A Bayesian adaptive enrichment design using aggregate historical data to inform individualized treatment recommendations
利用历史汇总数据支持个体化治疗建议的贝叶斯自适应富集设计AI 解读 ↓
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利用历史汇总数据支持个体化治疗建议的贝叶斯自适应富集设计
- 研究问题
- 当历史研究只有平均治疗效应等汇总信息，无法识别亚组治疗参数时，如何借用这些信息支持自适应招募与个体化治疗？
- 方法与思路
- 作者以一个或多个汇总指标为锚点构造归一化幂先验，再用后验概率决定疗效或无效早停，以及向有希望的亚组继续招募。
- 材料陈述的贡献
- 摘要称，模拟中该设计相较不借用历史信息的设计提高功效、促进早停并降低预期样本量，并以睡眠呼吸暂停试验为动机。
- 阅读关注点
- 应核对汇总效应如何进入先验、历史偏差如何影响信息借用，以及亚组效应的识别来源、停止规则和错误率控制。
- 阅读边界
- 这是arXiv预印本，同行评审或期刊接收未经核实。摘要未提供具体增益与识别条件，其方法首创性声明也尚未核验。
原文摘要
Adaptive enrichment trials aim to identify and recruit participants most likely to benefit from treatment based on evolving biomarker evidence, with the goal of informing individualized treatment recommendations. Bayesian methods are well suited to these designs because they allow external information to be incorporated in a principled manner. In practice, prior studies often provide only summary-level information, with subgroup-specific estimates unavailable due to design or privacy constraints. Existing dynamic borrowing approaches therefore rely on aggregate measures, such as the average treatment effect, and implicitly assume that historical information maps directly onto model parameters. In adaptive enrichment settings aimed at identifying individualized treatment effects, however, subgroup-specific treatment parameters are not identifiable when only marginal historical effects are available. To address this gap, we propose a Bayesian adaptive enrichment design that borrows information from external studies using a normalized power prior anchored on one or more summary measures, such as the average treatment effect. { To our knowledge, no existing method addresses this gap.} Interim analyses use posterior probabilities to guide early stopping for efficacy or futility, or to continue recruitment within promising biomarker-defined subgroups. Simulation studies evaluate operating characteristics across historical bias, sample size, and prior informativeness. Together with a motivating future trial in obstructive sleep apnea, the results show efficiency gains versus non-borrowing designs, including improved power, earlier stopping, and reduced expected sample size.