Model Selection for Cox Models with Time-Varying Coefficients
Article first published online: 16 APR 2012
© 2011, International Biometric Society
Volume 68, Issue 2, pages 419–428, June 2012
How to Cite
Yan, J. and Huang, J. (2012), Model Selection for Cox Models with Time-Varying Coefficients. Biometrics, 68: 419–428. doi: 10.1111/j.1541-0420.2011.01692.x
- Issue published online: 26 JUN 2012
- Article first published online: 16 APR 2012
- Received September 2010. Revised September 2011., Accepted September 2011.
- Group lasso;
- Varying coefficient
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.