Using the optimal robust Receiver Operating Characteristic (ROC) curve for predictive genetic tests

Biometrics

Volumn 66, Issue 2, 2010, Pages 586-593

  Lu, Qing ^a   Obuchowski, Nancy ^b   Won, Sungho ^c   Zhu, Xiaofeng ^c   Elston, Robert C ^c  

^a MICHIGAN STATE UNIVERSITY   (United States)

^b LERNER RESEARCH INSTITUTE   (United States)

^c CASE WESTERN RESERVE UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

Backward clustering; Classification tree; Cross validation; Logistic regression

Indexed keywords

LEARNING ALGORITHMS; TREES (MATHEMATICS);

BACKWARD CLUSTERING; CLASSIFICATION TREES; CLUSTERINGS; CROSS VALIDATION; EARLY DISEASE; GENETIC TESTS; GENETIC VARIANTS; LIKELIHOOD RATIOS; LOGISTICS REGRESSIONS; RECEIVER OPERATING CHARACTERISTIC CURVES;

REGRESSION ANALYSIS;

CLUSTER ANALYSIS; DIABETES; GENETIC VARIATION; GENOME; LOGISTICS; POLYMORPHISM; REGRESSION ANALYSIS; SIMULATION;

DIABETES MELLITUS, TYPE 1; GENETIC ASSOCIATION; GENETIC PREDISPOSITION; GENETIC SCREENING; GENETICS; HUMAN; PREDICTIVE VALUE; PROCEDURES; RECEIVER OPERATING CHARACTERISTIC; SINGLE NUCLEOTIDE POLYMORPHISM; STATISTICAL MODEL;

DIABETES MELLITUS, TYPE 1; GENETIC PREDISPOSITION TO DISEASE; GENETIC TESTING; GENOME-WIDE ASSOCIATION STUDY; HUMANS; LOGISTIC MODELS; POLYMORPHISM, SINGLE NUCLEOTIDE; PREDICTIVE VALUE OF TESTS; ROC CURVE;

EID: 77952970297     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2009.01278.x     Document Type: Article

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