Using individual participant data to improve network meta-analysis projects

• Published in: BMJ evidence-based medicine Vol. 28; no. 3; pp. 197 - 203
• Main Authors: Riley, Richard D, Dias, Sofia, Donegan, Sarah, Tierney, Jayne F, Stewart, Lesley A, Efthimiou, Orestis, Phillippo, David M
• Format: Journal Article
• Language: English
• Published: England BMJ Publishing Group Ltd 01.06.2023; BMJ Publishing Group LTD; BMJ Publishing Group
• Subjects: Bias; Clinical outcomes; Clinical trials; Data collection; Evidence-based medicine; evidence-based practice; Humans; Medical prognosis; Medical research; Meta-analysis; Meta-Analysis as Topic; Network Meta-Analysis; Patients; policy; Research Methods and Reporting; Validity; evidence-based practice; policy
• ISSN: 2515-446X; 2515-4478; 2515-4478
• DOI: 10.1136/bmjebm-2022-111931

A network meta-analysis combines the evidence from existing randomised trials about the comparative efficacy of multiple treatments. It allows direct and indirect evidence about each comparison to be included in the same analysis, and provides a coherent framework to compare and rank treatments. A traditional network meta-analysis uses aggregate data (eg, treatment effect estimates and standard errors) obtained from publications or trial investigators. An alternative approach is to obtain, check, harmonise and meta-analyse the individual participant data (IPD) from each trial. In this article, we describe potential advantages of IPD for network meta-analysis projects, emphasising five key benefits: (1) improving the quality and scope of information available for inclusion in the meta-analysis, (2) examining and plotting distributions of covariates across trials (eg, for potential effect modifiers), (3) standardising and improving the analysis of each trial, (4) adjusting for prognostic factors to allow a network meta-analysis of conditional treatment effects and (5) including treatment–covariate interactions (effect modifiers) to allow relative treatment effects to vary by participant-level covariate values (eg, age, baseline depression score). A running theme of all these benefits is that they help examine and reduce heterogeneity (differences in the true treatment effect between trials) and inconsistency (differences in the true treatment effect between direct and indirect evidence) in the network. As a consequence, an IPD network meta-analysis has the potential for more precise, reliable and informative results for clinical practice and even allows treatment comparisons to be made for individual patients and targeted populations conditional on their particular characteristics.