Variable selection for generalized odds rate mixture cure models with interval-censored failure time data

Variable selection for failure time data with a cured fraction has been discussed by many authors but most of existing methods apply only to right-censored failure time data. In this paper, we consider variable selection when one faces interval-censored failure time data arising from a general class of generalized odds rate mixture cure models, and we propose a penalized variable selection method by maximizing a derived penalized likelihood function. In the method, the sieve approach is employed to approximate the unknown function, and it is implemented using a novel penalized expectation-maximization (EM) algorithm. Also the asymptotic properties of the proposed estimators of regression parameters, including the oracle property, are obtained. Furthermore, a simulation study is conducted to assess the finite sample performance of the proposed method, and the results indicate that it works well in practice. Finally, the approach is applied to a set of real data on childhood mortality taken from the Nigeria Demographic and Health Survey. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

This paper discusses variable selection for interval-censored failure time data with a cured fraction using a penalized likelihood function. The proposed method employs a sieve approach and a penalized EM algorithm, with asymptotic properties of the estimators obtained. Simulation studies and real data application demonstrate the good performance of the method in practice.