資料來源:
三民書局
Applied meta-analysis with R / Ding-Geng (Din) Chen, Karl E. Peace.
- 作者： Chen, Ding-Geng.
- 其他作者：
- 其他題名：
- Chapman & Hall/CRC biostatistics series.
- 出版： Boca Raton, FL : CRC Press, Taylor & Francis Group c2013.
- 叢書名： Chapman & Hall/CRC biostatistics series
- 主題： Biostatistics--methods. , Meta-Analysis as Topic. , Software.
- ISBN： 9781466505995 (bound): NT2215 、 1466505990 (bound)
- 一般註：Includes bibliographical references (p. 305-313) and index.
-
讀者標籤：
- 系統號： 000252647 | 機讀編目格式
館藏資訊
In biostatistical research and courses, practitioners and students often lack a thorough understanding of how to apply statistical methods to synthesize biomedical and clinical trial data. Filling thi
摘要註
"Preface In Chapter 8 of our previous book (Chen and Peace, 2010), we briefy introduced meta-analysis using R. Since then, we have been encouraged to develop an entire book on meta-analyses using R that would include a wide variety of applications - which is the theme of this book. In this book we provide a thorough presentation of meta-analysis with detailed step-by-step illustrations on their implementation using R. In each chapter, examples of real studies compiled from the literature and scienti c publications are presented. After presenting the data and sufficient background to permit understanding the application, various meta-analysis methods appropriate for analyzing data are identified. Then analysis code is developed using appropriate R packages and functions to meta-analyze the data. Analysis code development and results are presented in a stepwise fashion. This stepwise approach should enable readers to follow the logic and gain an understanding of the analysis methods and the R implementation so that they may use R and the steps in this book to analyze their own meta-data. Based on their experience in biostatistical research and teaching biostatistical meta-analysis, the authors understand that there are gaps between developed statistical methods and applications of statistical methods by students and practitioners. This book is intended to ll this gap by illustrating the implementation of statistical mata-analysis methods using R applied to real data following a step-by-step presentation style. With this style, the book is suitable as a text for a course in meta-data analysis at the graduate level (Master's or Doctorate's), particularly for students seeking degrees in statistics or biostatistics"--
