Estimation in a general semiparametric hazards regression model with missing covariates

In survival analysis, missing observations are often encountered in covariate measurements, and ignoring this feature may make an invalid inference. In this article, we consider a general semiparametric hazards regression model for right-censored data with some covariates missing at random. The covariate effects in this model are characterized by a time-scale change and a relative hazard ratio. A class of weighted estimators are proposed, and the resulting estimators are shown to be consistent and asymptotically normal. Furthermore, fully augmented weighted estimators are also studied to improve estimation efficiency. Simulation studies demonstrate that the proposed estimators perform well in a finite sample. An application to the mouse leukemia data is provided.

Automated summary

In this paper, a general semi-parametric hazards regression model is proposed to deal with missing covariate observations in survival analysis. Weighted estimators and fully augmented weighted estimators are introduced and shown to be consistent and asymptotically normal. Simulation studies and application to leukemia data demonstrate the effectiveness of the proposed methods.