mROC: A computer program for combining tumour markers in predicting disease states

Andrew Kramar, David Faraggi, Antoine Fortuné, Benjamin Reiser

Research output: Contribution to journalArticlepeer-review


Receiver operating characteristic (ROC) curves are limited when several diagnostic tests are available, mainly due to the problems of multiplicity and inter-relationships between the different tests. The program presented in this paper uses the generalised ROC criteria, as well as its confidence interval, obtained from the non-central F distribution, as a possible solution to this problem. This criterion corresponds to the best linear combination of the test for which the area under the ROC curve is maximal. Quantified marker values are assumed to follow a multivariate normal distribution but not necessarily with equal variances for two populations. Other options include Box-Cox variable transformations, QQ-plots, interactive graphics associated with changes in sensitivity and specificity as a function of the cut-off. We provide an example to illustrate the usefulness of data transformation and of how linear combination of markers can significantly improve discriminative power. This finding highlights potential difficulties with methods that reject individual markers based on univariate analyses.

Original languageEnglish
Pages (from-to)199-207
Number of pages9
JournalComputer Methods and Programs in Biomedicine
Issue number2-3
StatePublished - 2001

Bibliographical note

Funding Information:
This project was partially financed by the French Ligue Nationale Contre le Cancer.


  • ROC curves
  • Tumour marker combinations
  • mROC

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Health Informatics


Dive into the research topics of 'mROC: A computer program for combining tumour markers in predicting disease states'. Together they form a unique fingerprint.

Cite this