Free Download Bayesian Meta-Analysis
by Grant, Robert;Di Tanna, Gian Luca;, Di Tanna, Gian Luca
English | 2025 | ISBN: 1032451890 | 328 pages | True EPUB | 9.59 MB
"...this book is extremely timely...not just a technical exposition, but provides practical guidance about using different software platforms, as well as valuable advice about extracting summary statistics, eliciting prior information, communicating results, visualisation, and many other issues...reflects years of thoughtful experience, and should be of huge value to anyone faced with pooling studies into a coherent whole." ~From the Foreword by Professor Sir David Spiegelhalter 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.
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