Abstract
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.
Original language | English |
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Pages (from-to) | 391-400 |
Number of pages | 10 |
Journal | Biomarkers in Medicine |
Volume | 6 |
Issue number | 4 |
Early online date | 23 Aug 2012 |
DOIs | |
Publication status | Published - Aug 2012 |
Keywords
- Alzheimer Disease/diagnosis
- Amyloid beta-Peptides/cerebrospinal fluid
- Biomarkers/cerebrospinal fluid
- Humans
- Likelihood Functions
- Magnetic Resonance Imaging
- Peptide Fragments/cerebrospinal fluid
- Positron-Emission Tomography
- ROC Curve
- Uncertainty