Evaluating Efficiency of Platform Trials Under Delayed Outcomes Using Treatment Throughput
Abstract
Background: Platform trials improve efficiency by enabling early stopping of ineffective arms, shared controls, and addition of new treatments without compromising statistical properties. However, delays in observing primary outcomes may reduce these benefits. Investigators must either pause recruitment, delaying identification of effective treatments, or continue recruitment, potentially enrolling participants who cannot benefit from interim decisions. We assess the impact of outcome delay on platform trial efficiency using a novel metric. Methods: We propose Expected Throughput (ET), defined as the expected number of treatment arms evaluated per 1000 participants. ET accounts for participants recruited while awaiting outcomes. We consider a multi-stage sequential design for each treatment arm and assess ET across different outcome delays, assuming uniform patient accrual. Results: Outcome delays substantially reduce platform trial efficiency, with the impact dependent on recruitment rate and delay length. At a recruitment rate of 10 patients per month, severe losses occur when delays exceed 12 months. When outcome delay exceeds two-thirds of the time required to recruit a single arm, substantially fewer treatment arms are evaluated than anticipated. Conclusion: Outcome delay can markedly reduce platform trial efficiency. Recruitment rates and expected outcome delays should therefore be considered during trial planning.
Related
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BibTeX
@misc{mukherjee2026evaluating,
title = {{Evaluating Efficiency of Platform Trials Under Delayed Outcomes Using Treatment Throughput}},
author = {Aritra Mukherjee and James M. S. Wason},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.08560},
eprint = {2609.08560},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.08560},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Evaluating Efficiency of Platform Trials Under Delayed Outcomes Using Treatment Throughput
AU - Aritra Mukherjee
AU - James M. S. Wason
PY - 2026
DA - 2026/09/08
PB - arXiv
DO - 10.48550/arxiv.2609.08560
UR - https://arxiv.org/abs/2609.08560
AB - Background: Platform trials improve efficiency by enabling early stopping of ineffective arms, shared controls, and addition of new treatments without compromising statistical properties. However, delays in observing primary outcomes may reduce these benefits. Investigators must either pause recruitment, delaying identification of effective treatments, or continue recruitment, potentially enrolling participants who cannot benefit from interim decisions. We assess the impact of outcome delay on platform trial efficiency using a novel metric. Methods: We propose Expected Throughput (ET), defined as the expected number of treatment arms evaluated per 1000 participants. ET accounts for participants recruited while awaiting outcomes. We consider a multi-stage sequential design for each treatment arm and assess ET across different outcome delays, assuming uniform patient accrual. Results: Outcome delays substantially reduce platform trial efficiency, with the impact dependent on recruitment rate and delay length. At a recruitment rate of 10 patients per month, severe losses occur when delays exceed 12 months. When outcome delay exceeds two-thirds of the time required to recruit a single arm, substantially fewer treatment arms are evaluated than anticipated. Conclusion: Outcome delay can markedly reduce platform trial efficiency. Recruitment rates and expected outcome delays should therefore be considered during trial planning.
ER -