title: Estimating mean cost using auxiliary covariates.
abstract: 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.
authors.affiliation: Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina 27705, USA. wendy.pan@duke.edu
authors.forename: Wenqin
authors.initials: W
authors.lastname: Pan
authors.forename: Donglin
authors.initials: D
authors.lastname: Zeng
mesh_terms: D003198:Computer Simulation; D003365:Costs and Cost Analysis / Q000379:methods / Q000706:statistics & numerical data*; D017048:Health Care Costs / Q000706:statistics & numerical data*; D008722:Methods; D015233:Models, Statistical; D016008:Statistical Distributions