Abstract
Background: 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.
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-β 1 - 42, Total tau, and P-tau181p) 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.
