Title: Variable selection via penalized GEE for a marginal survival model
Authors: Yi Niu - Dalian University of Technology (China) [presenting]
Abstract: Clustered and multivariate survival times, such as times to recurrent events, often arise in biomedical and health research, and marginal survival models are often used to model such data. When there are a large number of predictors available, variable selection is always an important issue when modeling such data with a marginal survival model. We consider a marginal Cox's proportional hazards model. Under the sparsity assumption, we propose a penalized generalized estimating equations 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 demonstrate its application using two real datasets.