Pohl Moritz, Baumann Lukas, Behnisch Rouven, Kirchner Marietta, Krisam Johannes, Sander Anja
Institute of Medical Biometry, University Hospital Heidelberg, Heidelberg, Germany.
Dtsch Arztebl Int. 2021 Dec 27;118(51-52):883-888. doi: 10.3238/arztebl.m2021.0373.
Clinical trials are of central importance for the evaluation and comparison of treatments. The transparency and intelligibility of the treatment effect under investigation is an essential matter for physicians, patients, and health-care authorities. The estimand framework has been introduced because many trials are deficient in this respect.
Introduction, definition, and application of the estimand framework on the basis of an example and a selective review of the literature.
The estimand framework provides a systematic approach to the definition of the treatment effect under investigation in a clinical trial. An estimand consists of five attributes: treatment, population, variable, population-level summary, and handling of intercurrent events. Each of these attributes is defined in an interdisciplinary discussion during the trial planning phase, based on the clinical question being asked. Special attention is given to the handling of intercurrent events (ICEs): these are events-e.g., discontinuation or modification of treatment or the use of emergency medication-that can occur once the treatment has begun and might affect the possibility of observing the endpoints or their interpretability. There are various strategies for the handling of ICEs; these can, for example, also reflect the existing intention-to-treat (ITT) principle. Per-protocol analyses, in contrast, are prone to bias and cannot be represented in a sensible manner by an estimand, although they may be performed as a supplementary analysis. The discussion of potential intercurrent events and how they should appropriately be handled in view of the aim of the trial must already take place in the planning phase.
Use of the estimand framework should make it easier for both physicians and patients to understand what trials reveal about the efficacy of treatment, and to compare the results of different trials.
临床试验对于治疗方法的评估和比较至关重要。所研究治疗效果的透明度和可理解性对于医生、患者和医疗保健当局而言是至关重要的问题。引入估计量框架是因为许多试验在这方面存在缺陷。
基于一个实例和对文献的选择性回顾介绍、定义并应用估计量框架。
估计量框架为临床试验中所研究治疗效果的定义提供了一种系统方法。一个估计量由五个属性组成：治疗、人群、变量、人群水平汇总以及并发事件的处理。在试验规划阶段，基于所提出的临床问题，通过跨学科讨论对这些属性中的每一个进行定义。特别关注并发事件（ICEs）的处理：这些是在治疗开始后可能发生的事件，例如治疗的中断或改变或急救药物的使用，并且可能影响观察终点的可能性或其可解释性。存在多种处理ICEs的策略；例如，这些策略也可以反映现有的意向性分析（ITT）原则。相比之下，符合方案分析容易产生偏差，并且估计量无法以合理的方式表示，尽管可以作为补充分析进行。关于潜在并发事件以及鉴于试验目的应如何适当处理这些事件的讨论必须在规划阶段就已进行。
使用估计量框架应使医生和患者都更容易理解试验揭示的治疗效果，并比较不同试验的结果。
