Abstract
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.
Highlights
Precision medicine and stratified healthcare tailors treatment decisions to individuals based on their particular characteristics.1,2 The goal is to optimize treatment decisions and reduce unnecessary costs for each individual, by selecting treatments most likely to benefit them based on their participant-level covariate values
In the individual participant data (IPD) meta-analysis conducted by the International Weight Management in Pregnancy (i-WIP) collaboration introduced in Section 2.2, a primary objective was to examine a potential interaction between baseline body mass index (BMI) and intervention effect on gestational weight gain
There are multiple reasons for pursuing the collection and synthesis of IPD, 70,71 but personalized medicine is driving many IPD meta-analyses to search for treatment-covariate interactions
Summary
Precision medicine and stratified healthcare tailors treatment decisions to individuals based on their particular characteristics. The goal is to optimize treatment decisions and reduce unnecessary costs for each individual, by selecting treatments most likely to benefit (or least likely to harm) them based on their participant-level covariate values. When individual participant data (IPD) from multiple randomized trials are available, meta-analysis provides the opportunity to increase power to detect true treatment-covariate interactions.. Over the last decade we have been involved in a number of IPD meta-analysis projects aiming to examine treatment-covariate interactions at the participant-level, and learnt important lessons and pitfalls from a statistical point of view. In this tutorial article, we share our experience to help other researchers in this situation, building on previous work in both IPD meta-analysis and single study settings..
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