Searches for optimal linear combination of multiple diagnostic tests (markers) that maximizes the area under the receiver operating characteristic curve (AUC); performs an approximated cross-validation for estimating the AUC associated with the estimated coefficients.
Xin Huang, Gengsheng Qin, Yixin Fang
Maintainer: Xin Huang <email@example.com>
Huang X, Qin G, Fang Y. (2011) Optimal Combinations of Diagnostic Tests Based on AUC. Biometrics. Jun;67(2):568-76.
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rho<-0 m<-50 n<-50 y1.sd<-0.5 y2.sd<-0.5 y1.mean<-2 y2.mean<-1 lambda <- 5 set.seed(88) # generate non-diseased population F(X1, X2) # the sample from 2-dimensinal multinormal distribution with mean 0 and std=1 X1X2<-mvrnorm(m, c(1,1), matrix(c(0.5,rho,rho,0.5),2,2)) # generate diseased population G(Y1,Y2) # the sample from 2-dimensinal multinormal distribution with mean # (y1.mean,y2.mean) and std=(y1.sd,y2.sd) Y1Y2<-mvrnorm(n, c(y1.mean,y2.mean), matrix(c(y1.sd^2,rho*y1.sd*y2.sd, rho*y1.sd*y2.sd, y2.sd^2),2,2)) # only the first marker, the "true" model, should have the maximum AUC amount all models optAUC(X1X2, Y1Y2, column.select=1) # two markers in the model, the AUC from GCV is smaller than just first marker in the model, because the second marker is noise # the AUC from ACV (apearent estimate by substituting the estimated beta into the model) is larger than previous model, because overfitting optAUC(X1X2, Y1Y2, column.select=c(1:2))
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