A Class of Weighted Estimating Equations for Semiparametric Transformation Models with Missing Covariates. (26th July 2017)

• Record Type: Journal Article
• Authors: Ning, Yang; Yi, Grace; Reid, Nancy
• Is Part Of: Scandinavian journal of statistics. Volume 45:Number 1(2018:Mar.)
• Page Start: 87
• Page End: 109
• Publication Date: 2017-07-26
• Subjects: augmented weighted estimating equations -- missing at random -- right censoring -- survival data -- transformation models; Statistics -- Periodicals; 310
• DOI: 10.1111/sjos.12289
• Languages: English
• ISSNs: 0303-6898

Abstract

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.