Document Type

Article

Disciplines

Medicine and Health Sciences

Abstract

Cox models with time-varying coefficients offer great flexibility in capturing the temporal dynamics of covariate effects on right censored failure times. Since 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.

Comments

Biometrics. Author manuscript; available in PMC 2012 June 28. Published in final edited form as: Biometrics. 2012 June; 68(2): 419–428. Published online 2012 April 16. doi: 10.1111/j.1541-0420.2011.01692.x PMCID: PMC3384767 NIHMSID: NIHMS332898

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