Why you should avoid using multiple Fine–Gray models: insights from (attempts at) simulating proportional subdistribution hazards data
本文探讨了在竞争风险研究中，当多个竞争事件都有科学意义时，应避免为每个事件分别使用Fine-Gray模型，而推荐使用原因特异性风险模型等更优替代方案。
Abstract Studies considering competing risks will often aim to estimate the cumulative incidence functions conditional on an individual’s baseline characteristics. While the Fine–Gray subdistribution hazard model is tailor-made for analysing only one of the competing events, it may still be used in settings where multiple competing events are of scientific interest, where it is specified for each cause in turn. In this work, we provide an overview of data-generating mechanisms where proportional subdistribution hazards hold for at least one cause. We use these to motivate why the use of multiple Fine–Gray models should be avoided in favour of better alternatives such as cause-specific hazard models.