Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Biometry and Clinical Epidemiology, Berlin, Germany.
Institute of Medical Biometry and Informatics, University of Heidelberg, Berlin, Germany.
Clin Res Cardiol. 2018 May;107(5):437-443. doi: 10.1007/s00392-018-1205-7. Epub 2018 Feb 16.
Composite endpoints combining several event types of clinical interest often define the primary efficacy outcome in cardiologic trials. They are commonly evaluated as time-to-first-event, thereby following the recommendations of regulatory agencies. However, to assess the patient's full disease burden and to identify preventive factors or interventions, subsequent events following the first one should be considered as well. This is especially important in cohort studies and RCTs with a long follow-up leading to a higher number of observed events per patients. So far, there exist no recommendations which approach should be preferred.
Recently, the Cardiovascular Round Table of the European Society of Cardiology indicated the need to investigate "how to interpret results if recurrent-event analysis results differ […] from time-to-first-event analysis" (Anker et al., Eur J Heart Fail 18:482-489, 2016). This work addresses this topic by means of a systematic simulation study.
This paper compares two common analysis strategies for composite endpoints differing with respect to the incorporation of recurrent events for typical data scenarios motivated by a clinical trial.
We show that the treatment effects estimated from a time-to-first-event analysis (Cox model) and a recurrent-event analysis (Andersen-Gill model) can systematically differ, particularly in cardiovascular trials. Moreover, we provide guidance on how to interpret these results and recommend points to consider for the choice of a meaningful analysis strategy.
When planning trials with a composite endpoint, researchers, and regulatory agencies should be aware that the model choice affects the estimated treatment effect and its interpretation.
将几种临床感兴趣的事件类型组合在一起的复合终点通常是心血管试验中的主要疗效终点。它们通常作为首次事件时间进行评估,从而遵循监管机构的建议。然而,为了评估患者的全部疾病负担并识别预防因素或干预措施,也应考虑首次事件后的后续事件。这在队列研究和 RCT 中尤为重要,因为它们随访时间较长,导致每个患者观察到的事件数量较多。到目前为止,还没有关于应该优先采用哪种方法的建议。
最近,欧洲心脏病学会心血管圆桌会议指出需要研究“如果复发事件分析结果与首次事件时间分析结果不同,应如何解释结果”(Anker 等人,Eur J Heart Fail 18:482-489, 2016)。这项工作通过系统的模拟研究解决了这个问题。
本文比较了两种常见的复合终点分析策略,它们在考虑到由临床试验引发的典型数据场景中的复发事件方面存在差异。
我们表明,首次事件时间分析(Cox 模型)和复发事件分析(Andersen-Gill 模型)估计的治疗效果可能会系统地不同,特别是在心血管试验中。此外,我们提供了如何解释这些结果的指导,并为选择有意义的分析策略提供了一些注意点。
在计划具有复合终点的试验时,研究人员和监管机构应意识到模型选择会影响估计的治疗效果及其解释。