An extension of parametric ROC analysis for calculating diagnostic accuracy when underlying distributions are mixture of Gaussian

摘要

The semiparametric LAB ROC approach of fitting binormal model for estimating AUC as a global index of accuracy has been justified (except for bimodal forms), while for estimating a local index of accuracy such as TPF, it may lead to a bias in severe departure of data from binormality. We extended parametric ROC analysis for quantitative data when one or both pair members are mixture of Gaussian (MG) in particular for bimodal forms. We analytically showed that AUC and TPF are a mixture of weighting parameters of different components of AUCs and TPFs of a mixture of underlying distributions. In a simulation study of six configurations of MG distributions:{bimodal, normal} and {bimodal, bimodal} pairs, the parameters of MG distributions were estimated using the EM algorithm. The results showed that the estimated AUC from our proposed model was essentially unbiased, and that the bias in the estimated TPF at a clinically relevant range of FPF was roughly 0.01 for a sample size of n = 100/100. In practice, with severe departures from binormality, we recommend an extension of the LABROC and software development for future research to allow for each member of the pair of distributions to be a mixture of Gaussian that is a more flexible parametric form.%Department of Social Medicine and Health, Babol University of Medical Sciences, Babol, Iran;Department of Epidemiology and Biostatistics, McGill University, 1020 Pine Avenue West, Montreal, PQ,Canada H3A 1A2;CAMP, Department of Mathematics. Vrije Universiteit, Brussel (VUB), Pleinlaan 2,1050, Brussels, Belgium;