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Estimands and Estimation Strategies for Platform Trials with Time Trends

"In platform trials, model-based approaches have been proposed to mitigate bias from time trends. However, these methods typically condition on time, yielding treatment effect estimates specific to particular calendar periods. While such conditional estimates are unbiased for their respective time-specific estimands, investigators typically seek an unconditional treatment effect - one that does not depend on calendar time and reflects the effect in the overall trial population. This raises fundamental questions: when combining data from multiple periods, what is the appropriate target estimand? What defines the overall trial population if arms continuously enter and leave? Which estimator should be used?
In this work, we examine both conditional and marginal estimands in platform trials with time trends, clarifying the target populations of inferential interest. We evaluate model-based approaches, G-computation and augmented inverse probability weighting estimators, comparing their properties, including bias and variance. We discuss how the choice of estimand and the target population, as well as which trial data to use for estimation, affects the performance of estimators."