
Individual Participant Data Meta-Analysis by Richard D Riley
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 review
- Recognise the scope, resources and challenges of IPD meta-analysis projects
- Appreciate the importance of a multi-disciplinary project team and close collaboration with the original study investigators
- Understand how to obtain, check, manage and harmonise IPD from multiple studies
- Examine risk of bias (quality) of IPD and minimise potential biases throughout the project
- Understand fundamental statistical methods for IPD meta-analysis, including two-stage and one-stage approaches (and their differences), and statistical software to implement them
- Clearly report and disseminate IPD meta-analyses to inform policy, practice and future research
- Critically appraise existing IPD meta-analysis projects
- 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
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Multiple Imputation and its Application
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Data Monitoring Committees in Clinical Trials
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Bayesian Approaches to Clinical Trials and Health-Care Evaluation
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Risk Assessment
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Evidence Synthesis for Decision Making in Healthcare
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Modern Analysis of Customer Surveys
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Statistical Practice in Business and Industry
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Spatio-temporal Design
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Applied Missing Data Analysis in the Health Sciences
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Statistical Methods for Hospital Monitoring with R
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Statistical Methods for Trend Detection and Analysis in the Environmental Sciences
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Statistical Estimation of Epidemiological Risk
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Statistical Monitoring of Complex Multivariate Processes
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A Practical Guide to Designing Phase II Trials in Oncology
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Quantitative Finance
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Statistical Applications for Environmental Analysis and Risk Assessment
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Statistical Methods for Dose-Finding Experiments
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Geostatistics for Environmental Scientists
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Selection Bias and Covariate Imbalances in Randomized Clinical Trials
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Spatial Analysis Along Networks
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Using Statistical Methods for Water Quality Management
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Weight-of-Evidence for Forensic DNA Profiles
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Statistical and Methodological Aspects of Oral Health Research
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Statistics and the Evaluation of Evidence for Forensic Scientists
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Modeling and Analysis of Compositional Data
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Statistical DNA Forensics
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Analysing Survival Data from Clinical Trials and Observational Studies
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Statistical Analysis of Microstructures in Materials Science
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Understanding Biostatistics
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Nondetects and Data Analysis
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Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science
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Maximum Likelihood Estimation and Inference
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Applied Mixed Models in Medicine
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How to Design, Analyse and Report Cluster Randomised Trials in Medicine and Health Related Research
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Statistical Issues in Drug Development
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Missing Data in Clinical Studies
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Comparing Clinical Measurement Methods
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Statistical Methods for Groundwater Monitoring
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The False Discovery Rate
Richard D. Riley is Professor of Biostatistics in the School of Medicine, Keele University, UK.
Jayne F. Tierney is Professor of Evidence Synthesis at the MRC Clinical Trials Unit, University College London, UK.
Lesley A. Stewart is Professor of Evidence Synthesis and Director of the Centre for Reviews and Dissemination, University of York, UK.
| SKU | Unavailable |
| ISBN 13 | 9781119333722 |
| ISBN 10 | 1119333725 |
| Title | Individual Participant Data Meta-Analysis |
| Author | Richard D Riley |
| Series | Statistics In Practice |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | John Wiley & Sons Inc |
| Year published | 2021-06-17 |
| Number of pages | 560 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |






































