A new accuracy metric under three classes when subclasses are involved and its confidence interval estimation.
Alzheimer's disease
biomarker evaluation
diagnostic studies
generalized inference
volume under
Journal
Statistics in medicine
ISSN: 1097-0258
Titre abrégé: Stat Med
Pays: England
ID NLM: 8215016
Informations de publication
Date de publication:
10 Dec 2023
10 Dec 2023
Historique:
revised:
26
07
2023
received:
13
02
2023
accepted:
04
09
2023
medline:
20
11
2023
pubmed:
2
10
2023
entrez:
2
10
2023
Statut:
ppublish
Résumé
"Compound multi-class classification" refers to the setting where three or more main classes are involved and at least one of the main classes have multiple subclasses. A common practice in evaluating biomarker performance under "compound multi-class classification" is "subclasses pooling." In this article, we first explore the downsides of accuracy metrics based on pooled data. Then we propose a new accuracy measure proper for "compound multi-class classification" with three ordinal main classes, namely "volume under compound
Substances chimiques
Biomarkers
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
5207-5228Informations de copyright
© 2023 John Wiley & Sons Ltd.
Références
Pepe MS, Etzioni R, Feng Z, et al. Phases of biomarker development for early detection of cancer. J Natl Cancer Inst. 2001;93(14):1054-1061.
Scinto LF, Daffner KR. Early Diagnosis of Alzheimer's Disease. Berlin: Springer; 2000.
Morris JC, Cummings J. Mild cognitive impairment (MCI) represents early-stage Alzheimer's disease. J Alzheimers Dis. 2005;7(3):235-239.
Young JL, Roffers SD, Ries LAG, Fritz AG, Hurlbut AA, eds. SEER Summary Staging Manual 2000: Codes and Coding Instructions. Bethesda, MD: National Institutes of Health, National Cancer Institute; 2001.
Torre LA, Trabert B, DeSantis CE, et al. Ovarian cancer statistics, 2018. CA Cancer J Clin. 2018;68(4):284-296.
Pepe MS. The Statistical Evaluation of Medical Tests for Classification and Prediction. New York: Oxford University Press; 2003.
Zhou XH, McClish DK, Obuchowski NA. Statistical Methods in Diagnostic Medicine. Hoboken, NJ: John Wiley & Sons; 2009.
Zou KH, Liu A, Bandos AI, Ohno-Machado L, Rockette HE. Statistical Evaluation of Diagnostic Performance: Topics in ROC Analysis. Boca Raton, FL: CRC Press; 2011.
Xiong C, Belle vG, Miller JP, Morris JC. Measuring and estimating diagnostic accuracy when there are three ordinal diagnostic groups. Stat Med. 2006;25(7):1251-1273.
Xiong C, Van Belle G, Miller JP, et al. A parametric comparison of diagnostic accuracy with three ordinal diagnostic groups. Biom J. 2007;49(5):682-693.
Kang L, Tian L. Estimation of the volume under the ROC surface with three ordinal diagnostic categories. Comput Stat Data Anal. 2013;62:39-51.
Nakas CT. Developments in ROC surface analysis and assessment of diagnostic markers in three-class classification problems. Revstat Stat J. 2014;12(1):43-65.
Fletcher E, Gavett B, Crane P, et al. A robust brain signature region approach for episodic memory performance in older adults. Brain. 2021;144(4):1089-1102.
Ding Z, Fleishman G, Yang X, Thompson P, Kwitt R, Niethammer M. Fast predictive simple geodesic regression. Med Image Anal. 2019;56:193-209.
Feng YTL. Issues and solutions in biomarker evaluation when subclasses are involved under binary classification. Stat Methods Med Res. 2021;30(1):87-98.
Tsui KW, Weerahandi S. Generalized p-values in significance testing of hypotheses in the presence of nuisance parameters. J Am Stat Assoc. 1989;84(406):602-607.
Weerahandi S. Generalized confidence intervals. J Am Stat Assoc. 1993;88(423):899-905.
Weerahandi S. Generalized Confidence Intervals. Berlin: Springer; 1995:143-168.
Krishnamoorthy K, Lu Y. Inferences on the common mean of several normal populations based on the generalized variable method. Biometrics. 2003;59(2):237-247.
Krishnamoorthy K, Mathew T. Inferences on the means of lognormal distributions using generalized p-values and generalized confidence intervals. J Stat Plan Inference. 2003;115(1):103-121.
Tian L. Inferences on the mean of zero-inflated lognormal data: the generalized variable approach. Stat Med. 2005;24(20):3223-3232.
Tian L. Inferences on the common coefficient of variation. Stat Med. 2005;24(14):2213-2220.
Krishnamoorthy K, Xia Y. Inferences on correlation coefficients: one-sample, independent and correlated cases. J Stat Plan Inference. 2007;137(7):2362-2379.
Lin S, Lee JC, Wang R. Generalized inferences on the common mean vector of several multivariate normal populations. J Stat Plan Inference. 2007;137(7):2240-2249.
Lai CY, Tian L, Schisterman EF. Exact confidence interval estimation for the Youden index and its corresponding optimal cut-point. Comput Stat Data Anal. 2012;56(5):1103-1114.
Zou KH, Hall W. Two transformation models for estimating an ROC curve derived from continuous data. J Appl Stat. 2000;27(5):621-631.
O'Malley AJ, Zou KH. Bayesian multivariate hierarchical transformation models for ROC analysis. Stat Med. 2006;25(3):459-479.
Wang D, Attwood K, Tian L. Receiver operating characteristic analysis under tree orderings of disease classes. Stat Med. 2016;35(11):1907-1926.
Bantis LE, Nakas CT, Reiser B, Myall D, Dalrymple-Alford JC. Construction of joint confidence regions for the optimal true class fractions of receiver operating characteristic (ROC) surfaces and manifolds. Stat Methods Med Res. 2017;26(3):1429-1442.
Yin J, Nakas CT, Tian L, Reiser B. Confidence intervals for differences between volumes under receiver operating characteristic surfaces (VUS) and generalized Youden indices (GYIs). Stat Methods Med Res. 2018;27(3):675-688.
Faraggi D, Reiser B, Schisterman EF. ROC curve analysis for biomarkers based on pooled assessments. Stat Med. 2003;22(15):2515-2527.
Dorfman DD, Berbaum KS, Metz CE, Lenth RV, Hanley JA, Dagga HA. Proper receiver operating characteristic analysis: the bigamma model. Acad Radiol. 1997;4(2):138-149.
Bantis LE, Nakas CT, Reiser B. Construction of confidence regions in the ROC space after the estimation of the optimal Youden index-based cut-off point. Biometrics. 2014;70(1):212-223.
Chen P, Ye ZS. Approximate statistical limits for a gamma distribution. J Qual Technol. 2017;49(1):64-77.
Ye ZS, Chen N. Closed-form estimators for the gamma distribution derived from likelihood equations. Am Stat. 2017;71(2):177-181.
Wang BX, Wu F. Inference on the gamma distribution. Dent Tech. 2018;60(2):235-244.
Krishnamoorthy K, Mathew T, Mukherjee S. Normal-based methods for a gamma distribution: prediction and tolerance intervals and stress-strength reliability. Dent Tech. 2008;50(1):69-78.
Krishnamoorthy K, Wang X. Fiducial confidence limits and prediction limits for a gamma distribution: censored and uncensored cases. Environ. 2016;27(8):479-493.
Perl DP. Neuropathology of Alzheimer's disease. Mt Sinai J Med. 2010;77(1):1931-7581.
Nelson L, Tabet N. Slowing the progression of Alzheimer's disease; what works? Ageing Res Rev. 2015;23:193-209.
Gillette-Guyonnet S, Andrieu S, Nourhashemi F, et al. Long-term progression of Alzheimer's disease in patients under antidementia drugs. Alzheimers Dement. 2011;7(6):579-592.
Woloshin S, Kesselheim AS. What to know about the Alzheimer drug Aducanumab (Aduhelm). JAMA Intern Med. 2022;182:892.
Villasenor JA, Gonzalez-Estrada E. A variance ratio test of fit for gamma distributions. Stat Probab Lett. 2015;96:281-286.
Chételat G, Arbizu J, Barthel H, et al. Amyloid-PET and 18F-FDG-PET in the diagnostic investigation of Alzheimer's disease and other dementias. Lancet Neurol. 2020;19(11):951-962.
Wong DF, Rosenberg PB, Zhou Y, et al. In vivo imaging of amyloid deposition in Alzheimer disease using the radioligand 18F-AV-45 (flobetapir F 18). J Nucl Med. 2010;51(6):913-920.
Nakas CT, Yiannoutsos CT. Ordered multiple-class ROC analysis with continuous measurements. Stat Med. 2004;23(22):3437-3449.
Li J, Fine JP. ROC analysis with multiple classes and multiple tests: methodology and its application in microarray studies. Biostatistics. 2008;9(3):566-576.
Li J, Fine JP, Pencina MJ. Multi-category diagnostic accuracy based on logistic regression. Stat Theory Relat Fields. 2017;1(2):143-158.