应用患者报告结局连续评估方法于子宫内膜癌放疗的 I 期研究。
Application of the patient-reported outcomes continual reassessment method to a phase I study of radiotherapy in endometrial cancer.
机构信息
Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA.
Massey Cancer Center, Virginia Commonwealth University, Richmond, VA, USA.
出版信息
Int J Biostat. 2022 Nov 17;19(1):163-176. doi: 10.1515/ijb-2022-0023. eCollection 2023 May 1.
This article considers the concept of designing Phase I clinical trials using both clinician- and patient-reported outcomes to adaptively allocate study participants to tolerable doses and determine the maximum tolerated dose (MTD) at the study conclusion. We describe an application of a Bayesian form of the patient-reported outcomes continual reassessment method (PRO-CRMB) in an ongoing Phase I study of adjuvant hypofractionated whole pelvis radiation therapy (WPRT) in endometrial cancer (NCT04458402). The study's primary objective is to determine the MTD per fraction of WPRT, defined by acceptable clinician- and patient-reported DLT rates. We conduct simulation studies of the operating characteristics of the design and compared them to a rule-based approach. We illustrate that the PRO-CRMB makes appropriate dose assignments during the study to give investigators and reviewers an idea of how the method behaves. In simulation studies, the PRO-CRMB demonstrates superior performance to a 5 + 2 stepwise design in terms of recommending target treatment courses and allocating patients to these courses. The design is accompanied by an easy-to-use R shiny web application to simulate operating characteristics at the design stage and sequentially update dose assignments throughout the trial's conduct.
本文考虑了使用临床医生和患者报告的结果来设计 I 期临床试验的概念,以自适应地将研究参与者分配到可耐受的剂量,并在研究结束时确定最大耐受剂量 (MTD)。我们描述了一种在正在进行的子宫内膜癌辅助短程全骨盆放射治疗 (WPRT) 的 I 期研究中应用患者报告结果连续评估方法 (PRO-CRMB) 的贝叶斯形式的应用,该研究的主要目的是确定 WPRT 的每个分数的 MTD,由可接受的临床医生和患者报告的 DLT 率定义。我们对设计的操作特性进行了模拟研究,并将其与基于规则的方法进行了比较。我们表明,PRO-CRMB 在研究期间进行了适当的剂量分配,让研究人员和审查者了解该方法的行为。在模拟研究中,PRO-CRMB 在推荐目标治疗方案和将患者分配到这些方案方面的表现优于 5+2 逐步设计。该设计伴随着一个易于使用的 R shiny 网络应用程序,用于在设计阶段模拟操作特性,并在整个试验进行过程中顺序更新剂量分配。