Individual participant data meta‐analysis to examine interactions between treatment effect and participant‐level covariates: Statistical recommendations for conduct and planning

Precision medicine research often searches for treatment‐covariate interactions, which refers to when a treatment effect (eg, measured as a mean difference, odds ratio, hazard ratio) changes across values of a participant‐level covariate (eg, age, gender, biomarker). Single trials do not usually hav...

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Published in:Statistics in medicine Vol. 39; no. 15; pp. 2115 - 2137
Main Authors: Riley, Richard D., Debray, Thomas P.A., Fisher, David, Hattle, Miriam, Marlin, Nadine, Hoogland, Jeroen, Gueyffier, Francois, Staessen, Jan A., Wang, Jiguang, Moons, Karel G.M., Reitsma, Johannes B., Ensor, Joie
Format: Journal Article
Language:English
Published: Hoboken, USA John Wiley & Sons, Inc 10.07.2020
Wiley Subscription Services, Inc
Subjects:
Clinical trials
Data Analysis
effect modifier
Humans
individual participant data (IPD)
Medical statistics
Meta-analysis
Meta-Analysis as Topic
Models, Statistical
Precision medicine
Proportional Hazards Models
subgroup effect
treatment‐covariate interaction
Tutorial in Biostatistics
individual participant data (IPD)
meta-analysis
treatment-covariate interaction
subgroup effect
effect modifier
ISSN:0277-6715, 1097-0258, 1097-0258
Online Access:Get full text
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Summary:Precision medicine research often searches for treatment‐covariate interactions, which refers to when a treatment effect (eg, measured as a mean difference, odds ratio, hazard ratio) changes across values of a participant‐level covariate (eg, age, gender, biomarker). Single trials do not usually have sufficient power to detect genuine treatment‐covariate interactions, which motivate the sharing of individual participant data (IPD) from multiple trials for meta‐analysis. Here, we provide statistical recommendations for conducting and planning an IPD meta‐analysis of randomized trials to examine treatment‐covariate interactions. For conduct, two‐stage and one‐stage statistical models are described, and we recommend: (i) interactions should be estimated directly, and not by calculating differences in meta‐analysis results for subgroups; (ii) interaction estimates should be based solely on within‐study information; (iii) continuous covariates and outcomes should be analyzed on their continuous scale; (iv) nonlinear relationships should be examined for continuous covariates, using a multivariate meta‐analysis of the trend (eg, using restricted cubic spline functions); and (v) translation of interactions into clinical practice is nontrivial, requiring individualized treatment effect prediction. For planning, we describe first why the decision to initiate an IPD meta‐analysis project should not be based on between‐study heterogeneity in the overall treatment effect; and second, how to calculate the power of a potential IPD meta‐analysis project in advance of IPD collection, conditional on characteristics (eg, number of participants, standard deviation of covariates) of the trials (potentially) promising their IPD. Real IPD meta‐analysis projects are used for illustration throughout.
Bibliography:Funding information
NIHR Health Technology Assessment Programme, programme 12/01; National Institute for Health Research (NIHR) Clinical Trials Unit Support Funding, TOP grant of the Netherlands Organisation for Health Research and Development (ZonMw), 91215058; NIHR Doctoral Fellowship, DRF‐2018‐11‐ST2‐077; Netherlands Organisation for Health Research and Development, Keele University
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Funding information NIHR Health Technology Assessment Programme, programme 12/01; National Institute for Health Research (NIHR) Clinical Trials Unit Support Funding, TOP grant of the Netherlands Organisation for Health Research and Development (ZonMw), 91215058; NIHR Doctoral Fellowship, DRF‐2018‐11‐ST2‐077; Netherlands Organisation for Health Research and Development, Keele University
ISSN:0277-6715
1097-0258
1097-0258
DOI:10.1002/sim.8516