Ohneberg Kristin, Schumacher Martin, Beyersmann Jan
Institute for Medical Biometry and Statistics, Faculty of Medicine and Medical Center-University of Freiburg, Stefan-Meier-Str. 26, 79104, Freiburg, Germany.
Freiburg Center for Data Analysis and Modeling (FDM), Eckerstr. 1, 79104, Freiburg, Germany.
Stat Med. 2017 Nov 30;36(27):4353-4363. doi: 10.1002/sim.7437. Epub 2017 Aug 22.
Competing risks extend standard survival analysis to considering time-to-first-event and type-of-first-event, where the event types are called competing risks. The competing risks process is completely described by all cause-specific hazards, ie, the hazard marked by the event type. Separate Cox models for each cause-specific hazard are the standard approach to regression modelling, but they come with the interpretational challenge that there are as many regression coefficients as there are competing risks. An alternative approach is to directly model the cumulative event probabilities, but again, there will be as many models as there are competing risks. The aim of this paper is to investigate the usefulness of a third alternative. Proportional odds modelling of all cause-specific hazards summarizes the effect of one covariate on "opposing" competing outcomes in one regression coefficient. For instance, if the competing outcomes are hospital death and alive discharge from hospital, the modelling assumption is that a covariate affects both outcomes in opposing directions, but the effect size is of the same absolute magnitude. We will investigate the interpretational aspects of the approach analysing a data set on intensive care unit patients using parametric methods.
竞争风险将标准生存分析扩展到考虑首次事件发生时间和首次事件类型，其中事件类型被称为竞争风险。竞争风险过程完全由所有特定病因风险函数描述，即由事件类型标记的风险函数。针对每个特定病因风险函数的单独Cox模型是回归建模的标准方法，但它们存在解释上的挑战，即回归系数的数量与竞争风险的数量一样多。另一种方法是直接对累积事件概率进行建模，但同样，模型的数量也与竞争风险的数量一样多。本文的目的是研究第三种替代方法的实用性。对所有特定病因风险函数进行比例优势建模，在一个回归系数中总结一个协变量对“相反”竞争结局的影响。例如，如果竞争结局是医院死亡和出院存活，建模假设是一个协变量以相反方向影响这两个结局，但效应大小的绝对值相同。我们将使用参数方法分析一个重症监护病房患者数据集，研究该方法的解释方面。
