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A Flexible Model for the Mean and Variance Functions with Application to Medical Cost Data

机译:均值和方差函数的灵活模型应用于医疗费用数据

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摘要

Medical cost data are often skewed to the right and heteroscedastic, having a nonlinear relation with covariates. To tackle these issues, we consider an extension to generalized linear models by assuming nonlinear associations of covariates in the mean function and allowing the variance to be an unknown but smooth function of the mean. We make no further assumption on the distributional form. The unknown functions are described by penalized splines, and the estimation is carried out using nonparametric quasi-likelihood. Simulation studies show the flexibility and advantages of our approach. We apply the model to the annual medical costs of heart failure patients in the clinical data repository (CDR) at the University of Virginia Hospital System.
机译:医疗费用数据通常偏向右侧且是异方差的,与协变量具有非线性关系。为了解决这些问题,我们考虑通过假设均值函数中协变量的非线性关联并使方差成为均值的未知但平滑函数来扩展到广义线性模型。我们对分布形式不做进一步的假设。用罚样条描述未知函数,并使用非参数拟似然来进行估计。仿真研究表明了我们方法的灵活性和优势。我们将该模型应用于弗吉尼亚大学医院系统临床数据存储库(CDR)中的心力衰竭患者的年度医疗费用。

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