<p><i>Individual Participant Data Meta-Analysis: A Handbook for Healthcare Research </i>provides a comprehensive introduction to the fundamental principles and methods that healthcare researchers need when considering, conducting or using individual participant data (IPD) meta-analysis projects. Written and edited by researchers with substantial experience in the field, the book details key concepts and practical guidance for each stage of an IPD meta-analysis project, alongside illustrated examples and summary learning points.</p> <p>Split into five parts, the book chapters take the reader through the journey from initiating and planning IPD projects to obtaining, checking, and meta-analysing IPD, and appraising and reporting findings. The book initially focuses on the synthesis of IPD from randomised trials to evaluate treatment effects, including the evaluation of participant-level effect modifiers (treatment-covariate interactions). Detailed extension is then made to specialist topics such as diagnostic test accuracy, prognostic factors, risk prediction models, and advanced statistical topics such as multivariate and network meta-analysis, power calculations, and missing data.</p> <p>Intended for a broad audience, the book will enable the reader to:</p> <ul> <li>Understand the advantages of the IPD approach and decide when it is needed over a conventional systematic review</li> <li>Recognise the scope, resources and challenges of IPD meta-analysis projects</li> <li>Appreciate the importance of a multi-disciplinary project team and close collaboration with the original study investigators</li> <li>Understand how to obtain, check, manage and harmonise IPD from multiple studies</li> <li>Examine risk of bias (quality) of IPD and minimise potential biases throughout the project</li> <li>Understand fundamental statistical methods for IPD meta-analysis, including two-stage and one-stage approaches (and their differences), and statistical software to implement them</li> <li>Clearly report and disseminate IPD meta-analyses to inform policy, practice and future research</li> <li>Critically appraise existing IPD meta-analysis projects</li> <li>Address specialist topics such as effect modification, multiple correlated outcomes, multiple treatment comparisons, non-linear relationships, test accuracy at multiple thresholds, multiple imputation, and developing and validating clinical prediction models</li> </ul> Detailed examples and case studies are provided throughout.
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