Chapple Andrew G, Vannucci Marina, Thall Peter F, Lin Steven
Rice University, Department of Statistics, 6100 Main St., Duncan Hall 2124, Houston, TX 77005, U.S.A.
Department of Biomathematics, Box 237, M.D. Anderson Cancer Center, University of Texas, 1515 Holocombe Boulevard, Houston, TX 77030, U.S.A.
Comput Stat Data Anal. 2017 Aug;112:170-185. doi: 10.1016/j.csda.2017.03.002. Epub 2017 Mar 22.
A variable selection procedure is developed for a semi-competing risks regression model with three hazard functions that uses priors and stochastic search variable selection algorithms for posterior inference. A rule is devised for choosing the threshold on the marginal posterior probability of variable inclusion based on the Deviance Information Criterion (DIC) that is examined in a simulation study. The method is applied to data from esophageal cancer patients from the MD Anderson Cancer Center, Houston, TX, where the most important covariates are selected in each of the hazards of effusion, death before effusion, and death after effusion. The DIC procedure that is proposed leads to similar selected models regardless of the choices of some of the hyperparameters. The application results show that patients with intensity-modulated radiation therapy have significantly reduced risks of pericardial effusion, pleural effusion, and death before either effusion type.
针对具有三个风险函数的半竞争风险回归模型开发了一种变量选择程序，该程序使用先验和随机搜索变量选择算法进行后验推断。基于偏差信息准则（DIC）设计了一条规则，用于选择变量纳入的边际后验概率阈值，该规则在模拟研究中进行了检验。该方法应用于德克萨斯州休斯顿市MD安德森癌症中心的食管癌患者数据，其中在积液、积液前死亡和积液后死亡的每种风险中选择了最重要的协变量。所提出的DIC程序导致的选定模型相似，而与一些超参数的选择无关。应用结果表明，接受调强放射治疗的患者发生心包积液、胸腔积液以及在任何一种积液类型出现之前死亡的风险显著降低。
