Department of Mathematics and Statistics, Georgia State University, Atlanta, Georgia, USA.
Biom J. 2023 Mar;65(3):e2200021. doi: 10.1002/bimj.202200021. Epub 2023 Jan 15.
In practice, a disease process might involve three ordinal diagnostic stages: the normal healthy stage, the early stage of the disease, and the stage of full development of the disease. Early detection is critical for some diseases since it often means an optimal time window for therapeutic treatments of the diseases. In this study, we propose a new influence function-based empirical likelihood method and Bayesian empirical likelihood methods to construct confidence/credible intervals for the sensitivity of a test to patients in the early diseased stage given a specificity and a sensitivity of the test to patients in the fully diseased stage. Numerical studies are performed to compare the finite sample performances of the proposed approaches with existing methods. The proposed methods are shown to outperform existing methods in terms of coverage probability. A real dataset from the Alzheimer's Disease Neuroimaging Initiative (ANDI) is used to illustrate the proposed methods.
实际上，疾病过程可能涉及三个有序的诊断阶段：正常健康阶段、疾病早期阶段和疾病完全发展阶段。早期检测对于某些疾病至关重要，因为它通常意味着治疗疾病的最佳时间窗口。在这项研究中，我们提出了一种新的基于影响函数的经验似然方法和贝叶斯经验似然方法，以构建在特定性和测试对完全患病阶段患者的敏感性的情况下，测试对早期患病阶段患者的敏感性的置信/可信区间。进行了数值研究以比较所提出的方法与现有方法的有限样本性能。所提出的方法在覆盖概率方面表现优于现有方法。使用来自阿尔茨海默病神经影像学倡议 (ANDI) 的真实数据集来说明所提出的方法。
