Zhao Xiaobing, Xia Yufeng, Xu Xuan
School of Data Sciences, Zhejiang University of Finance and Economics, Hangzhou, Zhejiang Province, China.
Asset and Laboratory Management Office, Zhejiang University of Finance and Economics, Hangzhou, Zhejiang Province, China.
PLoS One. 2025 May 13;20(5):e0321796. doi: 10.1371/journal.pone.0321796. eCollection 2025.
Modeling medical costs is a crucial task in health economics, especially when high-dimensional covariates and nonlinear effects are present. In this study, we propose a partially nonlinear index model (PNIM) that integrates partially sufficient dimension reduction with a rapid instrumental variable pilot estimation method. Through simulations, we demonstrate that the proposed model excels at capturing significant nonlinear relationships. When applying the model to the Medical Expenditure Panel Survey (MEPS) dataset, we identify important nonlinear age effects on medical costs and highlight key factors such as hospitalization, cardiovascular diseases, and supplemental insurance coverage. These findings provide valuable insights for healthcare policy, including targeted interventions for specific age groups and enhanced management of chronic conditions. Overall, the proposed method offers a flexible and computationally efficient framework for analyzing complex medical cost data, with broad applicability in health economics.
对医疗成本进行建模是健康经济学中的一项关键任务，尤其是在存在高维协变量和非线性效应的情况下。在本研究中，我们提出了一种部分非线性指数模型（PNIM），该模型将部分充分降维与快速工具变量试点估计方法相结合。通过模拟，我们证明所提出的模型在捕捉显著的非线性关系方面表现出色。将该模型应用于医疗支出面板调查（MEPS）数据集时，我们确定了年龄对医疗成本的重要非线性影响，并突出了住院、心血管疾病和补充保险覆盖等关键因素。这些发现为医疗保健政策提供了有价值的见解，包括针对特定年龄组的有针对性干预措施以及加强慢性病管理。总体而言，所提出的方法为分析复杂的医疗成本数据提供了一个灵活且计算高效的框架，在健康经济学中具有广泛的适用性。
