Review of the First Edition:
The authors strive to reduce theory to a minimum, which makes it a self-learning text that is comprehensible for biologists, physicians, etc. who lack an advanced mathematics background. Unlike in many other textbooks, R is not introduced with meaningless toy examples; instead the reader is taken by the hand and shown around some analyses, graphics, and simulations directly relating to meta-analysis… A useful hands-on guide for practitioners who want to familiarize themselves with the fundamentals of meta-analysis and get started without having to plough through theorems and proofs.
—Journal of Applied Statistics
Statistical Meta-Analysis with 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.
Author(s): Ding-Geng (Din) Chen, Karl E. Peace
Edition: 2
Publisher: Chapman and Hall/CRC
Year: 2021
Language: English
Commentary: Uploaded by MRMuyinda
Pages: 456
City: New York
Tags: meta-analysis
Cover
Half Title
Series Page
Title Page
Copyright Page
Dedication
Table of Contents
Preface for the Second Edition
Preface for the First Edition
Authors
List of Figures
List of Tables
1 Introduction to R and Stata for Meta-Analysis
1.1 Introduction to R for Meta-Analysis
1.1.1 What is R?
1.1.2 Steps on Installing R and Updating R Packages
1.1.2.1 First Step: Install R Base System
1.1.2.2 Second Step: Installing and Updating R Packages
1.1.2.3 Steps to Get Help and Documentation
1.1.3 Database Management and Data Manipulations
1.1.3.1 Data Management with RMySQL
1.1.3.2 Data Management with Microsoft Excel and R Package gdata
1.1.3.3 Data Management with Microsoft Excel and R Package xlsx
1.1.3.4 Data Management with Microsoft Excel and R Package readxl
1.1.3.5 Other Methods to Read Data into R
1.1.3.6 R Package foreign
1.2 A Simple Simulation on Multicenter Studies for Meta-Analysis
1.2.1 Data Simulation
1.2.1.1 R Functions
1.2.1.2 Data Generation and Manipulation
1.2.1.3 Basic R Graphics
1.2.2 Data Analysis
1.2.2.1 Data Analysis from Each Center
1.2.2.2 Data Analysis with Pooled Data from Five Centers
1.2.2.3 A Brief Introduction to Meta-Analysis
1.3 Introduction of Stata for Meta-Analysis
1.4 Summary and Recommendations for Further Reading about Using R
2 Research Protocol for Meta-Analyses
2.1 Introduction
2.2 Defining the Research Objective
2.3 Criteria for Identifying Studies to Include in the Meta-Analysis
2.3.1 Clarifying the Disease under Study (What Is Meant by Mild-to-Moderate?)
2.3.2 The Effectiveness Measure or Outcome
2.3.3 The Type of Control Group
2.3.4 Study Characteristics
2.3.5 Type of Patient
2.3.6 Length of Study
2.4 Searching for and Collecting the Studies
2.5 Data Abstraction and Extraction
2.6 Meta-Analysis Methods
2.7 Results
2.8 Summary and Discussion
3 Fixed Effects and Random Effects in Meta-Analysis
3.1 Two Data Sets from Clinical Studies
3.1.1 Data for Cochrane Collaboration Logo: Binary Data
3.1.2 Clinical Studies on Amlodipine: Continuous Data
3.2 Fixed-Effects and Random-Effects Models in Meta-Analysis
3.2.1 Hypotheses and Effect Size
3.2.2 Fixed-Effects Meta-Analysis Model: The Weighted-Average
3.2.2.1 Fixed-Effects Model
3.2.2.2 The Weighting Schemes
3.2.3 Random-Effects Meta-Analysis Model: DerSimonian-Laird
3.2.3.1 Random-Effects Model
3.2.3.2 Derivation of DerSimonian-Laird Estimator of r[sup(2)]
3.2.4 Publication Bias
3.3 Meta-Analysis for Data from Cochrane Collaboration Logo
3.3.1 The Data
3.3.2 Fitting the Fixed-Effects Model
3.3.3 Fitting the Random-Effects Model
3.4 Meta-Analysis of Amlodipine Trial Data
3.4.1 The Data
3.4.2 Meta-Analysis with meta Package
3.4.2.1 Fit the Fixed-Effects Model
3.4.2.2 Fit the Random-Effects Model
3.4.3 Meta-Analysis with the metafor Package
3.4.3.1 Calculate the Effect Size
3.4.3.2 Fit the Fixed-Effects Model
3.4.3.3 Fit the Random-Effects Model
3.5 Which Model Should We Use? Fixed Effects or Random Effects?
3.5.1 Fixed-Effects
3.5.2 Random-Effects
3.5.3 Performing Both a Fixed-Effects and a Random-Effects Meta-Analysis
3.6 Summary and Conclusions
Appendix : Stata Programs for Fixed-Effects and Random-Effects in Meta-Analysis by Manyun Liu
4 Meta-Analysis with Binary Data
4.1 Data from Real Life Studies
4.1.1 Statin Clinical Trials
4.1.2 Five Studies on Lamotrigine for Treatment of Bipolar Depression
4.2 Meta-Analysis Methods
4.2.1 Analysis with RR
4.2.1.1 Definition
4.2.1.2 Statistical Significance
4.2.1.3 The RR Meta-Analysis: Step-by-Step
4.2.1.4 RR Meta-Analysis with R package meta
4.2.1.5 RR Meta-Analysis with R package metafor
4.2.2 Analysis with Risk Difference
4.2.2.1 Definition
4.2.2.2 Meta-Analysis with Step-by-Step Implementation
4.2.2.3 Meta-Analysis in R Package meta
4.2.2.4 Meta-Analysis in R Package metafor
4.2.3 Meta-Analysis with OR
4.2.3.1 Data Structure
4.2.3.2 OR: Woolf's Method
4.2.3.3 Meta-Analysis with R Package meta
4.2.3.4 Meta-Analysis with R Package metafor
4.2.4 Meta-Analysis Using Mantel-Haenszel Method
4.2.4.1 Details of the Mantel-Haenszel Method
4.2.4.2 Step-by-Step R Implementation
4.2.4.3 Meta-Analysis Using R Library Meta
4.2.5 Peto's Meta-Analysis Method
4.2.5.1 Peto's Odds Ratio
4.2.5.2 Step-by-Step Implementation in R
4.2.5.3 R Implementation in meta
4.3 Meta-Analysis of Lamotrigine Studies
4.3.1 Risk Ratio
4.3.2 Risk Difference
4.3.3 Odds Ratio
4.4 Discussions
Appendix: Stata Programs for Meta-Analysis with Binary Data by Manyun Liu
5 Meta-Analysis for Continuous Data
5.1 Two Published Data Sets
5.1.1 Impact of Intervention
5.1.2 Tubeless vs Standard PCNL
5.2 Methods for Continuous Data
5.2.1 Estimate the MD △
5.2.2 Estimate the SMD ó
5.3 Meta-Analysis of the Impact of Intervention
5.3.1 Load the Data Into R
5.3.2 Meta-Analysis Using R Library meta
5.3.3 Step-by-Step Implementation in R
5.3.4 Meta-Analysis Using R Library metafor
5.4 Meta-Analysis of Tubeless vs Standard PCNL
5.4.1 Comparison of Operation Duration
5.4.2 Comparison of Length of Hospital Stay
5.4.3 Comparison of Postoperative Analgesic Requirement
5.4.4 Comparison of Postoperative Hematocrit Change
5.4.5 Conclusions and Discussion
5.5 Discussion
Appendix: Stata Programs for Meta-Analysis for Continuous Data by Manyun Liu
6 Heterogeneity in Meta-Analysis
6.1 Heterogeneity Quantity Q and the Test of heterogeneity
6.2 Quantifying Heterogeneity
6.2.1 The τ[sup(2)] Index
6.2.2 The H Index
6.2.3 The I[sup(2)] Index
6.3 Illustration with Cochrane Collaboration Logo Data – Binomial Data
6.3.1 Cochrane Collaboration Logo Data
6.3.2 Illustration Using R Package meta
6.3.3 Implementation in R: Step by Step
6.3.4 Meta-Analysis Using R Package metafor
6.4 Illustration with PCNL Meta-Data – Continuous Data
6.4.1 Tubeless vs Standard PCNL Data
6.4.2 Implementation in R Library meta
6.4.3 Implementation in R: Step by Step
6.4.4 Implementation in R Library metafor
6.5 Discussions
Appendix: Stata Programs for Heterogeneity Assessment by Manyun Liu
7 Meta-Regression
7.1 Data
7.1.1 BCG Vaccine Data
7.1.2 Ischemic Heart Disease
7.1.3 ADHD for Children and Adolescents
7.2 The Methods in Meta-Regression
7.3 Meta-Analysis of BCG Data
7.3.1 Random-Effects Meta-Analysis
7.3.2 Meta-Regression Analysis
7.3.3 Meta-Regression vs Weighted Regression
7.4 Meta-Analysis of IHD Data
7.4.1 IHD Data
7.4.2 Random-Effects Meta-Analysis
7.4.3 Meta-Regression Analysis
7.4.4 Comparison of Different Fitting Methods
7.5 Meta-Analysis of ADHD Data
7.5.1 Data and Variables
7.5.2 Meta-Analysis
7.5.3 Meta-Regression Analysis
7.5.4 Summary of ADHD Meta-Analysis
7.6 Discussion
Appendix: Stata Programs for Meta-Regression by Manyun Liu
8 Multivariate Meta-Analysis
8.1 The Model and the Package of mvmeta
8.2 Bivariate Meta-Data from Five Clinical Trials on Periodontal Disease
8.3 Meta-Analysis with R Package mvmeta
8.3.1 Fixed-Effects Meta-Analysis
8.3.2 Random-Effects Meta-Analysis
8.3.3 Meta-Regression
8.4 Meta-Analysis with R Package metafor
8.4.1 Rearrange the Data Format
8.4.2 Fixed-Effects Meta-Analysis
8.4.3 Random-Effects Meta-Analysis
8.4.4 Meta-Regression
8.5 Discussions
Appendix: Stata Programs for Multivariate Meta-Analysis by Manyun Liu
9 Publication Bias in Meta-Analysis
9.1 Introduction
9.2 Reasons for Publication Bias in Systematic Review
9.3 Dealing with Publication Bias
9.4 Assessing the Potential of the Publication Bias
9.4.1 R Codes
9.4.2 Stata Codes
9.5 Statistical Tests for Funnel-Plot Asymmetry
9.5.1 Begg and Mazumdar's Rank Correlation Method
9.5.2 Egger's Linear Regression Test
9.5.3 Illustration with R and Stata System
9.5.3.1 Using Data from Cochrane Collaboration Logo (Table 9.1)
9.5.3.2 Using the Effect of Streptokinase After a Myocardial Infarction (strepto.dta) Data
9.6 Other Issues of Publication Bias and Remedies
10 Strategies to Handle Missing Data in Meta-Analysis
10.1 Introduction
10.2 Strategies to Handle Missing Data in Meta-Analysis
10.2.1 Deletion Methods
10.2.1.1 List-Wise Deletion (Complete Case Analysis)
10.2.1.2 Pair-Wise Deletion (Available Case Analysis)
10.2.2 Single Imputation Methods
10.2.3 Model-Based Methods For Missing Data
10.2.3.1 Maximum Likelihood Method
10.2.3.2 Multiple Imputations
10.3 Sensitivity Analysis by Informative Missingness Approach
10.4 Application
10.4.1 Meta-Analysis of Studies With Missing Outcome
10.4.2 Meta-Analysis of Studies With Missing Predictors
10.5 Conclusion
11 Meta-Analysis for Evaluating Diagnostic Accuracy
11.1 Introduction
11.2 Medical Diagnostic Tests Accuracy Studies
11.3 Meta-Analysis Pooling a Single Value of the Sensitivity or the Specificity
11.4 Joint Meta-Analysis of Sensitivity and Specificity Resulting in a Summary Point
11.5 Joint Meta-Analysis of Sensitivity and Specificity Resulting in Summary ROC Curve
11.6 R Demonstration
11.7 Stata Demonstration
11.8 Other Statistical Packages for DTA
12 Network Meta-Analysis
12.1 Concepts and Challenges of Network Meta-Analysis
12.2 Data Sets
12.2.1 Diabetes Clinical Trials
12.2.2 Parkinson's Clinical Trials
12.3 Frequentist Network Meta-Analysis
12.3.1 Fixed-Effects Model
12.3.1.1 Multiarm Studies
12.3.1.2 I-Squared for Network Meta-Analysis
12.3.2 Random-Effects Model
12.4 Network Meta-Analysis of Diabetes Clinical Trial Data
12.4.1 Network Meta-Analysis
12.4.2 The Treatment Ranking
12.4.3 Graphical Display of the Network Model
12.4.4 Forest Plots
12.5 The Net Heat Plot
12.6 Bayesian Network Meta-Analysis
12.6.1 Introduction to Bayesian Inference
12.6.1.1 Baye's Theorem
12.6.1.2 Prediction
12.6.1.3 Bayesian Computation with Simulation
12.6.2 Bayesian Meta-Analysis Models
12.6.2.1 Fixed-Effects Models
12.6.2.2 Random-Effects Models
12.6.3 Bayesian Network Meta-Analysis Model
12.6.4 Multiarm Trials
12.7 Bayesian Network Meta-Analysis of Parkinson's Clinical Trial Data in R
12.7.1 Data Preparation and Visualization
12.7.2 Generate the Network Meta-Analysis Model
12.7.3 Specify the Priors
12.7.4 Markov Chain Monte Carlo Simulation
12.7.5 Assessing the Convergence
12.7.6 Assessing Inconsistency: The Nodesplit Method
12.7.7 Summarize the Network Meta-Analysis Results
12.8 Network Meta-Analysis of Parkinson's Clinical Trial Data in Stata
13 Meta-Analysis for Rare Events
13.1 The Rosiglitazone Meta-Analysis
13.2 Step-by-Step Data Analysis in R
13.2.1 Load the Data
13.2.2 Data Analysis for MI
13.2.3 Data Analysis for Cardiovascular Death (Death)
13.3 Discussion
14 Meta-Analyses with Individual Patient-Level Data versus Summary Statistics
14.1 IPD with Five Studies of Lamotrigine
14.2 Treatment Comparison for Changes in HAMD
14.2.1 Meta-Analysis with IPD
14.2.1.1 IPD Analysis by Each Study
14.2.1.2 IPD Analysis with Pooled Data
14.2.1.3 IPD Analysis Incorporating Covariates
14.2.1.4 IPD Analysis with Linear Mixed-Effects Model
14.2.1.5 Summary of IPD Analysis
14.2.2 Meta-Analysis with SS
14.2.2.1 Generate the SS
14.2.2.2 Calculate the Effect Size: Mean Difference
14.2.2.3 Meta-Analysis with Fixed-Effects Model
14.2.2.4 Meta-Analysis with Random-Effects Model
14.3 Treatment Comparison for Changes in MADRS
14.3.1 Meta-Analysis with IPD
14.3.1.1 IPD Analysis for Each Study
14.3.1.2 IPD Analysis for All Four Studies
14.3.1.3 IPD Analysis with Covariates
14.3.2 Meta-Analysis with SS
14.3.2.1 Generate SS
14.3.2.2 Calculate ES: MD
14.3.2.3 Fixed-Effects and Random-Effects Meta-Analyses
14.4 Summary of Lamotrigine Analysis
14.5 Simulation Study on Continuous Outcomes
14.5.1 Simulation Data Generator
14.5.2 Simulation Data Estimator
14.5.3 Simulation
14.6 Discussions
15 Other R/Stata Packages for Meta-Analysis
15.1 R Packages of meta, rmeta, and metafor
15.2 Combining p-Values in Meta-Analysis
15.3 Combining Correlation Coefficients in Meta-Analysis
15.3.1 R Package metacor
15.3.2 Data on Land-Use Intensity
15.3.3 Meta-Analysis Using metacor Package
15.3.4 Meta-Analysis Using meta Package
15.3.5 Further Discussions on Meta-Analysis for Correlations
15.4 Other R Packages for Meta-Analysis
15.5 Other Stata Methods for Meta-Analysis
Bibliography
Index
