Accounting for control mislabeling in case-control biomarker studies.

ABSTRACT: In biomarker discovery studies, uncertainty associated with case and control labels is often overlooked. By omitting to take into account label uncertainty, model parameters and the predictive risk can become biased, sometimes severely. The most common situation is when the control set contains an unknown number of undiagnosed, or future, cases. This has a marked impact in situations where the model needs to be well-calibrated, e.g., when the prediction performance of a biomarker panel is evaluated. Failing to account for class label uncertainty may lead to underestimation of classification performance and bias in parameter estimates. This can further impact on meta-analysis for combining evidence from multiple studies. Using a simulation study, we outline how conventional statistical mode

SUBMITTER: Rantalainen M 

PROVIDER: S-EPMC3314325 | biostudies-literature | 2011 Dec

REPOSITORIES: biostudies-literature