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Environ Health Perspect. 1994 Nov;102 Suppl 8:73-8.

ROC curve regression analysis: the use of ordinal regression models for diagnostic test assessment.

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  • 1Department of Medicine and Community and Family Medicine, Dartmouth Medical School, Hanover, New Hampshire 03756.

Abstract

Diagnostic tests commonly are characterized by their true positive (sensitivity) and true negative (specificity) classification rates, which rely on a single decision threshold to classify a test result as positive. A more complete description of test accuracy is given by the receiver operating characteristic (ROC) curve, a graph of the false positive and true positive rates obtained as the decision threshold is varied. A generalized regression methodology, which uses a class of ordinal regression models to estimate smoothed ROC curves has been described. Data from a multi-institutional study comparing the accuracy of magnetic resonance (MR) imaging with computed tomography (CT) in detecting liver metastases, which are ideally suited for ROC regression analysis, are described. The general regression model is introduced and an estimate for the area under the ROC curve and its standard error using parameters of the ordinal regression model is given. An analysis of the liver data that highlights the utility of the methodology in parsimoniously adjusting comparisons for covariates is presented.

PMID:
7851336
[PubMed - indexed for MEDLINE]
PMCID:
PMC1566538
Free PMC Article
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