AI 中文总结
本研究针对早期临床试验贝叶斯双变量“通过/不通过”决策，提出校准先验设定框架，明确κ为先验主导参数，可控制先验间通过差异并应用于狼疮试验，为自适应先验精度方法提供依据。
AI 中文摘要
贝叶斯“通过/不通过（Go/No-Go）”决策需在正态-逆威沙特（Normal-Inverse-Wishart）框架下为共同主要终点指定先验分布，但关于先验超参数如何影响试验决策的指导仍有限。我们提出一种用于双变量“通过/不通过”决策的校准先验设定框架：对怀疑型（skeptical）和热情型（enthusiastic）先验进行校准，使每种先验为临床相关决策区域分配目标概率。我们证明，对于任意先验精度κ>0，存在唯一的尺度参数λ₀可实现目标校准。通过模拟评估不同κ下的操作特征，并将其应用于3期他利西普（telitacicept）狼疮试验。模拟结果显示，κ是先验区分度的主要驱动因素：κ=1时，先验间的通过（go）率差异为0.07；κ=10时，该差异达0.56，且假阳性率低于0.01。操作特征对自由度参数ν₀和先验相关系数ρ₀具有鲁棒性，支持默认ν₀=2。在狼疮试验应用中，κ=1时先验敏感性可忽略，但κ=10时，小样本下热情型先验的通过率为怀疑型先验的三倍。该框架将先验设定简化为两个选择：先验中心和先验精度κ；将κ确定为主导参数，结合试验前对κ的谨慎选择，为自适应先验精度方法提供了动机。
英文摘要
Bayesian Go/No-Go decisions with co-primary endpoints require specifying prior distributions under the Normal-Inverse-Wishart framework; however guidance on how prior hyperparameters influence trial decisions remains limited. We propose a calibrated prior specification framework for bivariate Go/No-Go decisions. Skeptical and enthusiastic priors are calibrated so that each assigns a target probability to a clinically relevant decision region. We prove that for any prior precision $κ> 0$, a unique scale parameter $λ_0$ achieves the target calibration. Operating characteristics are evaluated across different $κ$ via simulation and applied to a phase~3 telitacicept lupus trial.The simulation result indicates $κ$ is the primary driver of prior discrimination. At $κ= 1$, the go rate difference between priors was 0.07; at $κ= 10$ it reached 0.56, with false positive rates below 0.01. Operating characteristics were robust to the degrees of freedom parameter $ν_0$ and prior correlation $ρ_0$, supporting a default of $ν_0 = 2$. In the lupus application, prior sensitivity was negligible at $κ= 1$ but at $κ= 10$ the enthusiastic go rate was three times the skeptical rate at small sample sizes. The framework reduces prior specification to two choices: the prior center and the prior precision $κ$. The identification of $κ$ as the dominant parameter, together with the cautious choice of $κ$ before the trial, motivates adaptive approaches to prior precision.
