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Generalized Modeling Approaches to Risk Adjustment of Skewed Outcomes Data

Author

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  • Willard G. Manning
  • Anirban Basu
  • John Mullahy

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

  • Willard G. Manning & Anirban Basu & John Mullahy, 2003. "Generalized Modeling Approaches to Risk Adjustment of Skewed Outcomes Data," Working Papers 0313, Harris School of Public Policy Studies, University of Chicago.
    • Handle: RePEc:har:wpaper:0313
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    JEL classification:

    • I1 - Health, Education, and Welfare - - Health

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