On the analysis of discrete time competing risks data
On the analysis of discrete time competing risks data
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DOI：
10.1111/biom.12881
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发表时间：
2018-12-01
期刊：
影响因子：
1.9
通讯作者：
Fine, Jason P.
中科院分区：
文献类型：
--
作者：
Lee, Minjung;Feuer, Eric J.;Fine, Jason P.
回归方法已经针对具有连续事件时间的竞争风险数据进行了很好的开发，无论是导致特定的危害和累积发病率功能。但是，在许多应用程序中，包括国家癌症研究所的监视，流行病学和最终结果（SEER）计划的应用程序，可能会离散地观察到活动时间。将连续时间回归方法幼稚地应用于此类数据是不合适的。我们提出了最大的可能性推断，以估算离散时间原因特定危害函数模型参数，开发相关累积发生率函数的预测，并为预测的累积发生率函数提供一致的方差估计器。这些方法很容易使用标准软件用于通用估计方程，其中不同原因的模型可以单独拟合。对于SEER数据，可能希望在不同的时间尺度上对不同的事件类型进行建模，并将这些方法推广以适应此类场景，从而扩展了较早的连续时间数据工作。仿真研究表明，这些方法在现实的设置中表现良好。该方法用SEER的III期结肠癌数据进行了说明。
Regression methodology has been well developed for competing risks data with continuous event times, both for the cause-specific hazard and cumulative incidence functions. However, in many applications, including those from the Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute, the event times may be observed discretely. Naive application of continuous time regression methods to such data is not appropriate. We propose maximum likelihood inferences for estimation of model parameters for the discrete time cause-specific hazard functions, develop predictions for the associated cumulative incidence functions, and derive consistent variance estimators for the predicted cumulative incidence functions. The methods are readily implemented using standard software for generalized estimating equations, where models for different causes may be fitted separately. For the SEER data, it may be desirable to model different event types on different time scales and the methods are generalized to accommodate such scenarios, extending earlier work on continuous time data. Simulation studies demonstrate that the methods perform well in realistic set-ups. The methodology is illustrated with stage III colon cancer data from SEER.