HERC Econometrics with Observational Data

Cost as the Dependent Variable

by Mark Bounthavong, PhD, PharmD, MPH
Seminar date: 4/26/2023

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. Other models such as generalized linear models (GLM) are useful alternatives. 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. We’ll review these approaches and identify some good practices for analyzing cost data.

Target Audience: Researchers who would like an introduction to econometric methods for observational studies in health services research. Seminar material will assume knowledge of basic probability and statistics and familiarity with linear regression.

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DOWNLOAD: PDF handout | Audio only (mp3) | transcript

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