Yang Xu, Shishun Zhao, Tao Hu, Jianguo Sun
This paper discusses variable selection for interval-censored failure time data, a general type of failure time data that commonly arise in many areas such as clinical trials and follow-up studies. Although some methods have been developed in the literature for the problem, most of the existing procedures apply only to specific models. In this paper, we consider the data arising from a general class of partly linear additive generalized odds rate models and propose a penalized variable selection approach through maximizing a derived penalized likelihood function. In the method, the Bernsetin polynomials are employed to approximate both the unknown baseline hazard functions and the nonlinear covariate effects functions, and for the implementation of the method, a coordinate descent algorithm is developed. Also the asymptotic properties of the proposed estimators, including the oracle property, are established. An extensive simulation study is conducted to assess the finite-sample performance of the proposed estimators and indicates that it works well in practice. Finally, the proposed method is applied to a set of real data on Alzheimer's disease.
