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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.
“…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 ""This is an excellent text for anyone who wants to fully understand and use Bayesian methods for meta-analysis. Organised into three parts, it starts with a gentle introduction to Bayesian statistics and meta-analysis which are then expanded to more advanced topics, highlighting the situations where a Bayesian approach is particularly useful. Technical concepts are introduced gently and as needed, making the material easy to follow. Clearly defined learning objectives for each Chapter help the reader decide whether they are ready, or need, to delve into the material provided which makes the book easy to navigate. Code and an introduction to different Bayesian software are provided to allow the reader to explore the examples and get started with fitting their own meta-analysis models. If you want to learn about Bayesian methods for meta-analysis, this is where to start."" ~Sofia Dias, Professor in Health Technology Assessment CRD, University of York
Medical Product Safety Evaluation...
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