2025, Vol. 6, Issue 1, Part D

A comprehensive simulation study was conducted to evaluate the model’s performance under conditions of sparsity and predictor correlation. The results showed high estimation accuracy, perfect true positive rates, and robust behavior across quantile levels. BENQReg consistently outperformed standard Bayesian quantile models in variable recovery, especially at the median quantile level.

The model was then applied to a breast cancer dataset containing clinical, biomarker, and genetic features. BENQReg identified distinct predictors at different quantiles, including tumor size and HER2 status as early risk indicators, and hormone receptor status and BRCA2 expression as protective factors in patients with longer recurrence-free survival. These findings demonstrate the model’s capacity to detect quantile-dependent associations, offering clinically interpretable insights that support precision medicine.

BENQReg provides a robust, flexible, and informative approach for analyzing heterogeneous biomedical data and is especially valuable in high-dimensional settings where variable selection is critical.