Determining cut-points for Alzheimers disease biomarkers: Statistical issues, methods and challenges

Biomarkers in Medicine

Volumn 6, Issue 4, 2012, Pages 391-400

  Bartlett, Jonathan W ^a,b   Frost, Chris ^a,b   Mattsson, Niklas ^c,d   Skillbäck, Tobias ^c   Blennow, Kaj ^c   Zetterberg, Henrik ^b,c,d   Schott, Jonathan M ^b  

^a LONDON SCHOOL OF HYGIENE AND TROPICAL MEDICINE   (United Kingdom)

^b UNIVERSITY COLLEGE LONDON   (United Kingdom)

^c UNIVERSITY OF GOTHENBURG   (Sweden)

^d SAHLGRENSKA UNIVERSITY HOSPITAL   (Sweden)

Author keywords

accuracy; biomarker; cerebrospinal fluid; dichotomizationitivity; specificity

Indexed keywords

AMYLOID BETA PROTEIN[1-42]; BIOLOGICAL MARKER; TAU PROTEIN;

ACCURACY; ALZHEIMER DISEASE; CEREBROSPINAL FLUID ANALYSIS; DRUG RESEARCH; FALSE NEGATIVE RESULT; FALSE POSITIVE RESULT; GOLD STANDARD; HUMAN; MILD COGNITIVE IMPAIRMENT; NORMAL VALUE; PREDICTIVE VALUE; PROTEIN ANALYSIS; PROTEIN CEREBROSPINAL FLUID LEVEL; PROTEIN EXPRESSION; PROTEIN PHOSPHORYLATION; QUANTITATIVE ANALYSIS; RECEIVER OPERATING CHARACTERISTIC; REFERENCE VALUE; REVIEW; SAMPLE SIZE; SENSITIVITY AND SPECIFICITY; STANDARDIZATION; STATISTICAL ANALYSIS; STATISTICAL MODEL; YOUDEN INDEX;

ALZHEIMER DISEASE; AMYLOID BETA-PEPTIDES; BIOLOGICAL MARKERS; HUMANS; LIKELIHOOD FUNCTIONS; MAGNETIC RESONANCE IMAGING; PEPTIDE FRAGMENTS; POSITRON-EMISSION TOMOGRAPHY; ROC CURVE; UNCERTAINTY;

EID: 84865502596     PISSN: 17520363     EISSN: 17520371     Source Type: Journal    
DOI: 10.2217/bmm.12.49     Document Type: Review

References (20)

1. Andreasen N, Minthon L, Davidsson P et al. Evaluation of CSF-tau and CSF-Abeta42 as diagnostic markers for Alzheimer disease in clinical practice. Arch. Neurol. 58(3), 373-379 (2001). (Pubitemid 32217453)
2. Shaw LM, Vanderstichele H, Knapik-Czajka M et al. Cerebrospinal fluid biomarker signature in Alzheimer's Disease Neuroimaging Initiative subjects. Ann. Neurol. 65, 403-413 (2009). Original paper that estimated cut-points for cerebrospinal fluid biomarkers to maximize accuracy, using data from autopsy-confirmed Alzheimer's disease (AD) subjects and normal controls.
3. Mattsson N, Zetterberg H, Hansson O et al. CSF biomarkers and incipient Alzheimer disease in patients with mild cognitive impairment. JAMA 302, 385-393 (2009). Original paper that estimated cut-points for cerebrospinal fluid biomarkers to give 85% sensitivity for AD, using data from AD subjects and controls.
4. Hansson O, Zetterberg H, Buchhave P et al. Association between CSF biomarkers and incipient Alzheimer's disease in patients with mild cognitive impairment: a follow-up study. Lancet Neurol. 5(3), 228-234 (2006). (Pubitemid 43238346)
5. Schott JM, Bartlett JW, Fox NC, Barnes J. Increased brain atrophy rates in cognitively normal older adults with low cerebrospinal fluid Ab1-42. Ann. Neurol. 68(6), 825-834 (2010).
6. Jack CR, Knopman DS, Jagust WJ et al. Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade. Lancet Neurol. 9(1), 119-128 (2010).
7. Dubois B, Feldman HH, Jacova C et al. Research criteria for the diagnosis of Alzheimer's disease: revising the NINCDS-ADRDA criteria. Lancet Neurol. 6(8), 734-746 (2007). (Pubitemid 47058377)
8. Sperling RA, Aisen PS, Beckett LA et al. Toward defining the preclinical stages of Alzheimer's disease: recommendations from the National Institute on Aging - Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 7(3), 280-292 (2011).
9. Pepe MS. The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press, UK (2003). Describes the statistical concepts and methods involved in classification and prediction.
10. Grimes DA, Schulz KF. Uses and abuses of screening tests. Lancet 359, 881-884 (2002). Original paper highlighting important issues that should be considered when contemplating using a biomarker as a screening test. (Pubitemid 34233764)
11. Sjögren M, Vanderstichele H, Agren H et al. Tau and Ab42 in cerebrospinal fluid from healthy adults 21-93 years of age: establishment of reference values. Clin. Chem. 47(10), 1776-1881 (2001).
12. Perkins NJ, Schisterman EF. The inconsistency of optimal' cutpoints obtained using two criteria based on the receiver operating characteristic curve. Am. J. Epidemiol. 163(7), 670-675 (2006). Original paper demonstrating that different optimal' cut-points can result from the same data through use of statistical methods that quantify test performance differently.
13. Jack CR, Vemuri P, Wiste HJ et al. Evidence for ordering of Alzheimer disease biomarkers. Arch. Neurol. 68(12), 1526-1535 (2011).
14. Fluss R, Faraggi D, Reiser B. Estimation of the Youden index and its associated cutpoint point. Biom. J. 47(4), 458-472 (2005). (Pubitemid 41229379)
15. Krzanowski WJ, Hand DJ. ROC Curves for Continuous Data. CRC Press, FL, USA (2009).
16. Youden WJ. An index for rating diagnostic tests. Cancer 3, 32-35 (1950).
17. Schisterman EF, Perkins NJ, Liu A et al. Optimal cut-point and its corresponding Youden index to discriminate individuals using pooled blood samples. Epidemiology 16, 73-81 (2005). (Pubitemid 40239280)
18. Royston P, Thompson SG. Model-based screening by risk with application to Down's syndrome. Stat. Med. 11(2), 257-268 (1992).
19. De Meyer G, Shapiro F, Vanderstichele H et al. Diagnosis-independent Alzheimer disease biomarker signature in cognitively normal elderly people. Arch. Neurol. 67, 949-956 (2010).
20. Leeflang MMG, Moons KGM, Reitsma JB et al. Bias in sensitivity and specificity caused by data-driven selection of optimal cutpoint values: mechanisms, magnitude, and solutions. Clin. Chem. 54, 729-737 (2008). (Pubitemid 351573715)