Estimands and estimation in trials with time trends
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.
Related
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BibTeX
@misc{roig2026estimands,
title = {{Estimands and estimation in trials with time trends}},
author = {Marta Bofill Roig and Ekkehard Glimm and Kelly Van Lancker and Martin Posch},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.39742},
eprint = {2609.39742},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.39742},
}
RIS
TY - UNPB
TI - Estimands and estimation in trials with time trends
AU - Marta Bofill Roig
AU - Ekkehard Glimm
AU - Kelly Van Lancker
AU - Martin Posch
PY - 2026
DA - 2026/09/30
PB - arXiv
DO - 10.48550/arxiv.2609.39742
UR - https://arxiv.org/abs/2609.39742
AB - 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.
ER -