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Doing Meta Analysis with R A Hands On Guide 1st Edition by Mathias Harrer, Pim Cuijpers, Toshi A Furukawa, David D Ebert ISBN 0367610078 9780367610074

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Doing Meta Analysis with R A Hands On Guide 1st Edition Mathias Harrer Digital Instant Download

Author(s): Mathias Harrer, Pim Cuijpers, Toshi A. Furukawa, David D. Ebert
ISBN(s): 9780367610074, 0367610078
Edition: 1
File Details: PDF, 17.74 MB
Year: 2021
Language: english
SKU: EB-33377498 Category: Tags: , , ,

Doing Meta Analysis with R A Hands On Guide 1st Edition by Mathias Harrer, Pim Cuijpers, Toshi A Furukawa, David D Ebert – Ebook PDF Instant Download/Delivery: 0367610078, 9780367610074
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ISBN 10: 0367610078 
ISBN 13: 9780367610074
Author: Mathias Harrer, Pim Cuijpers, Toshi A Furukawa, David D Ebert

Doing Meta-Analysis with R: A Hands-On Guide serves as an accessible introduction on how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered, including calculation and pooling of outcome measures, forest plots, heterogeneity diagnostics, subgroup analyses, meta-regression, methods to control for publication bias, risk of bias assessments and plotting tools. Advanced but highly relevant topics such as network meta-analysis, multi-three-level meta-analyses, Bayesian meta-analysis approaches and SEM meta-analysis are also covered. A companion R package, dmetar, is introduced at the beginning of the guide. It contains data sets and several helper functions for the meta and metafor package used in the guide. The programming and statistical background covered in the book are kept at a non-expert level, making the book widely accessible. Features Contains two introductory chapters on how to set up an R environment and do basic imports/manipulations of meta-analysis data, including exercises Describes statistical concepts clearly and concisely before applying them in R Includes step-by-step guidance through the coding required to perform meta-analyses, and a companion R package for the book

Doing Meta Analysis with R A Hands On Guide 1st Table of contents:

I Getting Started
1 Introduction
1.1 What Are Meta-Analyses?
1.2 “Exercises in Mega-Silliness”: A Historical Anecdote
1.3 Apples and Oranges: A Quick Tour of Meta-Analysis Pitfalls
1.4 Problem Specification, Study Search & Coding
1.4.1 Defining the Research Question
1.4.2 Analysis Plan & Preregistration
1.4.3 Study Search
1.4.4 Study Selection
1.4.5 Data Extraction & Coding
1.5 Questions & Answers
1.6 Summary
2 Discovering R
2.1 Installing R & R Studio
2.2 Packages
2.3 The {dmetar} Package
2.4 Data Preparation & Import
2.5 Data Manipulation
2.5.1 Class Conversion
2.5.2 Data Slicing
2.5.3 Data Transformation
2.5.4 Saving Data
2.6 Questions & Answers
2.7 Summary
II Meta-Analysis in R
3 Effect Sizes
3.1 What Is an Effect Size?
3.2 Measures & Effect Sizes in Single Group Designs
3.2.1 Means
3.2.2 Proportions
3.2.3 Correlations
3.3 Effect Sizes in Control Group Designs
3.3.1 (Standardized) Mean Differences
3.3.2 Risk & Odds Ratios
3.3.3 Incidence Rate Ratios
3.4 Effect Size Correction
3.4.1 Small Sample Bias
3.4.2 Unreliability
3.4.3 Range Restriction
3.5 Common Problems
3.5.1 Different Effect Size Data Formats
3.5.2 The Unit-of-Analysis Problem
3.6 Questions & Answers
3.7 Summary
4 Pooling Effect Sizes
4.1 The Fixed-Effect & Random-Effects Model
4.1.1 The Fixed-Effect Model
4.1.2 The Random-Effects Model
4.2 Effect Size Pooling in R
4.2.1 Pre-Calculated Effect Size Data
4.2.2 (Standardized) Mean Differences
4.2.3 Binary Outcomes
4.2.4 Correlations
4.2.5 Means
4.2.6 Proportions
4.3 Questions & Answers
4.4 Summary
5 Between-Study Heterogeneity
5.1 Measures of Heterogeneity
5.1.1 Cochran’s Q
5.1.2 Higgins & Thompson’s I2 Statistic
5.1.3 The H2 Statistic
5.1.4 Heterogeneity Variance τ2 & Standard Deviation τ
5.2 Which Measure Should I Use?
5.3 Assessing Heterogeneity in R
5.4 Outliers & Influential Cases
5.4.1 Basic Outlier Removal
5.4.2 Influence Analysis
5.4.3 GOSH Plot Analysis
5.5 Questions & Answers
5.6 Summary
6 Forest Plots
6.1 What Is a Forest Plot?
6.2 Forest Plots in R
6.2.1 Layout Types
6.2.2 Saving the Forest Plots
6.3 Drapery Plots
6.4 Questions & Answers
6.5 Summary
7 Subgroup Analyses
7.1 The Fixed-Effects (Plural) Model
7.1.1 Pooling the Effect in Subgroups
7.1.2 Comparing the Subgroup Effects
7.2 Limitations & Pitfalls of Subgroup Analyses
7.3 Subgroup Analysis in R
7.4 Questions & Answers
7.5 Summary
8 Meta-Regression
8.1 The Meta-Regression Model
8.1.1 Meta-Regression with a Categorical Predictor
8.1.2 Meta-Regression with a Continuous Predictor
8.1.3 Assessing the Model Fit
8.2 Meta-Regression in R
8.3 Multiple Meta-Regression
8.3.1 Interactions
8.3.2 Common Pitfalls in Multiple Meta-Regression
8.3.3 Multiple Meta-Regression in R
8.4 Questions & Answers
8.5 Summary
9 Publication Bias
9.1 What Is Publication Bias?
9.2 Addressing Publication Bias in Meta-Analyses
9.2.1 Small-Study Effect Methods
9.2.2 P-Curve
9.2.3 Selection Models
9.3 Which Method Should I Use?
9.4 Questions & Answers
9.5 Summary
III Advanced Methods
10 “Multilevel” Meta-Analysis
10.1 The Multilevel Nature of Meta-Analysis
10.2 Fitting Three-Level Meta-Analysis Models in R
10.2.1 Model Fitting
10.2.2 Distribution of Variance across Levels
10.2.3 Comparing Models
10.3 Subgroup Analyses in Three-Level Models
10.4 Questions & Answers
10.5 Summary
11 Structural Equation Modeling Meta-Analysis
11.1 What Is Meta-Analytic Structural Equation Modeling?
11.1.1 Model Specification
11.1.2 Meta-Analysis from a SEM Perspective
11.1.3 The Two-Stage Meta-Analytic SEM Approach
11.2 Multivariate Meta-Analysis
11.2.1 Specifying the Model
11.2.2 Evaluating the Results
11.2.3 Visualizing the Results
11.3 Confirmatory Factor Analysis
11.3.1 Data Preparation
11.3.2 Model Specification
11.3.3 Model Fitting
11.3.4 Path Diagrams
11.4 Questions & Answers
11.5 Summary
12 Network Meta-Analysis
12.1 What Are Network Meta-Analyses?
12.1.1 Direct & Indirect Evidence
12.1.2 Transitivity & Consistency
12.1.3 Network Meta-Analysis Models
12.2 Frequentist Network Meta-Analysis
12.2.1 The Graph Theoretical Model
12.2.2 Frequentist Network Meta-Analysis in R
12.3 Bayesian Network Meta-Analysis
12.3.1 Bayesian Inference
12.3.2 The Bayesian Network Meta-Analysis Model
12.3.3 Bayesian Network Meta-Analysis in R
12.3.4 Network Meta-Regression
12.4 Questions & Answers
12.5 Summary
13 Bayesian Meta-Analysis
13.1 The Bayesian Hierarchical Model
13.2 Setting Prior Distributions
13.3 Bayesian Meta-Analysis in R
13.3.1 Fitting the Model
13.3.2 Assessing Convergence
13.3.3 Interpreting the Results
13.3.4 Generating a Forest Plot
13.4 Questions & Answers
13.5 Summary
IV Helpful Tools
14 Power Analysis
14.1 Fixed-Effect Model
14.2 Random-Effects Model
14.3 Subgroup Analyses
15 Risk of Bias Plots
15.1 Data Preparation
15.2 Summary Plots
15.3 Traffic Light Plots
16 Reporting & Reproducibility
16.1 Using R Projects
16.2 Writing Reproducible Reports with R Markdown
16.3 OSF Repositories
16.3.1 Access Token
16.3.2 The {osfr} Package & Authentication
16.3.3 Repository Setup
16.3.4 Upload & Download
16.3.5 Collaboration, Open Access & Pre-Registration
17 Effect Size Calculation & Conversion
17.1 Mean & Standard Error
17.2 Regression Coefficients
17.3 Correlations
17.4 One-Way ANOVAs
17.5 Two-Sample t-Tests
17.6 p-Values
17.7 χ2 Tests
17.8 Number Needed to Treat

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Tags: Mathias Harrer, Pim Cuijpers, Toshi A Furukawa, David D Ebert, Meta Analysis