Bayesian dose finding in oncology for drug combinations by copula regression
Bayesian dose finding in oncology for drug combinations by copula regression
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DOI：
10.1111/j.1467-9876.2009.00649.x
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发表时间：
2009-01-01
影响因子：
1.6
通讯作者：
Yuan, Ying
中科院分区：
文献类型：
--
作者：
Yin, Guosheng;Yuan, Ying
在癌症临床试验中，用多种药物组合治疗患者变得越来越普遍，生物化学协同作用通常是主要焦点。在典型的药物联合试验中，每种药物的毒性特征已经在单药试验中进行了彻底的研究，这自然提供了丰富的先验信息。我们提出了一个贝叶斯自适应设计的剂量发现，是基于一个Copula型模型，以考虑两个或两个以上的药物组合的协同效应。为了寻找最大耐受剂量组合，我们不断更新组合剂量毒性概率的后验估计。通过在二维概率空间中重新排序剂量毒性，我们自适应地将每个新的患者队列分配到最合适的剂量。通过比较联合剂量的毒性概率的后验估计值和预先规定的毒性目标来确定剂量递增、递减或保持相同剂量。我们进行了广泛的模拟研究，以检查设计的操作特性，并说明在各种实际情况下所提出的方法。
Treating patients with a combination of agents is becoming commonplace in cancer clinical trials, with biochemical synergism often the primary focus. In a typical drug combination trial, the toxicity profile of each individual drug has already been thoroughly studied in single-agent trials, which naturally offers rich prior information. We propose a Bayesian adaptive design for dose finding that is based on a copula-type model to account for the synergistic effect of two or more drugs in combination. To search for the maximum tolerated dose combination, we continuously update the posterior estimates for the toxicity probabilities of the combined doses. By reordering the dose toxicities in the two-dimensional probability space, we adaptively assign each new cohort of patients to the most appropriate dose. Dose escalation, de-escalation or staying at the same doses is determined by comparing the posterior estimates of the probabilities of toxicity of combined doses and the prespecified toxicity target. We conduct extensive simulation studies to examine the operating characteristics of the design and illustrate the proposed method under various practical scenarios.