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      Generalized modeling approaches to risk adjustment of skewed outcomes data.

      1 , ,
      Journal of health economics
      Elsevier BV

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          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., 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.

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          Author and article information

          Journal
          J Health Econ
          Journal of health economics
          Elsevier BV
          0167-6296
          0167-6296
          May 2005
          : 24
          : 3
          Affiliations
          [1 ] Harris School of Public Policy Studies, The University of Chicago, IL 60637, USA. w-manning@uchicago.edu
          Article
          S0167-6296(05)00005-6
          10.1016/j.jhealeco.2004.09.011
          15811539
          4f4d3e02-7a6a-4de9-b0ab-5a9153b42153
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