Generalized modeling approaches to risk adjustment of skewed outcomes data.
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., ordinary least square (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.
Manning
Willard G
WG
Harris School of Public Policy Studies, The University of Chicago, IL 60637, USA. w-manning@uchicago.edu
Basu
Anirban
A
Mullahy
John
J
D015233:Models, Statistical; D020379:Risk Adjustment / Q000379:methods* / Q000706:statistics & numerical data; D014481:United States