A class of weighted estimating equations for additive hazard models with covariates missing at random

Missing covariate data arise frequently in biomedical studies. In this article, we propose a class of weighted estimating equations for the additive hazard regression model when some of the covariates are missing at random. Time-specific and subject-specific weights are incorporated into the formulation of weighted estimating equations. Unified results are established for estimating selection probabilities that cover both parametric and non-parametric modeling schemes. The resulting estimators have closed forms and are shown to be consistent and asymptotically normal. Simulation studies indicate that the proposed estimators perform well for practical settings. An application to a mouse leukemia study is illustrated.

Automated summary

In this article, a class of weighted estimating equations is proposed for handling missing covariate data in biomedical studies. The approach effectively addresses the estimation of selection probabilities in both parametric and non-parametric modeling schemes.