Accommodating covariates in receiver operating characteristic analysis

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Regression models are introduced into the receiver operating characteristic (ROC) analysis to accommodate effects of covariates, such as genes.This “individualized” criterion exactly fits the ROC regression.The remaining parts of this paper are organized as follows.For example, in a continuous-scale test, the diagnosis of a disease is dependent upon whether a test result is above or below a specified cutoff value.Also, genome-wide association studies in human populations aim at creating genomic profiles which combine the effects of many associated genetic variants to predict the disease risk of a new subject with high discriminative accuracy [1].

Alternatively, they developed the focused information criterion (FIC), which focuses on a parameter singled out for interests.So far, not much attention has been drawn on the topic of variable selection in the ROC regression.Two possible reasons may account for this situation.For a given cutoff value of a biomarker or a combination of biomarkers, the sensitivity and the specificity are employed to quantitatively evaluate the discriminative performance.By varying cutoff values throughout the entire real line, the resulting plot of sensitivity against 1-specificity is a ROC curve. [4] provided broad reviews on many statistical methods for the evaluation of diagnostic tests.