Statistical Methods in Medical Research · Oct 21, 2026
A multivariate Bayesian biomarker screening algorithm that incorporates clinical covariates
By Vinattieri MV, Panero F, Jin Q et al.
Advanced hepatocellular carcinoma (HCC) has limited treatment options and poor survival. Early detection of HCC improves survival, but current ultrasound screening is inconsistent and poorly adhered to. Biomarker-based blood tests offer a more accurate, accessible, and reliable alternative. Recent developments in methods for early detection of HCC have focused on the trajectories of multiple biomarkers, assuming that biomarkers follow a joint hierarchical mixture model with random changepoints. We extend this model, allowing covariates to influence three aspects of the model: biomarker trajectories, diagnosis times, and subject-specific prior risks. We evaluated whether covariates could capture unexplained heterogeneity in biomarker levels to improve early detection. A Markov chain Monte Carlo algorithm was implemented to derive posterior distributions used in risk calculations among future patients and to determine whether a patient has a positive screen. The screening algorithm was evaluated through simulation studies under a range of possible covariate extensions. The Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) trial was used to further study the proposed biomarker screening algorithms for HCC using cross-validation.