Identification and estimation of causal effects using non-concurrent controls in platform trials
arxiv(2024)
摘要
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 a unique
challenge with the use of non-concurrent controls, raising questions about how
to efficiently utilize their data to estimate treatment effects. Specifically,
what estimands should be used to evaluate the causal effect of a treatment
versus control? Under what assumptions can these estimands be identified and
estimated? Do we achieve any efficiency gains? In this paper, 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 outcome regression, inverse probability weighting, and doubly
robust estimators for their estimation. We discuss efficiency gains,
demonstrate their performance in a simulation study, and apply them to the ACTT
platform trial, resulting in a 20
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