2013 journal article

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

STATISTICS IN MEDICINE, 32(24), 4306–4318.

By: J. Chen *, L. Liu *, D. Zhang n & Y. Shih *

author keywords: generalized linear model; semiparametric regression; health econometrics; smoothing parameter; generalized cross-validation
MeSH headings : Computer Simulation; Delivery of Health Care / economics; Heart Failure / economics; Humans; Likelihood Functions; Models, Economic; Models, Statistical; Virginia
TL;DR: This work considers 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. (via Semantic Scholar)
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Added: August 6, 2018

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. Copyright © 2013 John Wiley & Sons, Ltd.