Weighted quantile regression for analyzing health care cost data with missing covariates

Ben Sherwood, Lan Wang, Xiao Hua Zhou

Research output: Contribution to journalArticle

34 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)4967-4979
Number of pages13
JournalStatistics in Medicine
Volume32
Issue number28
DOIs
StatePublished - Dec 10 2013

Keywords

  • Health care cost data
  • Inverse probability weighting
  • Missing data
  • Quantile regression

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