论文标题
通过Beta回归模型分析处方药利用
Analysis of Prescription Drug Utilization with Beta Regression Models
论文作者
论文摘要
美国的医疗保健部门很复杂，也是该国约20％的国内生产总值的大型部门。研究人员和从业人员已经使用了医疗保健分析来更好地了解该行业。在本文中，我们研究并证明了使用Beta回归模型来研究美国品牌药物的利用，以了解不同领域的品牌名称药物利用的可变性。这些模型适用于从医疗保险和医疗补助服务和国税局获得的公共数据集。集成的嵌套拉普拉斯近似（INLA）用于执行推理。数值结果表明，Beta回归模型可以很好地符合品牌药物索赔率，包括空间依赖性可改善Beta回归模型的性能。此类模型可以用来反映处方药利用的效果，因为在风险评分模型中更新被保险人的健康风险。
The healthcare sector in the U.S. is complex and is also a large sector that generates about 20% of the country's gross domestic product. Healthcare analytics has been used by researchers and practitioners to better understand the industry. In this paper, we examine and demonstrate the use of Beta regression models to study the utilization of brand name drugs in the U.S. to understand the variability of brand name drug utilization across different areas. The models are fitted to public datasets obtained from the Medicare & Medicaid Services and the Internal Revenue Service. Integrated Nested Laplace Approximation (INLA) is used to perform the inference. The numerical results show that Beta regression models can fit the brand name drug claim rates well and including spatial dependence improves the performance of the Beta regression models. Such models can be used to reflect the effect of prescription drug utilization when updating an insured's health risk in a risk scoring model.
