延迟结局下使用治疗吞吐量评估平台试验的效率

中文摘要 AI 辅助

背景：平台试验通过允许无效治疗组的早期终止、共享对照组以及在不损害统计性质的情况下添加新治疗来提高效率。然而，观察主要结局的延迟可能会降低这些益处。研究者必须要么暂停招募，从而延迟有效治疗的识别，要么继续招募，可能纳入无法从期中决策中获益的参与者。我们使用一种新的指标来评估结局延迟对平台试验效率的影响。方法：我们提出了预期吞吐量（ET），定义为每1000名参与者中评估的治疗组预期数量。ET考虑了在等待结局期间招募的参与者。我们为每个治疗组考虑了一个多阶段序贯设计，并在假设患者均匀入组的情况下，评估了不同结局延迟下的ET。结果：结局延迟显著降低了平台试验的效率，其影响取决于招募率和延迟长度。在每月10名患者的招募率下，当延迟超过12个月时会发生严重损失。当结局延迟超过招募单个治疗组所需时间的三分之二时，评估的治疗组数量将大幅少于预期。结论：结局延迟可能显著降低平台试验的效率。因此，在试验规划期间应考虑招募率和预期结局延迟。

英文摘要

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.

发表机构

• Population Health Sciences Institute, Newcastle University（纽卡斯尔大学人口健康科学研究所）

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