Full Description
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.
Contents
Introduction to R What Is R? Steps on Installing R and Updating R PackagesDatabase Management and Data Manipulations A Simple Simulation on Multi-Center Studies Summary and Recommendations for Further ReadingResearch Protocol for Meta-Analyses Introduction Defining the Research Objective Criteria for Identifying Studies to Include in the Meta-AnalysisSearching For and Collecting the Studies Data Abstraction and Extraction Meta-Analysis Methods Results Summary and DiscussionFixed Effects and Random Effects in Meta-Analysis Two Datasets from Clinical Studies Fixed-Effects and Random-Effects Models in Meta-AnalysisData Analysis in RWhich Model Should We Use? Fixed Effects or Random Effects?Summary and ConclusionsMeta-Analysis with Binary Data Meta-Analysis MethodsMeta-Analysis of Lamotrigine StudiesDiscussionsMeta-Analysis for Continuous Data Two Published Datasets Methods for Continuous DataMeta-Analysis of Tubeless versus Standard Percutaneous NephrolithotomyDiscussionHeterogeneity in Meta-Analysis Heterogeneity Quantity Q and the Test of heterogeneity in R meta The Quantifying Heterogeneity in R metaStep-By-Step Implementations in RDiscussionsMeta-Regression Data Meta-Regression Data Analysis Using R DiscussionIndividual Patient-Level Data Analysis versus Meta-Analysis Introduction Treatment Comparison for Changes in HAMDTreatment Comparison for Changes in MADRS Summary Simulation Study on Continuous Outcomes DiscussionsMeta-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 CoefficientsMultivariate Meta-Analysis DiscussionsIndex
