Applied Meta-Analysis with R
Preface
In Chapter 8 of our previous book (Chen and Peace, 2010), we brie
y 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 scientic
publications are presented. After presenting the data and sucient background
to permit understanding of the application, various meta-analysis methods
appropriate for analyzing data are identied. 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 meta-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. In addition, the book should
be a valuable reference for self-study and a learning tool for practitioners and
biostatisticians in public health, medical research universities, governmental
agencies and the pharmaceutical industry, particularly those with little or no
experience in using R.
R has become widely used in statistical modeling and computing since its
creation in the mid 1990s and it is now an integrated and essential software
for statistical analyses. Becoming familiar with R is then imperative for the
next generation of statistical data analysts. In Chapter 1, we present a basic
introduction to the R system, where to get R, how to install R and how to
upgrade R packages. Readers who are already familiar with R may skip this
chapter and go directly to any of the remaining chapters.
In Chapter 2, we provide an overview of the research protocols for metaanalysis.
In Chapter 3, we provide an overall introduction to meta-analysis
for both xed-eects and random-eects models in meta-analysis. Two real
datasets are introduced along with two commonly used R packages of meta
and rmeta.
In Chapters 4 and 5, we present meta-analysis for specic data types. In
Chapter 4, we consider meta-analysis with binary data. We begin this chapter
with two real Datasets. The rst is a meta-analysis of \Statin Clinical Trials"
to compare intensive statin therapy to moderate statin therapy in the
reduction of cardiovascular outcomes. The second is a meta-analysis of ve
studies on Lamotrigine for the treatment of bipolar depression. In Chapter 5,
we consider meta-analysis for continuous data. Similarly to Chapter 4, we introduce
two published datasets. The rst dataset uses 6 studies on the impact
of intervention. The second dataset is of studies from the literature comparing
tubeless to standard percutaneous nephrolithotomy.
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.
2013 -- ISBN: 1466505990 -- English -- 342 pages -- PDF -- 3.25 MB
Download
*
Preface
In Chapter 8 of our previous book (Chen and Peace, 2010), we brie
y 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 scientic
publications are presented. After presenting the data and sucient background
to permit understanding of the application, various meta-analysis methods
appropriate for analyzing data are identied. 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 meta-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. In addition, the book should
be a valuable reference for self-study and a learning tool for practitioners and
biostatisticians in public health, medical research universities, governmental
agencies and the pharmaceutical industry, particularly those with little or no
experience in using R.
R has become widely used in statistical modeling and computing since its
creation in the mid 1990s and it is now an integrated and essential software
for statistical analyses. Becoming familiar with R is then imperative for the
next generation of statistical data analysts. In Chapter 1, we present a basic
introduction to the R system, where to get R, how to install R and how to
upgrade R packages. Readers who are already familiar with R may skip this
chapter and go directly to any of the remaining chapters.
In Chapter 2, we provide an overview of the research protocols for metaanalysis.
In Chapter 3, we provide an overall introduction to meta-analysis
for both xed-eects and random-eects models in meta-analysis. Two real
datasets are introduced along with two commonly used R packages of meta
and rmeta.
In Chapters 4 and 5, we present meta-analysis for specic data types. In
Chapter 4, we consider meta-analysis with binary data. We begin this chapter
with two real Datasets. The rst is a meta-analysis of \Statin Clinical Trials"
to compare intensive statin therapy to moderate statin therapy in the
reduction of cardiovascular outcomes. The second is a meta-analysis of ve
studies on Lamotrigine for the treatment of bipolar depression. In Chapter 5,
we consider meta-analysis for continuous data. Similarly to Chapter 4, we introduce
two published datasets. The rst dataset uses 6 studies on the impact
of intervention. The second dataset is of studies from the literature comparing
tubeless to standard percutaneous nephrolithotomy.
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.
2013 -- ISBN: 1466505990 -- English -- 342 pages -- PDF -- 3.25 MB
Download
*