Determining cut-points for Alzheimer's disease biomarkers: statistical issues, methods and challenges.
New proposed criteria for the clinical diagnosis of Alzheimer's disease increasingly incorporate biomarkers, most of which are normally measured on a continuous scale. Operationalizing such criteria thus requires continuous biomarkers to be dichotomized, which in turns requires the selection of a cut-point at which to dichotomize. In this article, we review the statistical principles underlying the choice of cut-points, describe some of the most commonly adopted statistical approaches used to estimate cut-points, highlight potential pitfalls in some of the approaches and characterize in what sense the estimated cut-point from each approach is optimal. We also emphasize that how a cut-point is selected must be made in reference to how the resulting dichotomized biomarker is to be used, and in particular what actions will follow from a positive or negative test result.
Centre for Statistical Methodology, London School of Hygiene & Tropical Medicine, Keppel Street, London, UK.
Jonathan W
JW
Bartlett
Chris
C
Frost
Niklas
N
Mattsson
Tobias
T
Skillbäck
Kaj
K
Blennow
Henrik
H
Zetterberg
Jonathan M
JM
Schott
D000544:Alzheimer Disease / Q000175:diagnosis* / Q000378:metabolism / Q000473:pathology; D016229:Amyloid beta-Peptides / Q000134:cerebrospinal fluid; D015415:Biomarkers / Q000134:cerebrospinal fluid; D006801:Humans; D016013:Likelihood Functions; D008279:Magnetic Resonance Imaging; D010446:Peptide Fragments / Q000134:cerebrospinal fluid; D049268:Positron-Emission Tomography; D012372:ROC Curve; D035501:Uncertainty
D016229:Amyloid beta-Peptides; D015415:Biomarkers; D010446:Peptide Fragments; C075222:amyloid beta-protein (1-42)