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

Yang Ning, Grace Yi and Nancy Reid

Scandinavian Journal of Statistics, 2018, vol. 45, issue 1, 87-109

Abstract: 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.

Date: 2018
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