AN ADAPTIVE BAYESIAN APPROACH TO JOINTLY MODELING RESPONSE AND TOXICITY IN PHAS

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 我们提出了一种新的自适应贝叶斯方法用于I期临床试验中的剂量发现，该方法基于响应和 假设反应阈值和毒性阈值联合服从双变量对数正态分布的毒性 分发。在癌症试验中，反应很少见。但生物反应可能是常见的，并可能有助于决定 第一阶段的升级应该是多么激进。在理想的决策理论框架下，每种药物的剂量选择 后续患者将把对患者最好的信息与信息的价值结合在一起 为审判而获得的。然而，显式地评估后者在计算上将是极其困难的。为 简而言之，我们将注意力仅限于下一位患者的每种结局的概率。该模型假设 反应和毒性事件的发生取决于个人各自的剂量阈值，并提供了 纳入关于人口阈值分布以及累积的先验信息的框架 数据。下一次剂量可以分配到最大的预期效用。一个简单的效用函数放入正效用 只有在反应和无毒共存的情况下。计算机仿真结果表明，所提出的设计方案 在不同的场景和不同的先例下可靠地选择首选剂量。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. We present a new adaptive Bayesian method for dose-finding in phase I clinical trials based on both response and toxicity under the assumption that the thresholds of response and toxicity jointly follow a bivariate log-normal distribution. Responses are rare in cancer trials. But biological responses may be common, and may help decide how aggressive a phase I escalation should be. In an ideal decision theory framework, the choice of dose for each successive patient would incorporate what is best for the patient, together with the value of the information to be obtained for the trial. However, to evaluate the latter explicitly would be computationally extremely difficult. For simplicity, we restrict attention to the probability of each outcome for the next patient only. The model assumes that response and toxicity events happen depending on the respective dose thresholds for the individual, and provides a framework for incorporating prior information about the population threshold distribution, as well as accumulated data. The next dose can be assigned to maximize expected utility. A simple utility function places positive utility only on the co-occurrence of response and non-toxicity. Computer simulation results show that the proposed design reliably chooses the preferred dose under different scenarios and different priors.