Insititue of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, Hamburg, 20246, Germany.
Institute of Medical Biometry and Informatics, Universtiy Medical Center Ruprecht-Karls Universtiy Heidelberg, Im Neuenheimer Feld 130.3, Heidelberg, 69120, Germany.
BMC Med Res Methodol. 2018 Jan 4;18(1):2. doi: 10.1186/s12874-017-0462-x.
Many clinical trials focus on the comparison of the treatment effect between two or more groups concerning a rarely occurring event. In this situation, showing a relevant effect with an acceptable power requires the observation of a large number of patients over a long period of time. For feasibility issues, it is therefore often considered to include several event types of interest, non-fatal or fatal, and to combine them within a composite endpoint. Commonly, a composite endpoint is analyzed with standard survival analysis techniques by assessing the time to the first occurring event. This approach neglects that an individual may experience more than one event which leads to a loss of information. As an alternative, composite endpoints could be analyzed by models for recurrent events. There exists a number of such models, e.g. regression models based on count data or Cox-based models such as the approaches of Andersen and Gill, Prentice, Williams and Peterson or, Wei, Lin and Weissfeld. Although some of the methods were already compared within the literature there exists no systematic investigation for the special requirements regarding composite endpoints.
Within this work a simulation-based comparison of recurrent event models applied to composite endpoints is provided for different realistic clinical trial scenarios.
We demonstrate that the Andersen-Gill model and the Prentice- Williams-Petersen models show similar results under various data scenarios whereas the Wei-Lin-Weissfeld model delivers effect estimators which can considerably deviate under commonly met data scenarios.
Based on the conducted simulation study, this paper helps to understand the pros and cons of the investigated methods in the context of composite endpoints and provides therefore recommendations for an adequate statistical analysis strategy and a meaningful interpretation of results.
许多临床试验侧重于比较两个或更多组之间关于罕见事件的治疗效果。在这种情况下，为了达到可接受的效能，需要观察大量患者很长一段时间。由于可行性问题，因此通常考虑纳入几种感兴趣的事件类型，包括非致命或致命的，并将它们组合在一个复合终点内。通常，使用标准生存分析技术通过评估首次发生事件的时间来分析复合终点。这种方法忽略了个体可能经历多次事件，从而导致信息丢失。作为替代方法，可以使用用于复发性事件的模型来分析复合终点。存在许多这样的模型，例如基于计数数据的回归模型或基于 Cox 的模型，例如 Andersen 和 Gill、Prentice、Williams 和 Peterson 或 Wei、Lin 和 Weissfeld 的方法。尽管一些方法已经在文献中进行了比较，但对于复合终点的特殊要求还没有系统的研究。
在这项工作中，针对不同的现实临床试验场景，提供了应用于复合终点的复发性事件模型的基于模拟的比较。
我们证明了 Andersen-Gill 模型和 Prentice-Williams-Petersen 模型在各种数据场景下具有相似的结果，而 Wei-Lin-Weissfeld 模型在常见的数据场景下提供的效果估计值可能会有很大偏差。
基于进行的模拟研究，本文有助于理解在复合终点背景下所研究方法的优缺点，并为适当的统计分析策略和有意义的结果解释提供建议。
