Evaluation of Cerebrospinal Fluid Assay Variability in Alzheimer’s Disease

Article type: Research Article

Authors: White, Matthew T.^{a; b} | Shaw, Leslie M.^c | Xie, Sharon X.^{d; *} | for the Alzheimer’s Disease Neuroimaging Initiative¹ | and the National Alzheimer’s Coordinating Center¹

Affiliations: [a] The Clinical Research Center, Boston Children’s Hospital, Boston, MA, USA | [b] Department of Psychiatry, Harvard Medical School, Boston, MA, USA | [c] Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA | [d] Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA

Correspondence: [*] Correspondence to: Sharon X. Xie, Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA. Tel.: +215 573 3867; Fax: +215 573 4865; E-mail: sxie@mail.med.upenn.edu.

Note: [1] Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu) and the National Alzheimer’s Coordinating Center (NACC) database (http://www.alz.washington.edu). As such, the investigators within the ADNI and the NACC contributed to the design and implementation of ADNI/NACC and/or provided data but did not participate in the analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

Abstract: 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

DOI: 10.3233/JAD-151045

Journal: Journal of Alzheimer's Disease, vol. 51, no. 2, pp. 463-470, 2016