Individual Participant Data Meta-Analysis

A Handbook for Healthcare Research

Richard D. Riley, Jayne F. Tierney, Lesley A. Stewart, Richard D. Riley, Jayne F. Tierney, Lesley A. Stewart

• Publisher: Wiley
• Year: 2021
• Print ISBN:
  9781119333722
• eBook ISBN:
  9781119333753
• Edition: 1
• Topic: Medicine
• Subtopic: Medical Theory, Practice & Reference

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

Detailed examples and case studies are provided throughout.

1
Individual Participant Data Meta‐Analysis for Healthcare Research

Richard D. Riley, Lesley A. Stewart, and Jayne F. Tierney

1.1 Introduction

Healthcare and clinical decision‐making should be guided by the evidence arising from high‐quality research studies. Often a single study is insufficient to make firm recommendations, and so multiple studies are conducted to address the same research question. This motivates the need for evidence synthesis: the combination of data from multiple studies to provide an overall summary of current knowledge. For example, when multiple randomised trials have examined the effect of a particular treatment, evidence syntheses are needed to combine and summarise the information from these trials, in order to establish whether the treatment is effective or not.

Systematic reviews are the cornerstone of evidence synthesis and evidence‐based decision‐making in healthcare. They use transparent methods to identify, appraise and combine a body of research evidence, with the goal of producing summary results that guide best practices for stakeholders including patients, clinicians, health professionals, and policy‐makers. Systematic review methodology has been championed by organisations such as Cochrane, who publish systematic reviews in the Cochrane Library summarising the effects of interventions,¹ the accuracy of diagnostic tests,² the prognostic effect of particular factors,³ and the performance of risk prediction models.⁴ Most systematic reviews include a meta‐analysis,⁵ which is a statistical technique for combining (synthesising) quantitative data obtained from multiple research studies. Traditionally, most meta‐analyses have used aggregate data extracted from study publications, but there is growing demand for meta‐analyses that utilise individual participant data (IPD).^6–9

This book is intended as a comprehensive handbook for healthcare researchers undertaking IPD meta‐analysis projects. In this introductory chapter, we clarify differences between IPD and aggregate data, and outline why IPD meta‐analysis projects are increasingly needed. Then, we detail the scope of our book and its intended audience, and signpost where to find material in subsequent chapters.

1.2 What Is IPD and How Does It Differ from Aggregate Data?

IPD refers to the raw information recorded for each participant in a research study (e.g. a randomised trial), such as baseline characteristics, prognostic factors, treatments received, outcomes and follow‐up details, and can be represented by a dataset containing a separate row per participant and columns containing values for each participant‐level variable. For example, IPD for a randomised trial of anti‐hypertensive treatment will usually include the pre‐ and post‐treatment blood pressure level, a treatment group indicator, important clinical characteristics and prognostic factors recorded at baseline (such as age, sex, BMI and comorbidities), and relevant follow‐up information (such as time to cardiovascular disease or death). An IPD meta‐analysis project, theref...

Table of contents

• Cover
• Table of Contents
• Series Page
• Title Page
• Copyright Page
• Dedication Page
• Acknowledgements
• 1 Individual Participant Data Meta‐Analysis for Healthcare Research
• Part I: Rationale, Planning, and Conduct
• Part I: References
• Part II: Fundamental Statistical Methods and Principles
• Part II: References
• Part III: Critical Appraisal and Dissemination
• Part III: References
• Part IV: Special Topics in Statistics
• Part IV: References
• Part V: Diagnosis, Prognosis and Prediction
• Part V: References
• Index
• End User License Agreement