BAYESIAN DATA AUGMENTATION DOSE FINDING WITH CONTINUAL REASSESSMENT METHOD AND DELAYED TOXICITY.
BAYESIAN DATA AUGMENTATION DOSE FINDING WITH CONTINUAL REASSESSMENT METHOD AND DELAYED TOXICITY.
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DOI：
10.1214/13-aoas661
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发表时间：
2013-12-01
期刊：
影响因子：
--
通讯作者：
Yuan Y
中科院分区：
文献类型：
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作者：
Liu S;Yin G;Yuan Y
实施适应性剂量探索设计时的一个主要实际障碍是，在开始治疗后不久可能无法观察到决策规则所使用的毒性结果。为了解决这个问题，我们提出了用于剂量查找的数据增强连续重新评估方法（DA-CRM）。通过自然地将未观察到的毒性视为缺失数据，我们表明这种缺失数据是不可忽略的，因为缺失取决于未观察到的结果。贝叶斯数据增强方法用于从后验完整条件分布中对缺失数据和模型参数进行采样。我们通过广泛的模拟研究评估 DA-CRM 的性能，并将其与其他现有方法进行比较。结果表明，所提出的设计令人满意地解决了与迟发毒性相关的问题，并具有理想的操作特性：更安全地治疗患者，并以更高的概率选择最大耐受剂量。新的 DA-CRM 通过两项 I 期癌症临床试验进行了说明。
A major practical impediment when implementing adaptive dose-finding designs is that the toxicity outcome used by the decision rules may not be observed shortly after the initiation of the treatment. To address this issue, we propose the data augmentation continual re-assessment method (DA-CRM) for dose finding. By naturally treating the unobserved toxicities as missing data, we show that such missing data are nonignorable in the sense that the missingness depends on the unobserved outcomes. The Bayesian data augmentation approach is used to sample both the missing data and model parameters from their posterior full conditional distributions. We evaluate the performance of the DA-CRM through extensive simulation studies, and also compare it with other existing methods. The results show that the proposed design satisfactorily resolves the issues related to late-onset toxicities and possesses desirable operating characteristics: treating patients more safely, and also selecting the maximum tolerated dose with a higher probability. The new DA-CRM is illustrated with two phase I cancer clinical trials.