|
Abstract:
|
Multi-arm platform trials investigate multiple agents simultaneously, typically with staggered entry and exit of experimental treatment arms versus a shared control arm. In such settings, there is considerable debate whether to limit analyses for a treatment arm to concurrently randomized control subjects, or to allow comparisons to both concurrent and non-concurrent (pooled) control subjects. The potential bias from temporal drift over time is at the core of this debate. We propose time-adjusted analyses, including a “Bayesian Time Machine”, to model potential temporal drift in the entire study population, such that primary analyses can incorporate all randomized control subjects from the platform trial. We conduct a simulation study that shows the Bayesian Time Machine to provide estimates with generally greater precision and smaller mean square error than alternative approaches, at the risk of small bias and Type I error inflation. The Bayesian Time Machine provides a compromise between bias and precision by smoothing estimates across time and leveraging all available data for the estimation of treatment effects.
|
