Mansournia Mohammad Ali, Nazemipour Maryam, Etminan Mahyar
Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Department of Ophthalmology, Medicine and Pharmacology, University of British Columbia, Vancouver, Canada.
Glob Epidemiol. 2022 Jul 11;4:100080. doi: 10.1016/j.gloepi.2022.100080. eCollection 2022 Dec.
Competing events are events that preclude the occurrence of the primary outcome. Much has been written on mainly the statistics behind competing events analyses. However, many of these publications and tutorials have a strong statistical tone and might fall short in providing a practical guide to clinician researchers as to when to use a competing event analysis and more importantly which method to use and why. Here we discuss the different target effects in the Fine-Gray and cause-specific methods using simple causal diagrams and provide strengths and limitations of both approaches for addressing etiologic questions. We argue why the Fine-Gray method might not be the best approach for handling competing events in etiological time-to-event studies.
竞争事件是指会妨碍主要结局发生的事件。关于竞争事件分析背后的统计学内容,已有大量著述。然而,这些出版物和教程大多带有浓厚的统计学色彩,在为临床研究人员提供何时使用竞争事件分析的实用指南方面,可能有所欠缺,更重要的是,在指导他们选择使用哪种方法以及为何选择该方法上也存在不足。在此,我们使用简单的因果图来讨论Fine-Gray法和特定病因法中的不同目标效应,并阐述这两种方法在解决病因学问题方面的优势和局限性。我们认为,在病因学事件发生时间研究中,Fine-Gray法可能并非处理竞争事件的最佳方法。