Dose-finding based on multiple toxicities in a soft tissue sarcoma trial
Dose-finding based on multiple toxicities in a soft tissue sarcoma trial
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DOI：
10.1198/016214504000000043
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发表时间：
2004-03-01
影响因子：
3.7
通讯作者：
Thall, PF
中科院分区：
文献类型：
--
作者：
Bekele, BN;Thall, PF
一种新的化疗药物一期肿瘤试验的科学目标是找到一个具有可接受毒性水平的剂量。出于伦理原因，剂量发现是自适应进行的，根据从先前队列获得的数据为连续队列的患者选择剂量。一般。患者面临着几种性质不同的毒性的风险，每种毒性都有几种可能的严重程度。在这篇文章中，我们描述了我们如何在吉西他滨治疗软组织肉瘤的一期试验中解决剂量发现问题。计划试验的肿瘤学家想要解释他们已经确定的毒性在重要性上的差异。他们还要求剂量测定方法利用在给定剂量下观察到的低级别毒性这一事实，尽管不是剂量限制。提供一种警告，表明在较高剂量下可能发生较高级别的毒性。常规的第一阶段方法将每种毒性降低为其发生在被认为是剂量限制的严重程度或以上的一个指标，将“毒性”定义为这些指标中的最大值，并根据该单一二元变量确定基本剂量。由于传统方法不能解决上述问题，我们开发了一种贝叶斯方法，用于在肉瘤试验中发现剂量，该方法基于相关的、序数毒性的矢量，其严重程度随剂量而变化。我们还开发了一种方法来共同引出先验，权重向量量化每种类型毒性的每个级别的临床重要性，以及医生可接受的目标总毒性负担。我们的方法为每个队列分配最接近目标的当前后验平均总毒性负担的剂量。启发过程是迭代的，肿瘤学家反复展示算法的行为，并要求调整他们的权重，以确保统计决策反映适当的临床行为。我们描述了这种方法是如何在肉瘤试验中工作的，在几种临床情况下对试验进行了模拟和敏感性分析，并提供了一般应用的指导方针。
The scientific goal of a phase I oncology trial of a new chemotherapeutic agent is to find a dose with an acceptable level of toxicity. For ethical reasons, dose-finding is done adaptively, with doses chosen for successive cohorts of patients based on the data obtained from previous cohorts. Typically. patients are at risk for several qualitatively different toxicities, each occurring at several possible severity levels. In this article, we describe how we addressed the dose-finding problem in a phase I trial of gemcitabine for treatment of soft tissue sarcoma. The oncologists planning the trial wanted to account for differences in importance among the toxicities that they had identified. They also requested that the dose-finding method utilize the fact that a low-grade toxicity observed at a given dose, although not dose-limiting. provides a warning that a higher grade of that toxicity is likely to occur at a higher dose. Conventional phase I methods reduce each type of toxicity to an indicator of its occurrence at or above a severity level considered dose-limiting, define "toxicity" as the maximum of these indicators, and base dose-finding on that single binary variable. Because conventional methods do not address the aforementioned concerns, we developed a Bayesian method for dose-finding in the sarcoma trial based on a vector of correlated, ordinal-valued toxicities with severity levels varying with dose. We also developed a method for jointly eliciting the prior, a vector of weights quantifying the clinical importance of each level of each type of toxicity, and a target total toxicity burden acceptable to the physicians. Our method assigns each cohort the dose with a current posterior mean total toxicity burden closest to the target. The elicitation process is iterative, with the oncologists repeatedly shown the algorithm's behavior and asked to adjust their weights to ensure that the statistical decisions reflect appropriate clinical behavior. We describe how this methodology has worked in the sarcoma trial, present simulations and sensitivity analyses of the trial under several clinical scenarios, and provide guidelines for general application.