A Bayesian phase I‐II dose‐finding design is presented for a clinical trial with four coprimary outcomes that reflect the actual clinical observation process. During a prespecified fixed follow‐up period, the times to disease progression, toxicity, and death are monitored continuously, and an ordinal disease status variable, including progressive disease (PD) as one level, is evaluated repeatedly by scheduled imaging. We assume a proportional hazards model with piecewise constant baseline hazard for each continuous variable and a longitudinal multinomial probit model for the ordinal disease status process and include multivariate patient frailties to induce association among the outcomes. A finite partition of the nonfatal outcome combinations during the follow‐up period is constructed, and the utility of each set in the partition is elicited. Posterior mean utility is used to optimize each patient's dose, subject to a safety rule excluding doses with an unacceptably high rate of PD, severe toxicity, or death. A simulation study shows that, compared with the proposed design, a simpler design based on commonly used efficacy and toxicity outcomes obtained by combining the four variables described above performs poorly and has substantially smaller probabilities of correctly choosing truly optimal doses and excluding truly unsafe doses.

中文翻译：

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基于定期和连续监测疾病状态以及毒性和死亡时间的 I-II 期设计。

贝叶斯 I-II 期剂量发现设计用于临床试验，具有反映实际临床观察过程的四个共同主要结果。在预先指定的固定随访期内，持续监测疾病进展、毒性和死亡的时间，并通过预定成像重复评估有序疾病状态变量，包括将疾病进展 (PD) 作为一个级别。我们假设一个比例风险模型，每个连续变量具有分段常数基线风险，并为序数疾病状态过程假设一个纵向多项概率模型，并包括多变量患者虚弱以诱导结果之间的关联。构建了随访期间非致命结果组合的有限分区，并引出了分区中每个集合的效用。后平均效用用于优化每位患者的剂量，遵守安全规则，排除具有不可接受的高 PD、严重毒性或死亡率的剂量。一项模拟研究表明，与提议的设计相比，基于常用功效和毒性结果的更简单设计，通过组合上述四个变量而表现不佳，并且正确选择真正最佳剂量和排除真正不安全剂量的概率要小得多。