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利用替代方法实现肿瘤临床试验不良事件分析的现代化:MOTIVATE 试验的原理和设计。

Modernizing adverse events analysis in oncology clinical trials using alternative approaches: rationale and design of the MOTIVATE trial.

机构信息

Department of Biostatistics, Institut Claudius Regaud - IUCT-O, 1 avenue Irène Joliot-Curie, 31059, Toulouse Cedex 9, France.

Department of Medical Oncology, Institut Claudius Regaud - IUCT-O, Toulouse, France.

出版信息

Invest New Drugs. 2020 Dec;38(6):1879-1887. doi: 10.1007/s10637-020-00938-x. Epub 2020 May 7.

Abstract

In oncology clinical research, the analysis and reporting of adverse events is of major interest. A consistent depiction of the safety profile of a new treatment is as crucial in establishing how to use it as its antitumor activity. The advent of new therapeutics has led to major changes in the management of patients and targeted therapies or immune checkpoint inhibitors are administered continuously for months or even years. However, the classical methods of adverse events analysis are no longer adequate to properly assess their safety profile. Indeed, the worst grade method and time-to-event analysis cannot capture the duration or the evolution of adverse events induced by extended treatment durations. Many authors have highlighted this issue and argue that the analysis of safety data from clinical trials should be modernized by considering the dimension of time and the recurrent nature of adverse events. This paper aims to illustrate the limitations of current methods and discusses the value of alternative approaches such as the prevalence function, Q-TWiST, the ToxT and the recurrent event approaches. The rationale and design of the MOTIVATE trial, which aims to model the evolution of toxicities over time using the prevalence function in patients treated by immunotherapy, is also presented ( ClinicalTrials.gov Identifier: NCT03447483; Date of registration: 27 February 2018).

摘要

在肿瘤学临床研究中,不良事件的分析和报告是主要关注点。一致描述新治疗方法的安全性概况对于确定如何使用它与抗肿瘤活性一样至关重要。新疗法的出现导致了患者管理的重大变化,靶向治疗或免疫检查点抑制剂连续数月甚至数年给药。然而,不良事件分析的经典方法已不再足以正确评估其安全性概况。事实上,最差等级方法和时间事件分析无法捕获由延长治疗持续时间引起的不良事件的持续时间或演变。许多作者已经强调了这一问题,并认为应该通过考虑时间维度和不良事件的复发性来使临床试验的安全性数据分析现代化。本文旨在说明当前方法的局限性,并讨论替代方法的价值,如流行函数、Q-TWiST、ToxT 和复发性事件方法。还介绍了 MOTIVATE 试验的原理和设计,该试验旨在使用免疫治疗患者的流行函数来模拟毒性随时间的演变(ClinicalTrials.gov 标识符:NCT03447483;注册日期:2018 年 2 月 27 日)。

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