Comparing Estimation Methods for the Area Under the Bi-Weibull ROC Curve

In this paper, we carried out extensive simulation studies to compare the performances of partial and maximum likelihood based methods for estimating the area under the bi-Weibull ROC curve. Further, real data sets from HIV/AIDS research were analyzed for illustrative purposes. Simulation results suggest that both methods perform well and yield similar results for Weibull data. However, for non-Weibull data, both methods perform poorly. The bi-Weibull model yields smooth estimates of ROC curves and a closed-form expression for the area under the ROC curve. Moreover, by adjusting its shape parameter, the bi-Weibull model can represent a variety of distributions, such as exponential, Rayleigh, normal, and extreme value distributions. Its compatibility with Cox's proportional hazards model facilitates the derivation of covariate-adjusted ROC curves and supports analyses involving correlated and longitudinal biomarkers. These properties make the model very useful in the ROC curve analyses. Thus, the bi-Weibull model should be considered as an alternative when the restrictive distributional assumptions of the commonly used parametric models (e.g., binormal model) are not met.