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 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