Correcting for the Absence of a Gold Standard Improves Diagnostic Accuracy of Biomarkers in Alzheimer's Disease

Abstract

Background: Studies investigating the diagnostic accuracy of biomarkers for Alzheimer's disease (AD) are typically performed using the clinical diagnosis or amyloid-beta 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. Objective: To determine the diagnostic accuracy of AD biomarkers while taking the imperfectness of the reference test into account. Methods: 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-beta(1-42), Total tau, and P-tau(181p)) by including the clinical diagnosis as imperfect reference test into the analysis. Results: 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. Conclusions: 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.