1. From Estimands to Robust Inference of Treatment Effects in Platform Trials
- Author
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Qian, Yuhan, Yi, Yifan, Shao, Jun, Yi, Yanyao, Levin, Gregory, Mayer-Hamblett, Nicole, Heagerty, Patrick J., and Ye, Ting
- Subjects
Statistics - Methodology - Abstract
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 defining an appropriate target population for the estimand, as the populations represented by some commonly used analysis approaches can arbitrarily depend on the randomization ratio or trial type. For the first time, this article presents a clear framework for constructing a clinically meaningful estimand with precise specificity regarding the population of interest. The proposed entire concurrently eligible (ECE) population not only preserves the integrity of randomized comparisons but also remains invariant to both the randomization ratio and trial type. This lays the groundwork for future design, analysis, and research of 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 applied to the SIMPLIFY trial, using the R package RobinCID.
- Published
- 2024