BAP: Bayesian Biomarker-Assisted Platform Design for Dose Ranging in Multi-Agent Multi-Dose Trials
BAP：多药物多剂量试验的贝叶斯生物标志物辅助平台设计AI 解读 ↓
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BAP：多药物多剂量试验的贝叶斯生物标志物辅助平台设计
- 研究问题
- 如何在同一平台试验中评价多种药物及其剂量，并利用纵向生物标志物及时判断尚未观测的长期疗效？
- 方法与思路
- 作者提出多阶段随机对照的贝叶斯平台设计，用纵向标志物估计暂缺的长期反应，结合期中决策、停止监测和剂量优化。
- 材料陈述的贡献
- 摘要称，该框架整合概念验证、无效性与优效性监测及剂量优化；经参数校准后，模拟表现优于若干常规平台设计。
- 阅读关注点
- 应核对标志物与长期反应的模型、共享对照分配规则、期中决策阈值，以及频率学性质校准和模拟比较的具体场景。
- 阅读边界
- 仅据 OpenAlex 索引摘要，稿件版本未注明；摘要未给出模型假设、错误率或成本数值，模拟比较不足以支持普遍优越性。
原文摘要
Assessing the long-term benefits of new treatments can be expensive and time-consuming, particularly in disease areas with unmet medical needs. While platform trials enable the evaluation of multiple interventions simultaneously, they currently cannot assess studies involving multiple agents and doses or utilize longitudinal biomarkers in decision-making. We propose a Bayesian biomarker-assisted platform design that offers a unified framework for evaluating multiple investigational agents and their doses in a multi-stage, randomized controlled trial. The design streamlines the drug evaluation process and decreases development costs by including proof-of-concept, futility and superiority monitoring, and dose optimization in a single trial, while avoiding over-allocating patients to a shared placebo or active control arm. To facilitate making real-time interim group sequential decisions, temporarily unobserved long-term responses are estimated from longitudinal biomarker measurements. Design parameters and the maximum sample size are fine-tuned to achieve good frequentist properties. The proposed design is illustrated by a trial of three targeted agents for systemic lupus erythematosus, evaluated by their 24-week response rates. Extensive simulations show that the proposed design compares favorably to several conventional platform designs.