血液肿瘤学试验中的估计目标。

Estimands in hematologic oncology trials.

机构信息

Statistics and Decision Science, Janssen Research and Development LLC, Raritan, New Jersey, USA.

Clinical Development and Analytics, Novartis Pharma AG, Basel, Switzerland.

出版信息

Pharm Stat. 2021 Jul;20(4):793-805. doi: 10.1002/pst.2108. Epub 2021 Mar 8.

Abstract

The estimand framework included in the addendum to the ICH E9 guideline facilitates discussions to ensure alignment between the key question of interest, the analysis, and interpretation. Therapeutic knowledge and drug mechanism play a crucial role in determining the strategy and defining the estimand for clinical trial designs. Clinical trials in patients with hematological malignancies often present unique challenges for trial design due to complexity of treatment options and existence of potential curative but highly risky procedures, for example, stem cell transplant or treatment sequence across different phases (induction, consolidation, maintenance). Here, we illustrate how to apply the estimand framework in hematological clinical trials and how the estimand framework can address potential difficulties in trial result interpretation. This paper is a result of a cross-industry collaboration to connect the International Conference on Harmonisation (ICH) E9 addendum concepts to applications. Three randomized phase 3 trials will be used to consider common challenges including intercurrent events in hematologic oncology trials to illustrate different scientific questions and the consequences of the estimand choice for trial design, data collection, analysis, and interpretation. Template language for describing estimand in both study protocols and statistical analysis plans is suggested for statisticians' reference.

摘要

附录中包含的估计量框架有助于讨论,以确保关键问题、分析和解释之间的一致性。治疗知识和药物机制在确定临床试验设计的策略和定义估计量方面起着至关重要的作用。由于治疗方案的复杂性以及潜在的治愈但风险极高的程序(例如干细胞移植或不同阶段(诱导、巩固、维持)的治疗顺序),血液系统恶性肿瘤患者的临床试验通常会带来独特的设计挑战。在这里,我们说明如何在血液学临床试验中应用估计量框架,以及估计量框架如何解决试验结果解释中的潜在困难。本文是行业间合作的结果,旨在将国际协调会议(ICH)E9 附录概念与应用联系起来。将使用三项随机 3 期试验来考虑常见挑战,包括血液肿瘤学试验中的并发事件,以说明不同的科学问题以及估计量选择对试验设计、数据收集、分析和解释的影响。建议统计人员参考描述研究方案和统计分析计划中估计量的模板语言。

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