OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany; Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology - OncoRay, Dresden, Germany.
OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany; German Cancer Consortium (DKTK), Partner Site Dresden, and German Cancer Research Center (DKFZ), Heidelberg, Germany; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
Radiother Oncol. 2019 Jan;130:185-189. doi: 10.1016/j.radonc.2018.09.007. Epub 2018 Oct 9.
Clinical trials and retrospective studies in the field of radiation oncology often consider time-to-event data as their primary endpoint. Such studies are susceptible to competing risks, i.e. competing events may preclude the occurrence of the event of interest or modify the chance that the primary endpoint occurs. Competing risks are frequently neglected and the event of interest is analysed with standard statistical methods. Here, we would like to create awareness of the problem and demonstrate different methods for survival data analysis in the presence of competing risks.
在放射肿瘤学领域的临床试验和回顾性研究中，通常将生存时间数据作为主要终点。此类研究易受到竞争风险的影响，即竞争事件可能会阻止感兴趣事件的发生，或改变主要终点发生的机会。竞争风险经常被忽视，并且使用标准统计方法分析感兴趣事件。在这里，我们希望引起人们对该问题的认识，并展示在存在竞争风险的情况下进行生存数据分析的不同方法。
