College of Public Health, University of South Florida, Tampa, Florida, USA.
J Biopharm Stat. 2024 Mar;34(2):260-275. doi: 10.1080/10543406.2023.2188925. Epub 2023 Mar 20.
Statistical methods have been well developed for comparing the predictive values of two binary diagnostic tests under a paired design. However, existing methods do not make allowance for incomplete data. Although maximum likelihood based method can be used to deal with incomplete data, it requires iterative algorithm for implementation. A simple and easily implemented statistical method is therefore needed. Simple methods exist for comparing two sensitivities or specificities with incomplete data but such simple methods are not available for comparing two predictive values with incomplete data. In this paper, we propose two simple methods for comparing two predictive values with incomplete data. The test statistics derived by these two methods are simple to compute, only involving some minor modification of the existing weighted generalized score statistics with complete data. Simulation results demonstrate that the proposed methods are more efficient than the ad-hoc method that only uses the subjects wit complete data. As an illustration, the proposed methods are applied to an observational study comparing two non-invasive methods in detecting endometriosis.
统计方法已经得到了很好的发展，用于比较配对设计下两种二项诊断测试的预测值。然而，现有的方法并没有考虑不完整的数据。虽然基于最大似然的方法可以用于处理不完整数据，但它需要迭代算法来实现。因此，需要一种简单且易于实现的统计方法。对于不完整数据的比较，有简单的方法可以比较两种敏感性或特异性，但对于不完整数据的比较，没有简单的方法可以比较两种预测值。在本文中，我们提出了两种用于比较不完整数据的两种预测值的简单方法。这两种方法推导的检验统计量计算简单，只涉及对完整数据的加权广义得分统计量进行一些微小的修改。模拟结果表明，与仅使用完整数据的受试者的特定方法相比，所提出的方法更有效。作为说明，将所提出的方法应用于一项观察性研究，比较两种非侵入性方法检测子宫内膜异位症的效果。
