Estimating Mean Cost Using Auxiliary Covariates

Summary We study the estimation of mean medical cost when censoring is dependent and a large amount of auxiliary information is present. Under missing at random assumption, we propose semiparametric working models to obtain low‐dimensional summarized scores. An estimator for the mean total cost can be derived nonparametrically conditional on the summarized scores. We show that when either the two working models for cost‐survival process or the model for censoring distribution is correct, the estimator is consistent and asymptotically normal. Small‐sample performance of the proposed method is evaluated via simulation studies. Finally, our approach is applied to analyze a real data set in health economics.