From Estimands to Robust Inference of Treatment Effects in Platform Trials

A platform trial is an innovative clinical trial design that uses a master protocol (i.e., one overarching protocol) to evaluate multiple treatments in an ongoing manner and can accelerate the evaluation of new treatments. However, its flexibility introduces inferential challenges, with two fundamental ones being the precise definition of treatment effects and robust, efficient inference on these effects. Central to these challenges is the definition of an appropriate target population for the estimand, as some commonly used populations can be unexpectedly problematic. This article, for the first time, presents a clear framework for constructing a clinically meaningful estimand with precise specificity regarding the population of interest. The proposed estimand defines the treatment effect as a contrast of expected outcomes between two treatments within the entire concurrently eligible (ECE) population - the largest population that preserves the integrity of randomization - establishing a foundation for future research in platform trials. Then, we develop weighting and post-stratification methods for estimation of treatment effects with minimal assumptions. To fully leverage the efficiency potential of platform trials, we also consider a model-assisted approach for baseline covariate adjustment to gain efficiency while maintaining robustness against model misspecification. We derive and compare asymptotic distributions of proposed estimators in theory and propose robust variance estimators. The proposed estimators are empirically evaluated in a simulation study and illustrated in the SIMPLIFY trial, using the R package RobinCID.