HERC Econometrics with Observational Data
Cost as the Dependent Variable (Part II)
NOTE: A newer version of this presentation is available at http://www.hsrd.research.va.gov/for_researchers/cyber_seminars/archives/video_archive.cfm?SessionID=1258.
Paul Barnett, PhD
Seminar date: 12/4/2013
Description: Health care cost can be difficult to analyze. In addition to skewness and truncation, the variance in cost data may be correlated with one of the predictor (independent) variables, a problem call heteroscedasticity. As a result of these problems, Ordinary Least Squares regression models may generate biased regression parameters and inaccurate predictions. Generalized Linear Models are an important alternative. A GLM includes a link function and a variance structure. These are identified using specific tests. Another alternative is a two-part model, which can be used to analyze data with many observations in which no cost was incurred. Non-parametric tests can be used to compare the cost incurred by two or more groups. Although they have the advantage of not requiring any assumptions about the statistical properties of the cost variable, they can be too conservative, and they do not allow the analyst to control for the effect of other factors.
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