title: Failure time regression with continuous informative auxiliary covariates.
abstract: In this paper we use Cox's regression model to fit failure time data with continuous informative auxiliary variables in the presence of a validation subsample. We first estimate the induced relative risk function by kernel smoothing based on the validation subsample, and then improve the estimation by utilizing the information on the incomplete observations from non-validation subsample and the auxiliary observations from the primary sample. Asymptotic normality of the proposed estimator is derived. The proposed method allows one to robustly model the failure time data with an informative multivariate auxiliary covariate. Comparison of the proposed approach with several existing methods is made via simulations. Two real datasets are analyzed to illustrate the proposed method.
authors.affiliation: Department of Mathematics and Statistics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.
authors.forename: Lipika
authors.initials: L
authors.lastname: Ghosh
authors.affiliation: Department of Mathematics and Statistics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.
authors.forename: Jiancheng
authors.initials: J
authors.lastname: Jiang
authors.affiliation: Department of Mathematics and Statistics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.
authors.forename: Yanqing
authors.initials: Y
authors.lastname: Sun
authors.affiliation: Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
authors.forename: Haibo
authors.initials: H
authors.lastname: Zhou
keywords: Auxiliary covariates; Censoring; Estimated partial likelihood; Local linear smoothing; Validation