Subdistribution hazard models for competing risks in discrete time
Subdistribution hazard models for competing risks in discrete time
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DOI：
10.1093/biostatistics/kxy069
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发表时间：
2020-07-01
期刊：
影响因子：
2.1
通讯作者：
Beyersmann, Jan
中科院分区：
文献类型：
--
作者：
Berger, Moritz;Schmid, Matthias;Beyersmann, Jan
在纵向研究中，用于竞争风险分析的一种流行的建模方法是Fine and Gray（1999。竞争风险子分布的比例危害模型）的比例分布危害模型。美国统计协会杂志94，496-509）。该模型广泛用于分析临床和流行病学研究中的连续事件时间。但是，当事件时间在离散的时间尺度上测量时，它不适用，这是当事件对之间发生在连续的时间点之间的情况时（例如，在一个流行病学研究的两次随访之间）和确切的情况连续时间跨度的长度尚不清楚。为了适应这种情况，我们提出了一种在离散时间内建模子分布危害的技术。我们的方法基于二进制回归的加权ML估计方案，从而导致模型参数的一致和渐近正常估计器。我们通过对医院治疗的患者的医院肺炎进行分析来说明建模方法。
A popular modeling approach for competing risks analysis in longitudinal studies is the proportional subdistribution hazards model by Fine and Gray (1999. A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association 94, 496-509). This model is widely used for the analysis of continuous event times in clinical and epidemiological studies. However, it does not apply when event times are measured on a discrete time scale, which is a likely scenario when events occur between pairs of consecutive points in time (e.g., between two follow-up visits of an epidemiological study) and when the exact lengths of the continuous time spans are not known. To adapt the Fine and Gray approach to this situation, we propose a technique for modeling subdistribution hazards in discrete time. Our method, which results in consistent and asymptotically normal estimators of the model parameters, is based on a weighted ML estimation scheme for binary regression. We illustrate the modeling approach by an analysis of nosocomial pneumonia in patients treated in hospitals.