Assessing the Impact of Intercurrent Events on Power and Sample Size for Estimands with Time-to-Event Endpoints
Abstract
The precise definition of a primary estimand, accounting for intercurrent events (IEs) as per the ICH E9(R1) addendum, is fundamental to the design and interpretation of clinical trials. Conventional power and sample size calculations, however, often do not adequately incorporate the impact of IEs and their corresponding handling strategies, creating a risk of over- or under-powered studies. While simulation-based approaches can address this complexity, they are often computationally intensive and may only explore a limited set of scenarios. In this paper, we introduce a set of formulae for calculating power for estimands with time-to-event endpoints. We focus on estimands that use treatment policy, hypothetical, composite, or a combination of strategies for handling IEs, assuming constant hazards and that a participant's risk of experiencing an IE is unrelated to their risk of other IEs or the primary endpoint, while the effect of an IE on the endpoint is determined by the chosen handling strategy. Simulation is used to assess the accuracy of calculated power in a variety of settings, and we explore deviations in power estimates in scenarios where event times for outcomes and IEs are dependent, hazards are non-constant, or follow-up times vary. We illustrate the practical application of our approach through a case study in nasal polyposis, examining the sensitivity of sample size requirements to varying IE rates and their impacts on post-IE outcomes. The proposed formulae facilitate rapid and accurate power and assurance calculations, enabling clinical trial designs to be more closely aligned with the estimand of interest.
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BibTeX
@misc{bratton2026assessing,
title = {{Assessing the Impact of Intercurrent Events on Power and Sample Size for Estimands with Time-to-Event Endpoints}},
author = {Daniel J Bratton and Fiona Guillard and Sunita Rehal and Thomas Drury},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2606.03863},
eprint = {2606.03863},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2606.03863},
}
RIS
TY - UNPB
TI - Assessing the Impact of Intercurrent Events on Power and Sample Size for Estimands with Time-to-Event Endpoints
AU - Daniel J Bratton
AU - Fiona Guillard
AU - Sunita Rehal
AU - Thomas Drury
PY - 2026
DA - 2026/10/02
PB - arXiv
DO - 10.48550/arxiv.2606.03863
UR - https://arxiv.org/abs/2606.03863
AB - The precise definition of a primary estimand, accounting for intercurrent events (IEs) as per the ICH E9(R1) addendum, is fundamental to the design and interpretation of clinical trials. Conventional power and sample size calculations, however, often do not adequately incorporate the impact of IEs and their corresponding handling strategies, creating a risk of over- or under-powered studies. While simulation-based approaches can address this complexity, they are often computationally intensive and may only explore a limited set of scenarios. In this paper, we introduce a set of formulae for calculating power for estimands with time-to-event endpoints. We focus on estimands that use treatment policy, hypothetical, composite, or a combination of strategies for handling IEs, assuming constant hazards and that a participant's risk of experiencing an IE is unrelated to their risk of other IEs or the primary endpoint, while the effect of an IE on the endpoint is determined by the chosen handling strategy. Simulation is used to assess the accuracy of calculated power in a variety of settings, and we explore deviations in power estimates in scenarios where event times for outcomes and IEs are dependent, hazards are non-constant, or follow-up times vary. We illustrate the practical application of our approach through a case study in nasal polyposis, examining the sensitivity of sample size requirements to varying IE rates and their impacts on post-IE outcomes. The proposed formulae facilitate rapid and accurate power and assurance calculations, enabling clinical trial designs to be more closely aligned with the estimand of interest.
ER -