title: Weighted quantile regression for analyzing health care cost data with missing covariates.
abstract: Analysis of health care cost data is often complicated by a high level of skewness, heteroscedastic variances and the presence of missing data. Most of the existing literature on cost data analysis have been focused on modeling the conditional mean. In this paper, we study a weighted quantile regression approach for estimating the conditional quantiles health care cost data with missing covariates. The weighted quantile regression estimator is consistent, unlike the naive estimator, and asymptotically normal. Furthermore, we propose a modified BIC for variable selection in quantile regression when the covariates are missing at random. The quantile regression framework allows us to obtain a more complete picture of the effects of the covariates on the health care cost and is naturally adapted to the skewness and heterogeneity of the cost data. The method is semiparametric in the sense that it does not require to specify the likelihood function for the random error or the covariates. We investigate the weighted quantile regression procedure and the modified BIC via extensive simulations. We illustrate the application by analyzing a real data set from a health care cost study.
authors.affiliation: School of Statistics, University of Minnesota, 313 Ford Hall, 224 Church St SE, Minneapolis, MN 55455, U.S.A.
authors.forename: Ben
authors.initials: B
authors.lastname: Sherwood
authors.forename: Lan
authors.initials: L
authors.lastname: Wang
authors.forename: Xiao-Hua
authors.initials: XH
authors.lastname: Zhou
mesh_terms: D003198:Computer Simulation; D005260:Female; D017048:Health Care Costs / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D010595:Pharmacists; D012044:Regression Analysis
keywords: health care cost data; inverse probability weighting; missing data; quantile regression