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一种用于癌症I期试验的贝叶斯自适应设计,采用灵活的剂量范围。

A Bayesian adaptive design for cancer phase I trials using a flexible range of doses.

作者信息

Tighiouart Mourad, Cook-Wiens Galen, Rogatko André

机构信息

a Biostatistics and Bioinformatics Research Center, Cedars-Sinai Medical Center , Los Angeles , CA , USA.

出版信息

J Biopharm Stat. 2018;28(3):562-574. doi: 10.1080/10543406.2017.1372774. Epub 2017 Oct 6.

DOI:10.1080/10543406.2017.1372774
PMID:28858566
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5832533/
Abstract

We present a Bayesian adaptive design for dose finding in cancer phase I clinical trials. The goal is to estimate the maximum tolerated dose (MTD) after possible modification of the dose range during the trial. Parametric models are used to describe the relationship between the dose and the probability of dose-limiting toxicity (DLT). We investigate model reparameterization in terms of the probabilities of DLT at the minimum and maximum available doses at the start of the trial. Trial design proceeds using escalation with overdose control (EWOC), where at each stage of the trial we seek the dose of the agent such that the posterior probability of exceeding the MTD of this agent is bounded by a feasibility bound. At any time during the trial, we test whether the MTD is below or above the minimum and maximum doses, respectively. If during the trial there is evidence that the MTD is outside the range of doses, we extend the range of doses and complete the trial with the planned sample size. At the end of the trial, a Bayes estimate of the MTD is proposed. We evaluate design operating characteristics in terms of safety of the trial design and efficiency of the MTD estimate under various scenarios and model misspecification. The methodology is further compared to the original EWOC design. We showed by comprehensive simulation studies that the proposed method is safe and can estimate the MTD more efficiently than the original EWOC design.

摘要

我们提出了一种用于癌症I期临床试验剂量探索的贝叶斯自适应设计。目标是在试验期间对剂量范围进行可能的修改后,估计最大耐受剂量(MTD)。使用参数模型来描述剂量与剂量限制毒性(DLT)概率之间的关系。我们根据试验开始时最小和最大可用剂量下的DLT概率来研究模型重新参数化。试验设计采用过量控制下的剂量递增(EWOC)方法进行,即在试验的每个阶段,我们寻找药物的剂量,使得超过该药物MTD的后验概率受可行性界限的限制。在试验期间的任何时候,我们分别测试MTD是低于还是高于最小和最大剂量。如果在试验期间有证据表明MTD超出了剂量范围,我们扩大剂量范围并按计划样本量完成试验。在试验结束时,提出MTD的贝叶斯估计值。我们根据试验设计的安全性和在各种情况下以及模型错误设定下MTD估计的效率来评估设计操作特征。该方法进一步与原始的EWOC设计进行比较。我们通过全面的模拟研究表明,所提出的方法是安全的,并且比原始的EWOC设计能更有效地估计MTD。

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