Applied meta-analysis with R
著者
書誌事項
Applied meta-analysis with R
（Chapman & Hall/CRC biostatistics series）
CRC Press, c2013
- : hardback
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注記
Includes bibliographical references (p. 305-313) and index
内容説明・目次
内容説明
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 this knowledge gap, Applied Meta-Analysis with R shows how to implement statistical meta-analysis methods to real data using R.
Drawing on their extensive research and teaching experiences, the authors provide detailed, step-by-step explanations of the implementation of meta-analysis methods using R. 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 packages and functions. This systematic approach helps readers thoroughly understand the analysis methods and R implementation, enabling them to use R and the methods to analyze their own meta-data.
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) in public health, medical research, governmental agencies, and the pharmaceutical industry.
目次
Introduction to R
What Is R?
Steps on Installing R and Updating R Packages
Database Management and Data Manipulations
A Simple Simulation on Multi-Center Studies
Summary and Recommendations for Further Reading
Research Protocol for Meta-Analyses
Introduction
Defining the Research Objective
Criteria for Identifying Studies to Include in the Meta-Analysis
Searching For and Collecting the Studies
Data Abstraction and Extraction
Meta-Analysis Methods
Results
Summary and Discussion
Fixed Effects and Random Effects in Meta-Analysis
Two Datasets from Clinical Studies
Fixed-Effects and Random-Effects Models in Meta-Analysis
Data Analysis in R
Which Model Should We Use? Fixed Effects or Random Effects?
Summary and Conclusions
Meta-Analysis with Binary Data
Meta-Analysis Methods
Meta-Analysis of Lamotrigine Studies
Discussions
Meta-Analysis for Continuous Data
Two Published Datasets
Methods for Continuous Data
Meta-Analysis of Tubeless versus Standard Percutaneous Nephrolithotomy
Discussion
Heterogeneity in Meta-Analysis
Heterogeneity Quantity Q and the Test of heterogeneity in R meta
The Quantifying Heterogeneity in R meta
Step-By-Step Implementations in R
Discussions
Meta-Regression
Data
Meta-Regression
Data Analysis Using R
Discussion
Individual Patient-Level Data Analysis versus Meta-Analysis
Introduction
Treatment Comparison for Changes in HAMD
Treatment Comparison for Changes in MADRS
Summary
Simulation Study on Continuous Outcomes
Discussions
Meta-Analysis for Rare Events
The Rosiglitazone Meta-Analysis
Step-by-Step Data Analysis in R
Discussion
Other R Packages for Meta-Analysis
Combining p-Values in Meta-Analysis
R Packages for Meta-Analysis of Correlation Coefficients
Multivariate Meta-Analysis
Discussions
Index
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