"The authors begin by setting out the principles of statistical inference, simple models for meta-analysis and code for fitting them in a Bayesian framework. One of the great strengths of the book is that code is provided for performing Bayesian meta-analysis in multiple software packages, enabling beginners to get started easily or helping more experienced users to try out alternative Bayesian software. The second part of the book covers extraction of summary statistics, elicitation of opinions to inform priors and communication of methods and findings. In the third part, the authors present a range of models for more complex settings such as network meta-analysis, IPD meta-analysis, selection models for publication bias, multiple outcomes and handling missing summary statistics, again with code provided for implementation. This book will be a valuable resource for anyone wanting to learn more about the potential and practicalities of Bayesian meta-analysis, from students to experienced researchers." ~Becky Turner, University College London, MRC Clinical Trials Unit
In our field of study on congenital anomalies, where rare diseases and small numbers of events in studies are the rule, the book can be particularly helpful. Frequentist (agnostic) methods of evidence synthesis can present improtant limits in our field and Bayesian methods can be more efficient and informative as one can draw on the wealth of population-based data available on baseline prevalence of anomalies. Hence, one can find reasonable prior distributions with plausible bounds and gain more insights about the question at hand using Bayeisan meta-analysis as delineated in the book." ~Babak KHOSHNOOD, Université Paris Cité
this book is extremely timelynot 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 issuesreflects 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
an excellent new textbook on Bayesian methods in meta-analysisThe book is written for users who are familiar with traditional frequentist meta-analysis methods but are interested in Bayesian alternatives and software for fitting meta-analysis modelsthe authors have made a deliberate decision to focus on mastery rather than mathematicsReaders can find more detail on theory and mathematics in the numerous other texts that provide this, and in the meantime, they will be able to learn how to ft, interpret, and present results from Bayesian meta-analysisThe language is clear and the code-rich examples are valuable for learning. Furthermore, the helpful opinions, insights, and advice for conducting analyses, interpreting results, and making results useful for policy and decision making, based on the authors depth of expertise and experience, will help assure that users make appropriate modeling decisions. Grant and di Tanna have succeeded in producing a practical introduction to a complex topic that will allow users to be able to conduct Bayesian meta-analysis with confidence. ~ Christopher S. Hollenbeak, Pennsylvania State University, USA, in PharmacoEconomics, June 2025
