Department of Health Sciences, University of Leicester, Leicester, UK.
Department of Health Sciences, University of Leicester, Leicester, UK.
J Clin Epidemiol. 2018 Jul;99:64-74. doi: 10.1016/j.jclinepi.2018.03.005. Epub 2018 Mar 13.
Network meta-analyses (NMA) have extensively been used to compare the effectiveness of multiple interventions for health care policy and decision-making. However, methods for evaluating the performance of multiple diagnostic tests are less established. In a decision-making context, we are often interested in comparing and ranking the performance of multiple diagnostic tests, at varying levels of test thresholds, in one simultaneous analysis.
Motivated by an example of cognitive impairment diagnosis following stroke, we synthesized data from 13 studies assessing the efficiency of two diagnostic tests: Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA), at two test thresholds: MMSE <25/30 and <27/30, and MoCA <22/30 and <26/30. Using Markov chain Monte Carlo (MCMC) methods, we fitted a bivariate network meta-analysis model incorporating constraints on increasing test threshold, and accounting for the correlations between multiple test accuracy measures from the same study.
We developed and successfully fitted a model comparing multiple tests/threshold combinations while imposing threshold constraints. Using this model, we found that MoCA at threshold <26/30 appeared to have the best true positive rate, whereas MMSE at threshold <25/30 appeared to have the best true negative rate.
The combined analysis of multiple tests at multiple thresholds allowed for more rigorous comparisons between competing diagnostics tests for decision making.
网络荟萃分析（NMA）已广泛用于比较多种干预措施在医疗保健政策和决策中的效果。然而，评估多种诊断测试性能的方法尚未确定。在决策背景下，我们通常有兴趣在一次同时分析中比较和排名多种诊断测试在不同测试阈值水平下的性能。
受卒中后认知障碍诊断的示例启发，我们综合了 13 项研究的数据，评估了两种诊断测试的效率：简易精神状态检查（MMSE）和蒙特利尔认知评估（MoCA），在两个测试阈值下：MMSE<25/30 和 <27/30，MoCA<22/30 和 <26/30。使用马尔可夫链蒙特卡罗（MCMC）方法，我们拟合了一个双变量网络荟萃分析模型，纳入了对测试阈值增加的约束，并考虑了同一研究中多个测试准确性测量值之间的相关性。
我们开发并成功拟合了一个模型，用于比较多个测试/阈值组合，同时施加阈值约束。使用该模型，我们发现阈值<26/30 时的 MoCA 似乎具有最佳的真阳性率，而阈值<25/30 时的 MMSE 似乎具有最佳的真阴性率。
对多个阈值的多个测试进行联合分析，为决策中竞争诊断测试之间的更严格比较提供了可能。
