The item is a book hardback or cased book
The Author Name is Riley, Richard D.
The Title is Individual Participant Data Meta-Analysis
Condition New
Other Comments
Pages Count - 562. Binding type - Hardcover.
Content Language - English
Category - MEDICAL / Evidence-Based Medicine
Product Description -
Individual Participant Data Meta-Analysis: A Handbook for Healthcare Research 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. 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. Intended for a broad audience, the book will enable the reader to: Understand the advantages of the IPD approach and decide when it is needed over a conventional systematic reviewRecognise the scope, resources and challenges of IPD meta-analysis projectsAppreciate the importance of a multi-disciplinary project team and close collaboration with the original study investigatorsUnderstand how to obtain, check, manage and harmonise IPD from multiple studiesExamine risk of bias quality of IPD and minimise potential biases throughout the projectUnderstand fundamental statistical methods for IPD meta-analysis, including two-stage and one-stage approaches and their differences, and statistical software to implement themClearly report and disseminate IPD meta-analyses to inform policy, practice and future researchCritically appraise existing IPD meta-analysis projectsAddress 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 Detailed examples and case studies are provided throughout.
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