Reflections on estimands for patient-reported outcomes in cancer clinical trials.

IF 1.2 4区 医学 Q4 PHARMACOLOGY & PHARMACY
Rachael Lawrance, Konstantina Skaltsa, Antoine Regnault, Lysbeth Floden
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引用次数: 0

Abstract

It is common and important to include the patient's perspective of the impact of treatment on health-related quality of life (HRQoL) outcomes. In this commentary, we focus on applying the new addendum to ICH E9 guideline E9 (R1) relating to the estimand framework to Patient Reported Outcomes (PROs) collected in cancer clinical trials, from a statistician's viewpoint. Currently, common practice for statistical analysis of PRO endpoints of published cancer clinical trials demonstrates ambiguity, leaving critical questions unspecified, hindering conclusions about the effect of treatment on PRO endpoints as well as comparability between clinical trials. To avoid this scenario, we advocate the systematic use of the estimand framework which requires the prospective definition of clear PRO research questions. Among the five attributes of the estimands framework, the definition of the endpoint (what is the right PRO measure and timeframe to target and why?), the intercurrent event identification and management (what happens with PRO data post-disease progression, what is the impact of death?) and the population-level summary (what is an acceptable statistical summary for PRO data?) require the most attention for PRO estimands. We identify good practice and highlight discussion points including the challenges of statistical analysis in the presence of missing and/or unobservable data and in relation to death. Through this discussion we highlight that there is no "statistical magic", but that the estimand framework will help you find out what you really want to know when quantifying the benefit of treatments from the patients' perspective.

对癌症临床试验中患者报告结果估计的思考。
纳入患者对治疗对健康相关生活质量(HRQoL)结果影响的看法是常见且重要的。在这篇评论中,我们将从统计学家的角度,重点讨论如何应用ICH E9指南E9 (R1)的新附录,该附录与癌症临床试验中收集的患者报告结果(PROs)的估计框架有关。目前,对已发表的癌症临床试验的PRO终点进行统计分析的常见做法存在模糊性,使关键问题未明确,阻碍了对治疗对PRO终点的影响的结论以及临床试验之间的可比性。为了避免这种情况,我们提倡系统地使用评估框架,这需要对明确的PRO研究问题进行前瞻性定义。在估计框架的五个属性中,终点的定义(什么是正确的PRO测量和目标时间框架,为什么?)、并发事件的识别和管理(疾病进展后PRO数据发生了什么,死亡的影响是什么?)和人群水平的总结(什么是PRO数据可接受的统计总结?)需要对PRO估计最关注。我们确定了良好做法,并强调了讨论要点,包括在存在缺失和/或不可观察数据以及与死亡有关的情况下进行统计分析的挑战。通过这次讨论,我们强调没有“统计魔术”,但是估算框架将帮助您从患者的角度量化治疗的益处时找到您真正想知道的东西。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Biopharmaceutical Statistics
Journal of Biopharmaceutical Statistics 医学-统计学与概率论
CiteScore
2.50
自引率
18.20%
发文量
71
审稿时长
6-12 weeks
期刊介绍: The Journal of Biopharmaceutical Statistics, a rapid publication journal, discusses quality applications of statistics in biopharmaceutical research and development. Now publishing six times per year, it includes expositions of statistical methodology with immediate applicability to biopharmaceutical research in the form of full-length and short manuscripts, review articles, selected/invited conference papers, short articles, and letters to the editor. Addressing timely and provocative topics important to the biostatistical profession, the journal covers: Drug, device, and biological research and development; Drug screening and drug design; Assessment of pharmacological activity; Pharmaceutical formulation and scale-up; Preclinical safety assessment; Bioavailability, bioequivalence, and pharmacokinetics; Phase, I, II, and III clinical development including complex innovative designs; Premarket approval assessment of clinical safety; Postmarketing surveillance; Big data and artificial intelligence and applications.
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