Primary Submission Category: Randomized Studies
Identification and estimation of causal effects using non-concurrent controls in platform trials
Authors: Michele Santacatterina, Federico Macchiavelli, Xinyi Zhang, Ivan Diaz,
Presenting Author: Michele Santacatterina*
Platform trials are multi-arm designs that simultaneously evaluate multiple treatments for a single disease within the same overall trial structure. Unlike traditional randomized controlled trials, they allow treatment arms to enter and exit the trial at distinct times while employing a shared control arm. In platform trials, concurrent controls join alongside new treatments, while non-concurrent controls are already enrolled, potentially introducing time-related biases. The central question revolves around the effective utilization of non-concurrent controls to estimate treatment effects in platform trials. Specifically, what estimands should be used to evaluate the causal effect of a treatment versus a shared control? Under what assumptions can these estimands be identified and estimated? Do we achieve any efficiency gains? In this project, we use structural causal models and counterfactuals to clarify estimands and formalize their identification in the presence of non-concurrent controls in platform trials. We also provide estimators based on outcome regression, inverse probability weighting, and doubly robust estimators for their estimation. Additionally, we discuss efficiency gains, demonstrate their performance in a simulation study, and apply them using data from the Adaptive COVID-19 Treatment Trial (ACTT) platform trial.