Adaptive clinical trial design with delayed treatment effects using elicited prior distributions
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结合专家先验的延迟治疗效应自适应临床试验设计
- 研究问题
- 在生存终点试验中，治疗效应延迟出现可能破坏比例风险假设并导致过早终止，如何在设计中处理延迟的不确定性？
- 方法与思路
- 作者将专家 elicitation 得到的延迟治疗效应先验分布纳入自适应试验框架，用示例展示先验如何指导中期分析与早停。
- 材料陈述的贡献
- 摘要称该框架有助于降低过早终止风险、提高试验效率与治疗获益检出能力，并提供免费开源软件支持设计与决策。
- 阅读关注点
- 应核对专家先验的获取与校准、延迟效应的参数化、中期停止规则，以及错误率、功效和先验敏感性的评估方式。
- 阅读边界
- 这是arXiv预印本，同行评审或期刊接收未经核实。摘要仅描述框架与示例，未给出量化性能、软件地址或适用条件。
原文摘要
Clinical trials with time-to-event endpoints, such as overall survival (OS) or progression-free survival (PFS), are fundamental for evaluating new treatments, particularly in immuno-oncology. However, modern therapies, such as immunotherapies and targeted treatments, often exhibit delayed effects that challenge traditional trial designs. These delayed effects violate the proportional hazards assumption, which underpins standard statistical methods like the Cox proportional hazards model and the log-rank test. Careful planning is essential to ensure trials are appropriately designed to account for the timing and magnitude of these effects. Without this planning, interim analyses may lead to premature trial termination if the treatment effect is underestimated early in the study. We present an adaptive trial design framework that incorporates prior distributions, elicited from experts, for delayed treatment effects. By addressing the uncertainty surrounding delayed treatment effects, our approach enhances trial efficiency and robustness, minimizing the risk of premature termination and improving the detection of treatment benefits over time. We present an example illustrating how interim analyses, informed by prior distributions, can guide early stopping decisions. To facilitate the implementation of our framework, we have developed free, open-source software that enables researchers to integrate prior distributions into trial planning and decision-making. This software provides a flexible, accessible tool for designing trials that more accurately evaluate modern therapies through adaptive trial designs.