Generalized modeling approaches to risk adjustment of skewed outcomes data
Generalized modeling approaches to risk adjustment of skewed outcomes data
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DOI：
10.1016/j.jhealeco.2004.09.011
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发表时间：
2005-05-01
影响因子：
3.5
通讯作者：
Mullahy, J
中科院分区：
文献类型：
--
作者：
Manning, WG;Basu, A;Mullahy, J
有两大类模型用于解决在医疗保健应用中通常遇到的由数据中的偏度引起的计量经济学问题：（1）处理偏度的变换（例如，In（y）上的普通最小二乘（OLS））;以及（2）基于指数条件模型（ECM）和广义线性模型（GLM）方法的替代加权方法。在本文中，我们涵盖了这两类模型使用三参数广义伽玛（GGM）分布，其中包括几个标准的替代品作为特殊情况-OLS与正态误差，OLS的对数正态，标准伽玛和指数与对数链接，和威布尔。使用模拟方法，我们发现识别分布的测试是稳健的。GGM还提供了一个潜在的更强大的替代估计标准的替代品。还分析了一个使用住院费用的例子。(c)2005 Elsevier B. V.保留所有权利。
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 In(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. (c) 2005 Elsevier B.V. All rights reserved.