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Abstract:
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Variable selection has recently attracted a great deal of attention and correspondingly, many methods have been proposed. In this paper, we discuss the topic when one faces interval-censored failure time data arising from a model with time-varying coefficients, for which there does not seem to exist a method. For the situation, in addition to identifying important variables or covariates, a desired feature of a variable selection method is to distinguish time-varying coefficients from time-independent ones, which also presents an additional challenge. To address these, a penalized maximum likelihood procedure is presented and in the proposed method, the adaptive group Lassopenalty function and B-spline functions are used. The approach can simultaneously select between time-dependent and time-independent covariate effects. To implement the proposed procedure, an EM algorithm is developed, and a simulation study is conducted and suggests that the proposed method works well in practical situations. Finally, it is applied a set of real data on Alzheimer’s disease that motivated this study.
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