A family of estimators to diagnostic accuracy when candidate tests are subject to detection limits-Application to diagnosing early stage Alzheimer disease.
Alzheimer disease
Cox proportional hazards model
area under the receiver operating characteristic curve
bootstrap
confidence interval estimate
detection limits
diagnostic accuracy
maximum likelihood estimate
nonparametric estimate
receiver operating characteristic curve
Journal
Statistical methods in medical research
ISSN: 1477-0334
Titre abrégé: Stat Methods Med Res
Pays: England
ID NLM: 9212457
Informations de publication
Date de publication:
05 2022
05 2022
Historique:
pubmed:
20
1
2022
medline:
20
4
2022
entrez:
19
1
2022
Statut:
ppublish
Résumé
In disease diagnosis, individuals are usually assumed to be one of the two basic types, healthy or diseased, as typically based on an established gold standard. Candidate markers for diagnosing a disease often are much cheaper and less invasive than the gold standard but must be evaluated against the gold standard for their sensitivity and specificity to accurately diagnose the disease. When candidate diagnostic markers are fully measured, receiver operating characteristic curves have been the standard approaches for assessing diagnostic accuracy. However, full measurements of diagnostic markers may not be available above or below certain limits due to various practical and technical limitations. For example, in the diagnosis of Alzheimer disease using cerebrospinal fluid biomarkers, the Roche Elecsys® immunoassays have a measuring range for multiple cerebrospinal fluid molecular concentrations. Many cognitive tests used in diagnosing dementia due to Alzheimer disease are also subject to detection limits, often referred to as the floor and ceiling effects in the neuropsychological literature. We propose a new statistical methodology for estimating the diagnostic accuracy when a diagnostic marker is subject to detection limits by dividing the entire study sample into two sub-samples by a threshold of the diagnostic marker. We then propose a family of estimators to the area under the receiver operating characteristic curve by combining a conditional nonparametric estimator and another conditional semi-parametric estimator derived from Cox's proportional hazards model. We derive the variance to the proposed estimators, and further, assess the performance of the proposed estimators as a function of possible thresholds through an extensive simulation study, and recommend the optimum thresholds. Finally, we apply the proposed methodology to assess the ability of several cerebrospinal fluid biomarkers and cognitive tests in diagnosing early stage Alzheimer disease dementia.
Identifiants
pubmed: 35044258
doi: 10.1177/09622802211072511
pmc: PMC9018582
mid: NIHMS1780510
doi:
Substances chimiques
Biomarkers
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
882-898Subventions
Organisme : NIA NIH HHS
ID : P01 AG026276
Pays : United States
Organisme : NIA NIH HHS
ID : P30 AG066444
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG067505
Pays : United States
Organisme : NIA NIH HHS
ID : RF1 AG053550
Pays : United States
Organisme : NIA NIH HHS
ID : P01 AG003991
Pays : United States
Organisme : NIA NIH HHS
ID : P50 AG005681
Pays : United States
Références
Biom J. 2013 Sep;55(5):719-40
pubmed: 23553499
Biom J. 2011 May;53(3):464-76
pubmed: 22223252
Am J Epidemiol. 2007 Mar 15;165(6):710-8
pubmed: 17182981
Stat Med. 2006 Apr 15;25(7):1251-73
pubmed: 16345029
Stat Med. 2000 Dec 15;19(23):3171-91
pubmed: 11113952
J Clin Epidemiol. 2016 Aug;76:175-82
pubmed: 26964707
Am J Epidemiol. 2007 Feb 1;165(3):325-33
pubmed: 17110640
Biostatistics. 2006 Oct;7(4):585-98
pubmed: 16531470
J Clin Epidemiol. 1995 Dec;48(12):1503-10
pubmed: 8543964
Commun Stat Simul Comput. 2013 Jan;42(6):1213-1234
pubmed: 23794784
J Clin Epidemiol. 1995 Dec;48(12):1495-501
pubmed: 8543963
Stat Med. 2017 Oct 30;36(24):3830-3843
pubmed: 28786136
Arch Neurol. 2001 Mar;58(3):397-405
pubmed: 11255443
Neurology. 1993 Nov;43(11):2412-4
pubmed: 8232972
Biometrics. 1988 Sep;44(3):837-45
pubmed: 3203132
J Clin Epidemiol. 1996 Dec;49(12):1373-9
pubmed: 8970487
Neurology. 1996 Mar;46(3):661-5
pubmed: 8618663
Stat Med. 2006 Feb 15;25(3):481-93
pubmed: 16287217
Alzheimers Dement. 2016 May;12(5):517-26
pubmed: 26555316
Biom J. 2007 Aug;49(5):682-93
pubmed: 17763377
Biometrics. 1997 Jun;53(2):567-78
pubmed: 9192452
Comput Stat Data Anal. 2013 Dec;68:
pubmed: 24415817
Biometrics. 2008 Sep;64(3):895-903
pubmed: 18047527
Stat Med. 1998 May 15;17(9):1033-53
pubmed: 9612889
JAMA Neurol. 2015 Sep;72(9):1029-42
pubmed: 26147946
Neurology. 2016 Apr 19;86(16):1499-506
pubmed: 27009258
Alzheimers Dement. 2019 Nov;15(11):1448-1457
pubmed: 31506247
Med Decis Making. 2000 Jul-Sep;20(3):323-31
pubmed: 10929855
Alzheimer Dis Assoc Disord. 2006 Oct-Dec;20(4):210-6
pubmed: 17132964
Stat Med. 2010 Dec 10;29(28):2946-55
pubmed: 20809485
Radiology. 1982 Apr;143(1):29-36
pubmed: 7063747
Cancer Treat Rep. 1985 Oct;69(10):1071-77
pubmed: 4042087
Radiol Phys Technol. 2008 Jan;1(1):2-12
pubmed: 20821157
Acad Radiol. 2013 Jul;20(7):838-46
pubmed: 23747152
Science. 1988 Jun 3;240(4857):1285-93
pubmed: 3287615
Biometrics. 2000 Jun;56(2):337-44
pubmed: 10877287