Generalized Modeling Approaches to Risk Adjustment of Skewed Outcomes Data
Willard G. Manning, Anirban Basu, John Mullahy
NBER Technical Working Paper No. 293
There are two broad classes of models used to address the econometric problems caused by skewness in data commonly encountered in health care applications: (1) transformation to deal with skewness (e.g., OLS on ln(y)); and (2) alternative weighting approaches based on exponential conditional models (ECM) and generalized linear model (GLM) approaches. In this paper, we encompass these two classes of models using the three parameter generalized gamma (GGM) distribution, which includes several of the standard alternatives as special cases OLS with a normal error, OLS for the log normal, the standard gamma and exponential with a log link, and the Weibull. Using simulation methods, we find the tests of identifying distributions to be robust. The GGM also provides a potentially more robust alternative estimator to the standard alternatives. An example using inpatient expenditures is also analyzed.
Published: Manning, Willard G., Anirban Basu and John Mullahy. "Generalized Modeling Approaches To Risk Adjustment Of Skewed Outcomes Data," Journal of Health Economics, 2005, v24(3,May), 465-488.