Han Baoguang, Yu Menggang, Dignam James J, Rathouz Paul J
Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN, 46285, U.S.A.
Stat Med. 2014 Dec 20;33(29):5111-25. doi: 10.1002/sim.6313. Epub 2014 Oct 2.
Semicompeting risks data arise when two types of events, non-terminal and terminal, are observed. When the terminal event occurs first, it censors the non-terminal event, but not vice versa. To account for possible dependent censoring of the non-terminal event by the terminal event and to improve prediction of the terminal event using the non-terminal event information, it is crucial to model their association properly. Motivated by a breast cancer clinical trial data analysis, we extend the well-known illness-death models to allow flexible random effects to capture heterogeneous association structures in the data. Our extension also represents a generalization of the popular shared frailty models that usually assume that the non-terminal event does not affect the hazards of the terminal event beyond a frailty term. We propose a unified Bayesian modeling approach that can utilize existing software packages for both model fitting and individual-specific event prediction. The approach is demonstrated via both simulation studies and a breast cancer data set analysis.
当观察到非终末事件和终末事件这两种类型的事件时，就会出现半竞争风险数据。当终末事件首先发生时，它会截尾非终末事件，但反之则不然。为了考虑终末事件对非终末事件可能的相依截尾，并利用非终末事件信息改进对终末事件的预测，正确建模它们之间的关联至关重要。受一项乳腺癌临床试验数据分析的启发，我们扩展了著名的疾病-死亡模型，以允许灵活的随机效应来捕捉数据中的异质关联结构。我们的扩展还代表了流行的共享脆弱性模型的推广，该模型通常假设非终末事件除了一个脆弱性项之外不会影响终末事件的风险。我们提出了一种统一的贝叶斯建模方法，该方法可以利用现有的软件包进行模型拟合和个体特定事件预测。通过模拟研究和乳腺癌数据集分析对该方法进行了验证。
