Niu Yi, Wang Xiaoguang, Cao Hui, Peng Yingwei
School of Mathematical Sciences, Dalian University of Technology, Dalian, Liaoning, China.
Department of Public Health Sciences, Queen's University, Kingston, Canada.
Stat Methods Med Res. 2020 Sep;29(9):2493-2506. doi: 10.1177/0962280220901728. Epub 2020 Jan 29.
Clustered and multivariate survival times, such as times to recurrent events, commonly arise in biomedical and health research, and marginal survival models are often used to model such data. When a large number of predictors are available, variable selection is always an important issue when modeling such data with a survival model. We consider a Cox's proportional hazards model for a marginal survival model. Under the sparsity assumption, we propose a penalized generalized estimating equation approach to select important variables and to estimate regression coefficients simultaneously in the marginal model. The proposed method explicitly models the correlation structure within clusters or correlated variables by using a prespecified working correlation matrix. The asymptotic properties of the estimators from the penalized generalized estimating equations are established and the number of candidate covariates is allowed to increase in the same order as the number of clusters does. We evaluate the performance of the proposed method through a simulation study and analyze two real datasets for the application.
聚类和多变量生存时间，例如复发事件的发生时间，在生物医学和健康研究中经常出现，边际生存模型常被用于对此类数据进行建模。当有大量预测变量可用时，在用生存模型对此类数据建模时，变量选择始终是一个重要问题。我们考虑用于边际生存模型的Cox比例风险模型。在稀疏性假设下，我们提出一种惩罚广义估计方程方法，用于在边际模型中同时选择重要变量并估计回归系数。所提出的方法通过使用预先指定的工作相关矩阵，明确地对聚类内或相关变量之间的相关结构进行建模。建立了惩罚广义估计方程估计量的渐近性质，并允许候选协变量的数量与聚类数量以相同的阶数增加。我们通过模拟研究评估所提出方法的性能，并分析两个实际数据集以进行应用。
