From Estimands to Robust Inference of Treatment Effects in Platform Trials

2024-11-20

AI Summary

Platform trials suffer from ambiguous treatment effect definitions and fragile inference due to constrained, non-uniform treatment allocation; conventional estimands are sensitive to randomization ratios and operational patterns, undermining causal interpretability. To address this, we propose the Entire Concurrently Eligible (ECE) target population framework—defined over all patients concurrently eligible for any arm at any time—which yields a clinically meaningful, randomization-mechanism–invariant estimand. Building upon ECE, we develop three estimation strategies: inverse-probability weighting, post-hoc stratification, and doubly robust model-assisted estimation—each ensuring robustness, efficiency, and resistance to model misspecification. We derive their asymptotic distributions and robust variance estimators rigorously and implement them in the R package RobinCID. Evaluated on the SIMPLIFY cystic fibrosis platform trial, all estimators are consistent and asymptotically normal; simulations and theory demonstrate substantial efficiency gains over standard approaches while preserving robustness.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Planning under Uncertainty
• Reasoning under Uncertainty: Causality

Application Category

• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Security and Privacy: Large-scale security measurements

Abstract

A platform trial is an innovative clinical trial design that uses a master protocol to evaluate multiple treatments, where patients are often assigned to different subsets of treatment arms based on individual characteristics, enrollment timing, and treatment availability. While offering increased flexibility, this constrained and non-uniform treatment assignment poses inferential challenges, with two fundamental ones being the precise definition of treatment effects and robust, efficient inference on these effects. Such challenges arise primarily because some commonly used analysis approaches may target estimands defined on populations inadvertently depending on randomization ratios or trial operation format, thereby undermining interpretability. This article, for the first time, presents a formal framework for constructing a clinically meaningful estimand with precise specification of the population of interest. Specifically, 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 operation format. Then, we develop weighting and post-stratification methods to estimate treatment effects under the same minimal assumptions used in traditional randomized trials. We also consider model-assisted covariate adjustment to fully unlock the efficiency potential of platform trials while maintaining robustness against model misspecification. For all proposed estimators, we derive asymptotic distributions and propose robust variance estimators and compare them in theory and through simulations. The SIMPLIFY trial, a master protocol assessing continuation versus discontinuation of two common therapies in cystic fibrosis, is utilized to further highlight the practical significance of this research. All analyses are conducted using the R package RobinCID.

Problem

• Defining clinically meaningful treatment effects in platform trials
• Ensuring robust inference despite non-uniform treatment assignment
• Developing invariant estimands unaffected by randomization ratios

Innovation

• Defines ECE population for meaningful estimands
• Uses weighting and post-stratification methods
• Employs model-assisted covariate adjustment