Generalized Modeling Approaches to Risk Adjustment of Skewed Outcomes Data

42 Pages Posted: 27 Oct 2003

See all articles by Willard G. Manning

Willard G. Manning

University of Chicago - Harris School of Public Policy

Anirban Basu

University of Chicago - Department of Medicine

John Mullahy

University of Wisconsin - Madison - Department of Population Health Sciences; National Bureau of Economic Research (NBER)

Date Written: October 2003

Abstract

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.

Suggested Citation

Manning, Willard G. and Basu, Anirban and Mullahy, John, Generalized Modeling Approaches to Risk Adjustment of Skewed Outcomes Data (October 2003). NBER Working Paper No. t0293. Available at SSRN: https://ssrn.com/abstract=459402

Willard G. Manning

University of Chicago - Harris School of Public Policy ( email )

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Anirban Basu

University of Chicago - Department of Medicine ( email )

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HOME PAGE: http://home.uchicago.edu/~abasu

John Mullahy (Contact Author)

University of Wisconsin - Madison - Department of Population Health Sciences ( email )

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National Bureau of Economic Research (NBER)

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