Nabihah Tayob, The University of Texas MD Anderson Cancer Center, USA, will present the Department of Statistical Science seminar with a talk entitled, "A Bayesian Screening Approach for Hepatocellular Carcinoma Using Multiple Longitudinal Biomarkers".

Advanced hepatocellular carcinoma (HCC) has limited treatment options and poor survival. Patients with early stage disease have multiple (potentially curative) treatment options while those with advanced stage disease have very limited treatments available. Therefore, the earlier detection of HCC is critical to improving patient survival. Serum α-Fetoprotein (AFP) is a widely used screening biomarker, but it has limited sensitivity and is not elevated in all HCC cases. Hence, we incorporate a second blood-based biomarker, des-γ carboxy-prothrombin (DCP), that has shown potential. The Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) Trial is a valuable source of data to study biomarker screening for HCC. We assume the trajectories of AFP and DCP follow a joint hierarchical mixture model with random changepoints that allows for distinct changepoint times and subsequent trajectories of each biomarker. Changepoint indicators are jointly modeled with a Markov Random Field distribution to help detect borderline changepoints. Markov chain Monte Carlo methods are used to calculate posterior distributions, which are used in risk calculations among future patients and determine whether a patient has a positive screen. The screening algorithm was compared to alternatives in the HALT-C Trial using cross-validation.