A quasi-Monte-Carlo comparison of parametric and semiparametric regression methods for heavy-tailed and non-normal data: an application to healthcare costs
A quasi-Monte-Carlo comparison of parametric and semiparametric regression methods for heavy-tailed and non-normal data: an application to healthcare costs
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DOI：
10.1111/rssa.12141
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发表时间：
2016-10-01
影响因子：
2
通讯作者：
Rice, Nigel
中科院分区：
文献类型：
--
作者：
Jones, Andrew M.;Lomas, James;Rice, Nigel
我们对医疗费用的参数和半参数回归方法的最新发展进行了准蒙特卡罗比较，既针对彼此，也针对标准实践。2007-2008财政年度英国国家卫生服务医院住院病人的人口（每个病人的总和）被随机分为两个大小相等的亚群，以形成一个估计集和一个验证集。使用验证集评估样本外，条件密度近似估计器在预测条件均值方面显示出相当大的希望，在预测准确性方面表现最好，在偏差和拟合优度方面表现最好。表现最好的偏差模型是具有平方根变换因变量的线性回归模型，而具有平方根链接函数和泊松分布的广义线性模型在拟合优度方面表现最好。与我们比较中考虑的其他模型相比，使用日志链接的常用模型表现较差。
We conduct a quasi-Monte-Carlo comparison of the recent developments in parametric and semiparametric regression methods for healthcare costs, both against each other and against standard practice. The population of English National Health Service hospital in-patient episodes for the financial year 2007-2008 (summed for each patient) is randomly divided into two equally sized subpopulations to form an estimation set and a validation set. Evaluating out-of-sample using the validation set, a conditional density approximation estimator shows considerable promise in forecasting conditional means, performing best for accuracy of forecasting and among the best four for bias and goodness of fit. The best performing model for bias is linear regression with square-root-transformed dependent variables, whereas a generalized linear model with square-root link function and Poisson distribution performs best in terms of goodness of fit. Commonly used models utilizing a log-link are shown to perform badly relative to other models considered in our comparison.