Department of Surgery, University of Texas Southwestern Medical Center, Dallas, Texas.
Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern, Dallas, Texas.
Cancer. 2018 Aug;124(16):3339-3345. doi: 10.1002/cncr.31577. Epub 2018 Jul 5.
Phase I cancer trials increasingly incorporate dose-expansion cohorts (DECs), reflecting a growing demand to acquire more information about investigational drugs. Protocols commonly fail to provide a sample-size justification or analysis plan for the DEC. In this study, we develop a statistical framework for the design of DECs.
We assume the maximum tolerated dose (MTD) for the investigational drug has been identified in the dose-escalation stage of the trial. We use the 80% lower confidence bound and the 90% upper confidence bound for the response and toxicity rates, respectively, as decision thresholds for the dose-expansion stage. We calculate the operating characteristics with reference to prespecified minimum effective response rates and maximum safe DLT rates.
We apply our framework to specify a system of DEC plans. The design comprises three components: 1) the number of subjects enrolled at the MTD, 2) the minimum number of responses necessary to indicate provisional drug efficacy, and 3) the maximum number of dose-limiting toxicities (DLTs) permitted to indicate drug safety. We demonstrate our method in an application to a cancer immunotherapy trial.
Our simple and practical tool enables creation of DEC designs that appropriately address the safety and efficacy objectives of the trial.
越来越多的 I 期癌症试验纳入剂量扩展队列(DEC),反映出人们对获取更多关于研究药物信息的需求不断增长。但协议通常未能为 DEC 提供样本量的论证或分析计划。在这项研究中,我们开发了一种用于 DEC 设计的统计框架。
我们假设试验的剂量递增阶段已经确定了研究药物的最大耐受剂量(MTD)。我们分别使用反应率和毒性率的 80%下限置信区间和 90%上限置信区间作为剂量扩展阶段的决策阈值。我们参考预设的最小有效反应率和最大安全 DLT 率计算操作特征。
我们将我们的框架应用于指定 DEC 计划系统。设计包括三个部分:1)MTD 纳入的受试者数量,2)表示暂定药物疗效所需的最小反应数,以及 3)表示药物安全性所需的最大剂量限制毒性(DLT)数。我们在癌症免疫疗法试验的应用中展示了我们的方法。
我们的简单实用工具能够创建适当解决试验安全性和疗效目标的 DEC 设计。