A flexible model for the mean and variance functions, with application to medical cost data

Jinsong Chen, Lei Liu*, Daowen Zhang, Ya Chen T. Shih

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

Medical cost data are often skewed to the right and heteroscedastic, having a nonlinear relation with covariates. To tackle these issues, we consider an extension to generalized linear models by assuming nonlinear associations of covariates in the mean function and allowing the variance to be an unknown but smooth function of the mean. We make no further assumption on the distributional form. The unknown functions are described by penalized splines, and the estimation is carried out using nonparametric quasi-likelihood. Simulation studies show the flexibility and advantages of our approach. We apply the model to the annual medical costs of heart failure patients in the clinical data repository at the University of Virginia Hospital System.

Original languageEnglish (US)
Pages (from-to)4306-4318
Number of pages13
JournalStatistics in Medicine
Volume32
Issue number24
DOIs
StatePublished - Oct 30 2013

Keywords

  • Generalized cross-validation
  • Generalized linear model
  • Health econometrics
  • Semiparametric regression
  • Smoothing parameter

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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