HERC Econometrics with Observational Data
Cost as the Dependent Variable (Part I)
NOTE: A newer version of this presentation is available at http://www.hsrd.research.va.gov/for_researchers/cyber_seminars/archives/video_archive.cfm?SessionID=1257.
Paul Barnett, PhD
Seminar date: 11/20/2013
Description: Statistical analysis of health care cost is made difficult by two data problems. Some patients incur disproportionate costs, a statistical property called skewness. Other patients incur no cost at all; the distribution is truncated. As a result of these problems, it is rarely a good idea to analyze cost using the classic linear statistical model, ordinary least squares (OLS). Transforming cost by the taking its log results in a variables this is more normally distributed, allowing use of an OLS regression. The parameters from this regression have a natural interpretation as the proportionate effect of a unit change in the independent variable on cost. Care must be used when predicting costs from a model based on the log of costs. Log models have other limitations. The most important of these is that they should not be used when there are many zero cost observations in the data.
We're sorry - this video format is no longer supported. You may still download the resources below.
Request PDF Handout |
Audio only (mp3) |