%0 Journal Article %1 Begg2013 %A Begg, Colin B %A Gonen, Mithat %A Seshan, Venkatraman E %D 2013 %J Clinical trials (London, England) %K BiologicalMarkers ClinicalTrialsasTopic ClinicalTrialsasTopic:methods Humans PredictiveValueofTests ROCCurve ReproducibilityofResults RiskAssessment %N 5 %P 690-2 %R 10.1177/1740774513496490 %T Testing the incremental predictive accuracy of new markers. %U http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3800241&tool=pmcentrez&rendertype=abstract %V 10 %X BACKGROUND: It has become commonplace to use receiver operating curve (ROC) methodology to evaluate the incremental predictive accuracy of new markers in the presence of existing predictors. However, concerns have been raised about the validity of this practice. We have evaluated this issue in detail. RESULTS: Simulations have been used that show clearly that use of risk predictors from nested models as data in subsequent tests comparing areas under the ROC curves of the models leads to grossly invalid inferences. Careful examination of the issue reveals two major problems: (1) the data elements are strongly correlated from case to case and (2) the model that includes the additional marker has a tendency to interpret predictive contributions as positive information regardless of whether observed effect of the marker is negative or positive. Both of these phenomena lead to profound bias in the test. CONCLUSIONS: We recommend strongly against the use of ROC methods derived from risk predictors from nested regression models to test the incremental information of a new marker.

@article{Begg2013, abstract = {BACKGROUND: It has become commonplace to use receiver operating curve (ROC) methodology to evaluate the incremental predictive accuracy of new markers in the presence of existing predictors. However, concerns have been raised about the validity of this practice. We have evaluated this issue in detail. RESULTS: Simulations have been used that show clearly that use of risk predictors from nested models as data in subsequent tests comparing areas under the ROC curves of the models leads to grossly invalid inferences. Careful examination of the issue reveals two major problems: (1) the data elements are strongly correlated from case to case and (2) the model that includes the additional marker has a tendency to interpret predictive contributions as positive information regardless of whether observed effect of the marker is negative or positive. Both of these phenomena lead to profound bias in the test. CONCLUSIONS: We recommend strongly against the use of ROC methods derived from risk predictors from nested regression models to test the incremental information of a new marker.}, added-at = {2023-02-03T11:44:35.000+0100}, author = {Begg, Colin B and Gonen, Mithat and Seshan, Venkatraman E}, biburl = {https://www.bibsonomy.org/bibtex/2ec36a8d34dc7498a668adba26cab0507/jepcastel}, doi = {10.1177/1740774513496490}, interhash = {d41d0ac0f515d8bae6a911254b7491e3}, intrahash = {ec36a8d34dc7498a668adba26cab0507}, issn = {1740-7753}, journal = {Clinical trials (London, England)}, keywords = {BiologicalMarkers ClinicalTrialsasTopic ClinicalTrialsasTopic:methods Humans PredictiveValueofTests ROCCurve ReproducibilityofResults RiskAssessment}, month = {10}, note = {Models predictius; Proves diagnòstiques; ROC}, number = 5, pages = {690-2}, pmid = {23881367}, timestamp = {2023-02-03T11:44:35.000+0100}, title = {Testing the incremental predictive accuracy of new markers.}, url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3800241&tool=pmcentrez&rendertype=abstract}, volume = 10, year = 2013 }