A Class of Weighted Estimating Equations for Semiparametric Transformation Models with Missing Covariates

摘要

In survival analysis, covariate measurements often contain missing observations; ignoring this feature can lead to invalid inference. We propose a class of weighted estimating equations for right-censored data with missing covariates under semiparametric transformation models. Time-specific and subject-specific weights are accommodated in the formulation of the weighted estimating equations. We establish unified results for estimating missingness probabilities that cover both parametric and non-parametric modelling schemes. To improve estimation efficiency, the weighted estimating equations are augmented by a new set of unbiased estimating equations. The resultant estimator has the so-called double robustness' property and is optimal within a class of consistent estimators.