Coart Els, Barrado Leandro García, Duits Flora H, Scheltens Philip, van der Flier Wiesje M, Teunissen Charlotte E, van der Vies Saskia M, Burzykowski Tomasz
International Drug Development Institute (IDDI), Louvain-la-Neuve, Belgium.
Interuniversity Institute for Biostatistics and statistical Bioinformatics (I-BioStat), Hasselt University, Diepenbeek, Belgium.
J Alzheimers Dis. 2015;46(4):889-99. doi: 10.3233/JAD-142886.
Studies investigating the diagnostic accuracy of biomarkers for Alzheimer's disease (AD) are typically performed using the clinical diagnosis or amyloid-β positron emission tomography as the reference test. However, neither can be considered a gold standard or a perfect reference test for AD. Not accounting for errors in the reference test is known to cause bias in the diagnostic accuracy of biomarkers.
To determine the diagnostic accuracy of AD biomarkers while taking the imperfectness of the reference test into account.
To determine the diagnostic accuracy of AD biomarkers and taking the imperfectness of the reference test into account, we have developed a Bayesian method. This method establishes the biomarkers' true value in predicting the AD-pathology status by combining the reference test and the biomarker data with available information on the reliability of the reference test. The new methodology was applied to two clinical datasets to establish the joint accuracy of three cerebrospinal fluid biomarkers (amyloid-β 1 - 42, Total tau, and P-tau181p) by including the clinical diagnosis as imperfect reference test into the analysis.
The area under the receiver-operating-characteristics curve to discriminate between AD and controls, increases from 0.949 (with 95% credible interval [0.935,0.960]) to 0.990 ([0.985,0.995]) and from 0.870 ([0.817,0.912]) to 0.975 ([0.943,0.990]) for the cohorts, respectively.
Use of the Bayesian methodology enables an improved estimate of the exact diagnostic value of AD biomarkers and overcomes the lack of a gold standard for AD. Using the new method will increase the diagnostic confidence for early stages of AD.
研究阿尔茨海默病（AD）生物标志物诊断准确性的研究通常使用临床诊断或淀粉样β正电子发射断层扫描作为参考测试。然而，这两者都不能被视为AD的金标准或完美参考测试。已知不考虑参考测试中的误差会导致生物标志物诊断准确性出现偏差。
在考虑参考测试不完美性的情况下确定AD生物标志物的诊断准确性。
为了确定AD生物标志物的诊断准确性并考虑参考测试的不完美性，我们开发了一种贝叶斯方法。该方法通过将参考测试和生物标志物数据与参考测试可靠性的可用信息相结合，来确定生物标志物在预测AD病理状态方面的真实价值。将这种新方法应用于两个临床数据集，通过将临床诊断作为不完美参考测试纳入分析，来确定三种脑脊液生物标志物（淀粉样β1 - 42、总tau蛋白和磷酸化tau蛋白181p）的联合准确性。
在区分AD与对照时，受试者工作特征曲线下面积对于各队列分别从0.949（95%可信区间[0.935,0.960]）增加到0.990（[0.985,0.995]），以及从0.870（[0.817,0.912]）增加到0.975（[0.943,0.990]）。
使用贝叶斯方法能够更好地估计AD生物标志物的确切诊断价值，并克服了AD缺乏金标准的问题。使用新方法将提高AD早期阶段的诊断信心。
