Department of Statistics, Seoul National University, Seoul, South Korea.
Department of Statistics, Pukyong National University, Busan, South Korea.
Stat Med. 2021 Dec 20;40(29):6541-6557. doi: 10.1002/sim.9197. Epub 2021 Sep 20.
Competing risks data usually arise when an occurrence of an event precludes other types of events from being observed. Such data are often encountered in a clustered clinical study such as a multi-center clinical trial. For the clustered competing-risks data which are correlated within a cluster, competing-risks models allowing for frailty terms have been recently studied. To the best of our knowledge, however, there is no literature on variable selection methods for cause-specific hazard frailty models. In this article, we propose a variable selection procedure for fixed effects in cause-specific competing risks frailty models using a penalized h-likelihood (HL). Here, we study three penalty functions, LASSO, SCAD, and HL. Simulation studies demonstrate that the proposed procedure using the HL penalty works well, providing a higher probability of choosing the true model than LASSO and SCAD methods without losing prediction accuracy. The proposed method is illustrated by using two kinds of clustered competing-risks cancer data sets.
竞争风险数据通常在事件发生排除其他类型的事件被观察到的情况下出现。这种数据通常在聚类临床研究中遇到，如多中心临床试验。对于聚类竞争风险数据在聚类内相关，允许脆弱性项的竞争风险模型最近已经被研究。然而，据我们所知，关于特定原因风险脆弱性模型的变量选择方法尚无文献报道。在本文中，我们提出了一种使用惩罚 h 似然（HL）的固定效应特定原因竞争风险脆弱性模型的变量选择程序。在这里，我们研究了三种惩罚函数，LASSO、SCAD 和 HL。模拟研究表明，使用 HL 惩罚的建议程序效果良好，与 LASSO 和 SCAD 方法相比，选择真实模型的概率更高，而不会损失预测准确性。该方法通过使用两种聚类竞争风险癌症数据集进行了说明。
