Evaluation of confounding effects in ROC studies

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10 Scopus citations

Abstract

In many clinical studies, it is clear that external forces can affect the performance of diagnostic tests, as these factors influence the distributions of separator variables. A new estimator for the receiver operating characteristic (ROC) function is proposed; this estimator converges to the ROC function uniformly on the interval [0,1]. Using this new estimator, the author proposes to use Cox's proportional hazards regression model for the evaluation of confounding effects in ROC studies. The method can be used even when concomitant information is only available for the cases, for example, disease severity. A textbook example on prostate cancer is described for illustration.

Original languageEnglish (US)
Pages (from-to)998-1007
Number of pages10
JournalBiometrics
Volume53
Issue number3
DOIs
StatePublished - Sep 1 1997

Keywords

  • Concomitant information
  • Diagnostic test
  • Proportional Hazards model
  • ROC function
  • Regression analysis
  • Sensitivity
  • Specificity

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