ROC analysis with multiple classes and multiple tests: Methodology and its application in microarray studies

Biostatistics

Volumn 9, Issue 3, 2008, Pages 566-576

  Li, Jialiang ^a   Fine, Jason P ^b  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^b University of Wisconsin Madison   (United States)

Author keywords

Class prevalence; Diagnostic accuracy; Maximum likelihood estimation; Multicategory classification; Multinomial logistic regression

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; BIOMETRY; CLASSIFICATION; DECISION SUPPORT SYSTEM; DIAGNOSTIC TEST; DIFFERENTIAL DIAGNOSIS; DNA MICROARRAY; EWING SARCOMA; GENETICS; HUMAN; METHODOLOGY; NEUROBLASTOMA; NONHODGKIN LYMPHOMA; PROBABILITY; REGRESSION ANALYSIS; REPRODUCIBILITY; RHABDOMYOSARCOMA; ROC CURVE; SMALL CELL CARCINOMA; STANDARD; STATISTICS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BIOMETRY; CARCINOMA, SMALL CELL; DECISION SUPPORT TECHNIQUES; DIAGNOSIS, DIFFERENTIAL; DIAGNOSTIC TESTS, ROUTINE; HUMANS; LYMPHOMA, NON-HODGKIN; NEUROBLASTOMA; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PROBABILITY; REGRESSION ANALYSIS; REPRODUCIBILITY OF RESULTS; RHABDOMYOSARCOMA; ROC CURVE; SARCOMA, EWING'S; WEIGHTS AND MEASURES;

EID: 45849137394     PISSN: 14654644     EISSN: 14684357     Source Type: Journal    
DOI: 10.1093/biostatistics/kxm050     Document Type: Article

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