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Model Selection for Cox Models with Time‐Varying Coefficients
Author(s) -
Yan Jun,
Huang Jian
Publication year - 2012
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2011.01692.x
Subject(s) - covariate , accelerated failure time model , selection (genetic algorithm) , flexibility (engineering) , proportional hazards model , model selection , mathematics , computer science , lasso (programming language) , basis (linear algebra) , statistics , artificial intelligence , geometry , world wide web
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.