Advanced hepatocellular carcinoma (HCC) has limited treatment options and poor 
survival, therefore early detection is critical to improving the survival of 
patients with HCC. Current guidelines for high-risk patients include ultrasound 
screenings every six months, but ultrasounds are operator dependent and not 
sensitive for early HCC. Serum α-Fetoprotein (AFP) is a widely used diagnostic 
biomarker, but it has limited sensitivity and is not elevated in all HCC cases 
so, we incorporate a second blood-based biomarker, des'γ carboxy-prothrombin 
(DCP), that has shown potential as a screening marker for HCC. The data from 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. The 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 simulations studies under a range of possible scenarios and in 
the HALT-C Trial using cross-validation.