Some New Models for Predicting Health Care Costs of Individual Patients

Xiao-Hua Andrew Zhou, PhD
Professor, Department of Biostatistics, University of Washington
Director and Research Career Scientist, Biostatistics Unit, VA Puget Sound Health Care System
*Candidate for Division Head in the Division of Biostatistics

Monday, April 6th
3:30pm
MoosT 1-450
Minneapolis Campus

Abstract:

The rising cost of health care is one of the most important problems facing the United States. Accurately predicting such costs is an important first step in addressing this problem. However, due to some special distributional features of health care costs, including high skewness, presence of excessive zero values, and heteroscedasticity, it is difficult to obtain an accurate prediction of health care costs for patients.

In this talk, I will describe some new models for using covariates to predict the expected health care costs for patients. These new models include: (1) a parametric heteroscedastic transformation model, (2) a semi-parametric two-part heteroscedastic transformation model, (3) a quantile regression model, (4) a non-parametric heteroscedastic transformation regression model, and (4) a semi-parametric two-part mixed-effects heteroscedastic transformation model.

A social tea will be held at 2:45 P.M. in A434 Mayo. All are Welcome.
For more details contact 612-624-4655 or see http://www.biostat.umn.edu/seminar_academic.html