2021年6月11日，我院数据科学系叶鹏副教授在国际权威期刊《SCIENCE CHINA Mathematics》上发表论文《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-specifific and subject-specifific weights are incorporated into the formulation of weighted estimating equations. Unifified 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.