Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University, Washington, DC 20057, USA.
Department of Oncology, Georgetown University, Washington, DC 20057, USA.
Int J Environ Res Public Health. 2024 Feb 10;21(2):208. doi: 10.3390/ijerph21020208.
Health disparities are differences in health status across different socioeconomic groups. Classical methods, e.g., the Delta method, have been used to estimate the standard errors of estimated measures of health disparities and to construct confidence intervals for these measures. However, the confidence intervals constructed using the classical methods do not have good coverage properties for situations involving sparse data. In this article, we introduce three new methods to construct fiducial intervals for measures of health disparities based on approximate fiducial quantities. Through a comprehensive simulation study, We compare the empirical coverage properties of the proposed fiducial intervals against two Monte Carlo simulation-based methods-utilizing either a truncated Normal distribution or the Gamma distribution-as well as the classical method. The findings of the simulation study advocate for the adoption of the Monte Carlo simulation-based method with the Gamma distribution when a unified approach is sought for all health disparity measures.
健康差异是指不同社会经济群体之间健康状况的差异。经典方法，如Delta 方法，已被用于估计健康差异估计量的标准误差，并为这些估计量构建置信区间。然而，对于涉及稀疏数据的情况，使用经典方法构建的置信区间的覆盖性质并不理想。在本文中，我们介绍了三种新的方法，基于近似的基准量来构建健康差异衡量指标的基准区间。通过全面的模拟研究，我们将提出的基准区间的经验覆盖性质与两种基于蒙特卡罗模拟的方法进行了比较——一种是利用截断正态分布，另一种是利用伽马分布——以及经典方法。模拟研究的结果表明，当寻求所有健康差异衡量指标的统一方法时，应该采用基于伽马分布的蒙特卡罗模拟方法。
