Adjusting ROC curves for covariates in the presence of verification bias

Research output: Contribution to journalArticlepeer-review


The ROC (receiver operating characteristic) curve is frequently used for describing effectiveness of a diagnostic marker or test. Classical estimation of the ROC curve uses independent identically distributed samples taken randomly from the healthy and diseased populations. Frequently not all subjects undergo a definitive gold standard assessment of disease status (verification). Estimation of the ROC curve based on data only from subjects with verified disease status may be badly biased (verification bias). In this work we investigate the properties of the doubly robust (DR) method for estimating the ROC curve adjusted for covariates (ROC regression) under verification bias. We develop the estimator's asymptotic distribution and examine its finite sample size properties via a simulation study. We apply this procedure to fingerstick postprandial blood glucose measurement data adjusting for age.

Original languageEnglish
Pages (from-to)1-11
Number of pages11
JournalJournal of Statistical Planning and Inference
Issue number1
StatePublished - Jan 2012


  • Diagnostic test
  • ROC regression
  • Semi-parametric
  • Sensitivity
  • Specificity

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


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