Comparison of Methods to Generalize Randomized Clinical Trial Results without Individual-Level Data for the Target Population

Abstract

Our study explored the application of methods to generalize randomized controlled trial results to a target population without individual-level data. We compared four methods using aggregate data for the target population to generalize the JUPITER trial results to a target population of trial-eligible patients in Clinical Practice Research Datalink (CPRD). The gold-standard method used individual data from both the trial and CPRD to predict probabilities of being sampled in the trial and reweight trial participants to reflect CPRD patient characteristics. Methods 1 and 2 used weighting methods based on simulated individual data or the method of moments, respectively. Method 3 weighted the trial subgroup-specific treatment effects to match the distribution of an effect modifier in CPRD. Method 4 calculated the expected absolute benefits in CPRD assuming homogeneous relative treatment effect. Methods based on aggregate data for the target population generally yielded results between the trial and gold-standard estimates. Methods 1 and 2 yielded estimates closest to the gold-standard when continuous effect modifiers were represented as categorical variables. Although individual data or data on joint distributions remains the best approach to generalize the trial results, these methods using aggregate data may be useful tools for timely assessment of randomized trial generalizability.