AN EVALUATION OF TREATMENT-COVARIATE INTERACTION IN META-ANALYSIS WITH MARGINALIZING OF MISSING INDIVIDUAL PATIENT DATA

Meta-analysis methods based on individual patient data (IPD) have attracted attention in estimating a treatment-covariate interaction effect. An existing metaregression approach, based on aggregate data (AD) such as a treatment effect estimate and its standard error, is used only for the inference of between-trial interaction which indicates a relationship between the treatment effect estimates and mean covariate values; in contrast, the use of IPD can achieve estimation of not only the between-trial interaction but also within-trial interaction which indicates a relationship between individual outcomes and individual covariate values. However, most of the IPD metaanalyses are often difficult to implement because practitioners cannot always collect the IPD from all trials involved. We propose a new meta-analysis method for estimating both the between-trial and the within-trial interaction, in which we assume an IPD meta-analysis model for the missing IPD and then marginalize its density with respect to the missing IPD. The proposed method allows one to estimate the withintrial interaction even when only AD are available, and has potential benefits for another meta-analytic situation where some trials provide IPD and the others provide only AD. Through simulation studies, we demonstrate how close estimates of the between-trial and the within-trial interaction from the proposed method are to those from the IPD meta-analysis.