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利用外部数据的贝叶斯单臂与混合对照临床试验设计：修正与扩展AI 解读

Limited patient availability complicates sample size determination in pediatric clinical trials. Although Bayesian methods incorporating external data offer a solution, rigorously controlling the type I error rate remains difficult. Psioda and Ibrahim (2019) proposed a simulation-based framework as a practical solution. However, although their framework was designed to relax the type I error control, this relaxation fails when the external data exhibit a large treatment…

贝叶斯动态借用方法中多历史对照信息借用的后验量化：范围综述AI 解读

Bayesian dynamic borrowing methods incorporate historical control data into current clinical trial analyses while allowing the degree of borrowing to depend on the compatibility between historical and current data. Although many methods have been proposed, the degree of borrowing is often difficult to interpret, especially when multiple historical control sources are available. This scoping review focuses on posterior quantification of borrowing from multiple historical…

Cox 比例风险模型中未测混杂的非参数贝叶斯调整AI 解读

In observational studies, unmeasured confounders present a crucial challenge in accurately estimating desired causal effects. To calculate the hazard ratio (HR) in Cox proportional hazard models for time-to-event outcomes, two-stage residual inclusion and limited information maximum likelihood are typically employed. However, these methods are known to entail difficulty in terms of potential bias of HR estimates and parameter identification. This study introduces a novel…