title: A flexible model for the mean and variance functions, with application to medical cost data.
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.
authors.affiliation: Department of Preventive Medicine, Northwestern University, Chicago, IL, USA.
authors.forename: Jinsong
authors.initials: J
authors.lastname: Chen
authors.forename: Lei
authors.initials: L
authors.lastname: Liu
authors.forename: Daowen
authors.initials: D
authors.lastname: Zhang
authors.forename: Ya-Chen T
authors.initials: YC
authors.lastname: Shih
mesh_terms: D003198:Computer Simulation; D003695:Delivery of Health Care / Q000191:economics*; D006333:Heart Failure / Q000191:economics; D006801:Humans; D016013:Likelihood Functions; D018803:Models, Economic; D015233:Models, Statistical; D014768:Virginia
keywords: generalized cross-validation; generalized linear model; health econometrics; semiparametric regression; smoothing parameter