Full Description
Meta-analysis is the statistical combination of previously conducted studies, often from summary statistics but sometimes with individual participant data. It is widespread in life sciences and is gaining popularity in economics and beyond. In many real-life meta-analyses, challenges in the source information, such as unreported statistics or biases, can be incorporated using Bayesian methods. Bayesian Meta-Analysis: A Practical Introduction provides an approachable introduction for researchers who are new to Bayes, meta-analysis, or both. There is an emphasis on hands-on learning using a variety of software packages.
Key Features
Introductory chapters assume no prior experience or mathematical training, and are aimed at non-statistical researchers
Examples of basic meta-analyses in seven different software alternatives: BUGS, JAGS, Stan, bayesmeta, brms, Stata, and JASP
Practical advice on extracting information from studies, eliciting expert opinions, managing project decisions, and writing up findings
Discussion of specific problems, including publication bias, unreported statistics, and a mixture of study designs, with code examples
Accompanying online blog and forum, with all code and data from the book, plus more translations to different software
This book aims to bridge the gap between the researcher who wants to carry out tailored meta-analysis and the techniques they need, which have previously been available only in mathematically or computationally demanding publications.
Contents
1. A statistical inference primer. 2. What is Bayesian statistics?. 3. Common effect meta-analysis. 4. Random effects meta-analysis and heterogeneity. 5. How to extract statistics from published papers. 6. Eliciting priors. 7. Writing up your meta-analysis. 8. Using arm- and time-based statistics. 9. Network meta-analysis. 10. Individual participant data. 11. Unreported statistics. 12. Living systematic reviews and Bayesian updating. 13. Publication bias. 14. Multiple statistics. 15. Multiple outcomes or study designs. 16. Informing policy and economic evaluation. 17. Emerging topics in Bayesian meta-analysis.
