Title: Theory & Methods: Fitting Roc Curves Using Non‐linear Binomial Regression
Abstract:The performance of a diagnostic test is summarized by its receiver operating characteristic (ROC) curve. Empirical data on a test's performance often come in the form of observed true positive and fal...The performance of a diagnostic test is summarized by its receiver operating characteristic (ROC) curve. Empirical data on a test's performance often come in the form of observed true positive and false positive relative frequencies, under varying conditions. This paper describes a family of models for analysing such data. The underlying ROC curves are specified by a shift parameter, a shape parameter and a link function. Both the position along the ROC curve and the shift parameter are modelled linearly. The shape parameter enters the model non‐linearly but in a very simple manner. One simple application is to the meta‐analysis of independent studies of the same diagnostic test, illustrated on some data of Moses, Shapiro & Littenberg (1993). A second application to so‐called vigilance data is given, where ROC curves differ across subjects, and modelling of the position along the ROC curve is of primary interest.Read More
Publication Year: 2000
Publication Date: 2000-06-01
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 5
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