professor of statistics at South Dakota Stata University, USA. Dr. Chen's research interests include clinical trial biostatistical methodological development in Bayesian models, survival analysis, multi-level modelling and longitudinal data analysis, and statistical meta-analysis. He has published more than 200 refereed papers and co-authored/co-edited 30 book in statistics.

Karl E. Peace is the Georgia Cancer Coalition Distinguished Cancer Scholar, Founding Director of the Center for Biostatistics, Professor of Biostatistics, and Senior Research Scientist in the Jiann-Ping Hsu College of Public Health at Georgia Southern University (GSU). Dr. Peace has made pivotal contributions in the development and approval of drugs to treat numerous diseases and disorders. A fellow of the ASA, he has been a recipient of many honors, including the Drug Information Association Outstanding Service Award, the American Public Health Association Statistics Section Award, The First recipient of the President's Medal for outstanding contributions to GSU, and recognition by the Georgia and US Houses of Representatives, and the Virginia House of Delegates.

丁耿（Din）陳是美國統計學會的會士，目前擔任美國北卡羅來納大學教堂山分校的華萊士·H·庫拉特傑出教授。此前，他曾在美國紐約州羅徹斯特大學擔任生物統計學教授，並在喬治亞南方大學的建平許公共衛生學院擔任卡爾·E·皮斯卓越學者講座教授和生物統計學教授，以及在南達科他州立大學擔任統計學教授。陳博士的研究興趣包括臨床試驗生物統計方法的發展、生存分析、多層次建模和長期數據分析，以及統計元分析。他發表了200多篇經過同行評審的論文，並共同編著了30本統計學書籍。

卡爾·E·皮斯是喬治亞癌症聯盟卓越癌症學者、建平許公共衛生學院生物統計學中心創始主任、生物統計學教授和高級研究科學家。皮斯博士在開發和批准治療多種疾病和疾患的藥物方面做出了重要貢獻。作為美國統計學會的會士，他獲得了許多榮譽，包括藥物信息協會傑出服務獎、美國公共衛生協會統計部門獎、喬治亞南方大學傑出貢獻總統獎章的首位獲獎者，以及喬治亞和美國眾議院以及維吉尼亞州眾議院的表彰。