title: Model selection for Cox models with time-varying coefficients.
abstract: Summary Cox models with time-varying coefficients offer great flexibility in capturing the temporal dynamics of covariate effects on right-censored failure times. Because not all covariate coefficients are time varying, model selection for such models presents an additional challenge, which is to distinguish covariates with time-varying coefficient from those with time-independent coefficient. We propose an adaptive group lasso method that not only selects important variables but also selects between time-independent and time-varying specifications of their presence in the model. Each covariate effect is partitioned into a time-independent part and a time-varying part, the latter of which is characterized by a group of coefficients of basis splines without intercept. Model selection and estimation are carried out through a fast, iterative group shooting algorithm. Our approach is shown to have good properties in a simulation study that mimics realistic situations with up to 20 variables. A real example illustrates the utility of the method.
authors.affiliation: Department of Statistics, University of Connecticut, Storrs, Connecticut 06269, USA. jun.yan@uconn.edu
authors.forename: Jun
authors.initials: J
authors.lastname: Yan
authors.forename: Jian
authors.initials: J
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D001699:Biometry / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D016014:Linear Models; D008105:Liver Cirrhosis, Biliary / Q000401:mortality; D016016:Proportional Hazards Models; D016019:Survival Analysis; D013997:Time Factors