Robert Grant, Gian Luca Di Tanna

Bayesian Meta-Analysis (eBook, PDF)

A Practical Introduction

• Format: PDF

Produktbeschreibung

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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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 328
• Erscheinungstermin: 27. Juni 2025
• Englisch
• ISBN-13: 9781040357965
• Artikelnr.: 74334038

Autorenporträt

Robert Grant is a statistician who has worked throughout his career with evidence synthesis and Bayesian models. He is one of the developers of Stan software, and a chartered fellow of the Royal Statistical Society. He worked on health service quality indicators and clinical guidelines for the Royal College of Physicians and the National Institute for Health and Care Excellence from 1998-2010, then on epidemiological and health services research, and teaching for health care professionals around statistics and research methods, at St George's, University of London and Kingston University from 2010-2017. He provided freelance coaching, training and consultancy to clients from various sectors from 2017-2024.

Gian Luca Di Tanna is a biostatistician and health economist who has focused his career on applied statistical methodologies for randomized clinical trials and observational research, particularly Bayesian methods and evidence synthesis/meta-analysis.

He has held academic positions at Sapienza University of Rome, the University of Birmingham, the London School of Hygiene and Tropical Medicine, and Queen Mary University of London. He worked at the George Institute for Global Health at the University of New South Wales, Australia, where he served as Head of the Biostatistics Division and co-Head of the Meta-Research and Evidence Synthesis Unit.

From 2020 to 2022, he chaired the Statistical Methods for Health Economics and Outcomes Research Special Interest Group of the International Society for Pharmacoeconomics and Outcomes Research (ISPOR). He contributes as a Statistical Editor to Cochrane groups and serves on the editorial boards of PharmacoEconomics and BMC Medical Research Methodology. He was listed among the World's Top 2% Scientists in both the 2023 and 2024 rankings published by Stanford University and Clarivate Analytics. He is a Chartered Statistician of the Royal Statistical Society.

He is currently a Full Professor of Biostatistics and Health Economics and Head of Research and Services at the Department of Business Economics, Health, and Social Care at the University of Applied Sciences and Arts of Southern Switzerland (SUPSI). Additionally, he is a member of the Academic Board of the Swiss School of Public Health.

Inhaltsangabe

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.