  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 maintaining a control arm
throughout. This control arm comprises both concurrent controls, where
participants are randomized concurrently to either the treatment or control
arm, and non-concurrent controls, who enter the trial when the treatment arm
under study is unavailable. While flexible, platform trials introduce the
challenge of using non-concurrent controls, raising questions about estimating
treatment effects. Specifically, which estimands should be targeted? Under what
assumptions can these estimands be identified and estimated? Are there any
efficiency gains? In this paper, we discuss issues related to the
identification and estimation assumptions of common choices of estimand. We
conclude that the most robust strategy to increase efficiency without imposing
unwarranted assumptions is to target the concurrent average treatment effect
(cATE), the ATE among only concurrent units, using a covariate-adjusted doubly
robust estimator. Our studies suggest that, for the purpose of obtaining
efficiency gains, collecting important prognostic variables is more important
than relying on non-concurrent controls. We also discuss the perils of
targeting ATE due to an untestable extrapolation assumption that will often be
invalid. We provide simulations illustrating our points and an application to
the ACTT platform trial, resulting in a 20% improvement in precision.
