ANNOUNCE: Nonparametric ROC analysis program for IBM 
Author Message
 ANNOUNCE: Nonparametric ROC analysis program for IBM

          Nonparametric Receiver Operating Characteristic Analysis
                     Stephen Vida, M.D., F.R.C.P.(C)
                        Department of Psychiatry
                        Montreal General Hospital
                           McGill University

     Receiver operating characteristic analysis is a leading tool for
the analysis of the sensitivity and specificity of

        - signal detection devices, e.g. radar receivers.

        - diagnostic tests, e.g. laboratory tests.

        - diagnostic instruments, e.g. rating scales.

        - radiologic instruments, e.g. x-ray machines, CT scanners,
          and MRI scanners.

        - diagnosticians, e.g. clinicians.

     Sensitivity and specificity are key measures of test performance.  
For tests yielding numerical scores, sensitivity and specificity
usually vary inversely over the range of theoretically possible cutoff
scores, complicating the task of quantifying and comparing the
diagnostic accuracy of tests.

     Receiver Operating Characteristic analysis (ROC) approaches this
problem by plotting the curve of sensitivity, or true positive rate,
versus 1 - specificity, or false positive rate, for all possible cutoff
scores of the test.  The area under the ROC curve (AUC) can be used to
describe the diagnostic accuracy of the test.  Parametric and
nonparametric methods exist that allow the calculation of the AUC and
the comparison of tests.  A disadvantage of parametric formulations is
the assumption of a normal or Gaussian distribution of test scores.

     Parametric ROC formulations, based on normal or Gaussian
distributional assumptions, have existed for some time.  While to some
degree they are robust to non-normality of underlying data, some
authors have reported non-normal datasets for which parametric
formulations either failed to compute solutions or computed erroneous

     Nonparametric ROC formulations have more recently evolved that are
distribution-free, in that they do not depend on the distribution of the
test scores being analyzed but rather on their ranking.  Although
nonparametric, statistical efficiency comparable to parametric approaches
has been reported.

     Nonparametric ROC may be useful when

     1.   a distribution-free method is preferred because of
          non-normality of data.

     2.   parametric methods have failed to compute or have yielded
          erroneous or implausible results.

     3.   corroboration or comparison of parametric and nonparametric
          methods is desired.

     A Nonparametric Receiver Operating Characteristic Program is
available for IBM-compatible computers that

     -     plots from one to three ROC curves on the same graph.

     -     calculates the area under the ROC curve, its standard error,
           and its 95% confidence limits.

     -     calculates the sensitivity and specificity, with 95%
           confidence limits, at all possible cutoff scores of the
           instrument being analyzed.

     -     statistically compares two ROC curves from independent

     -     statistically compares two or three ROC curves simultaneously
           from correlated samples.

     -     exports ROC curve coordinates to Harvard graphics templates,

     -     performs statistical power analysis to estimate sample sizes
           required for ROC studies.

     -     is accompanied by a comprehensive user's manual that includes
           a review of the theory of ROC analysis and detailed
           instructions for the use of the program.

     For further information about ROC or the program described above,

Sat, 28 Feb 1998 03:00:00 GMT
 [ 1 post ] 

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