gBOIN: a unified model-assisted phase I trial design accounting for toxicity grades, and binary or continuous end points
gBOIN: a unified model-assisted phase I trial design accounting for toxicity grades, and binary or continuous end points
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DOI：
10.1111/rssc.12263
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发表时间：
2019-02-01
影响因子：
1.6
通讯作者：
Yin, Jun
中科院分区：
文献类型：
--
作者：
Mu, Rongji;Yuan, Ying;Yin, Jun
随着分子靶向药物和免疫疗法的出现，肿瘤药物开发的前景最近发生了变化。这些新的治疗药物似乎更可能诱导多种低或中度毒性，而不是剂量限制性毒性。已经提出了各种基于模型的剂量发现设计和毒性严重程度评分系统来解释毒性等级，但由于使用复杂的剂量-毒性模型以及在每次剂量递增和递减决策时需要重新拟合模型，因此难以实施。我们提出了一个广义贝叶斯最优区间设计，gBOIN，可容纳各种现有的毒性等级评分系统在一个统一的框架下。作为一种模型辅助设计，gBOIN基于指数分布族推导其最优决策规则，但与基于算法的设计一样以简单的方式进行：其剂量递增和递减的决策仅涉及终点的样本均值与两个预先指定的剂量递增和递减边界的简单比较。不需要模型拟合。我们表明，gBOIN具有理想的有限性质的一致性和大样本性质的一致性。数值研究表明，gBOIN产生良好的性能，是可比的或上级的一些现有的，更复杂的基于模型的设计。可以从免费获得用于实现gBOIN的Web应用程序。
The landscape of oncology drug development has recently changed with the emergence of molecularly targeted agents and immunotherapies. These new therapeutic agents appear more likely to induce multiple low or moderate grade toxicities rather than dose limiting toxicities. Various model-based dose finding designs and toxicity severity scoring systems have been proposed to account for toxicity grades, but they are difficult to implement because of the use of complicated dose-toxicity models and the requirement to refit the model at each decision of dose escalation and de-escalation. We propose a generalized Bayesian optimal interval design, gBOIN, that accommodates various existing toxicity grade scoring systems under a unified framework. As a model-assisted design, gBOIN derives its optimal decision rule on the basis of the exponential family of distributions but is carried out in a simple way as the algorithm-based design: its decision of dose escalation and de-escalation involves only a simple comparison of the sample mean of the end point with two prespecified dose escalation and de-escalation boundaries. No model fitting is needed. We show that gBOIN has the desirable finite property of coherence and a large sample property of consistency. Numerical studies show that gBOIN yields good performance that is comparable with or superior to that of some existing, more complicated model-based designs. A Web application for implementing gBOIN is freely available from .