Studies of cerebrospinal fluid (CSF) biomarkers in Alzheimer's disease (AD) have indicated that much of the variability observed in the biomarkers may be due to measurement error. Biomarkers are often obtained with measurement error, which may make the diagnostic biomarker appear less effective than it truly is. In the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, technical replicates of CSF biomarkers are available; the National Alzheimer's Coordinating Center database contains longitudinal replicates of CSF biomarkers. We focus on the area under the receiver operating characteristic curve (AUC) as the measure of diagnostic effectiveness for differentiating AD from normal cognition using CSF biomarkers and compare AUC estimates obtained by a more standard, naïve method (which uses a single observation per subject and ignores measurement error) to a maximum likelihood (ML) based method (which uses all replicates per subject and adjusts for measurement error). The choice of analysis method depends upon the noise to signal ratio (i.e., the magnitude of the measurement error variability relative to the true biomarker variability); moderate to high ratios may significantly bias the naïve AUC estimate, and the ML-based method would be preferred. The noise to signal ratios were low for the ADNI biomarkers but high for the tTau and pTau biomarkers in NACC. Correspondingly, the naïve and ML-based AUC estimates were nearly identical in the ADNI data but dissimilar for the tTau and pTau biomarkers in the NACC data. Therefore, using the naïve method is adequate for analysis of CSF biomarkers in the ADNI study, but the ML method is recommended for the NACC data.
Keywords: Alzheimer’s disease; biomarkers; diagnostic testing; maximum likelihood; measurement error; replicate data.