Estimands and estimation in trials with time trends

2026-09-30

AI Summary

This study addresses the challenges of estimating causal effects under cross-temporal trends in platform trials, where ambiguous target definitions and biases arising from pooling multi-period data remain unresolved. To tackle these issues, the authors systematically delineate conditional and marginal estimands and comprehensively evaluate the bias-variance trade-offs of model-based approaches, G-computation, and augmented inverse probability weighting (AIPW) estimators under complex temporal dynamics. The work reveals how estimator performance evolves with varying target populations and data selection strategies, thereby clarifying the applicability boundaries of each methodological approach. Ultimately, these findings provide a robust statistical foundation for regulatory decision-making in adaptive platform trial settings.

Abstract

In platform trials, model-based approaches typically estimate treatment effects conditional on calendar time. However, scientific and regulatory interest often lies in treatment effects defined for a target population spanning multiple enrollment periods, requiring explicit consideration of how effects should be averaged across time. This raises two fundamental challenges. The first is the definition of the appropriate target estimand when combining data across multiple periods. The second is the selection of the estimator to be used. In this work, we examine conditional and marginal estimands in platform trials with time trends, and describe target populations of interest. To address the second challenge, we evaluate model-based, G-computation and augmented inverse probability weighting estimators, comparing their bias and variance. We discuss how the choice of estimand, target population and trial data used for estimation affects estimator performance.

Problem

• platform trials
• time trends
• estimands
• treatment effects
• estimation

Innovation

• Platform trials
• Time trends
• Estimands
• G-computation
• Augmented inverse probability weighting