Comparison of methods of optimal cut-point selection for biomarkers in diagnostic medicine: a simulation study with application of clinical data in health informatics.

Abstract

Objectives: Several methods of cut-point selection for biomarkers have been suggested in biomedical research but the superiority of them over others was not studied comprehensively under different pairs of distributions, degree of overlap, and the ratio of sample sizes. This simulation study was aimed to compare five popular methods with application of clinical examples.

Results: The data of simulation was generated from the 12 configurations of binormal, bigamma, and biexponential pairs with different sample sizes The results showed that the four popular methods of Youden, Euclidean, Product, and Index of Union (IU) yielded identical optimal cut-point under binormal model with homoscedastic. While, with high AUC, the Youden may produce less bias and MSE, but for moderate and low AUC, Euclidean has less bias and MSE than other methods. The IU yielded more precise findings than the Youden for moderate and low AUC in binormal pairs, but its performance was lower with skewed distributions. In contrast, the cut-points produced by diagnostic odds ratio (DOR) were extremely high with low sensitivity and high MSE and bias. The results of clinical data showed that when AUC > 0.95, the five methods may produce identical cut-point, but DOR yields an extremely high value of cut-point for AUC < 0.95.