AI 中文总结
针对贝叶斯动态借用依赖参数化结局模型易误设的问题，提出NP-LEAP非参数潜在可交换先验框架，可进行个体层面可交换性评估，经模拟和实际案例验证其性能优于相关方法。
AI 中文摘要
贝叶斯动态借用（BDB）方法利用历史数据来降低治疗效应的不确定性，但现有方法依赖易受误设影响的参数化结局模型。我们提出非参数潜在可交换先验（NP-LEAP），这是一种与结局无关、假设 lean 的框架，用于从历史数据中借用信息。NP-LEAP 执行个体层面的可交换性评估，对历史数据的所有可能划分（分为可交换与不可交换子集）进行贝叶斯模型平均。尽管通过选择合适的核函数，NP-LEAP 适用于多种数据类型，但它特别适合具有时间-事件结局的研究，因为参数化 BDB 可能存在三重误设：施加参数化基线风险、比例风险结构及笼统可交换性。我们在温和正则条件下建立后验一致性。模拟研究表明，与参数化借用方法及非借用半参数频率论方法相比，NP-LEAP 具有良好的操作特性。我们通过在一项非小细胞肺癌患者随机试验中扩充对照组来阐释该方法。
英文摘要
Bayesian dynamic borrowing (BDB) methods leverage historical data to reduce treatment effect uncertainty, yet existing approaches rely on parametric outcome models susceptible to misspecification. We propose the nonparametric latent exchangeability prior (NP-LEAP), an outcome-agnostic, assumption-lean framework to borrow information from historical data. The NP-LEAP performs individual-level exchangeability assessment, inducing Bayesian model averaging over all possible partitions of the historical data into exchangeable and nonexchangeable subsets. Although applicable to a variety of data types with choice of appropriate kernel, the NP-LEAP is particularly well-suited for studies with time-to-event outcomes, where parametric BDB is potentially triply misspecified - imposing a parametric baseline hazard, the proportional hazards structure, and blanket exchangeability. We establish posterior consistency under mild regularity conditions. Simulation studies demonstrate favorable operating characteristics relative to parametric borrowing methods and nonborrowing semiparametric frequentist methods. We illustrate the method by augmenting the control arm in a randomized trial of patients with non-small cell lung cancer.
Comments31 pages, 5 figures
