Chen Jinsong, Liu Lei, Zhang Daowen, Shih Ya-Chen T
Department of Preventive Medicine, Northwestern University, Chicago, IL, USA.
Stat Med. 2013 Oct 30;32(24):4306-18. doi: 10.1002/sim.5838. Epub 2013 May 13.
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 at the University of Virginia Hospital System.
医疗成本数据往往向右偏态且具有异方差性，与协变量存在非线性关系。为解决这些问题，我们考虑对广义线性模型进行扩展，通过假设均值函数中协变量的非线性关联，并允许方差是均值的未知但平滑的函数。我们不对分布形式做进一步假设。未知函数用惩罚样条来描述，估计通过非参数拟似然法进行。模拟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中心力衰竭患者的年度医疗成本。
