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A Bayesian screening approach for hepatocellular carcinoma using multiple longitudinal biomarkers

Tayob, Nabihah, Stingo, Francesco, Do, Kim‐Anh, Lok, Anna S. F., Feng, Ziding
Biometrics 2018 v.74 no.1 pp. 249-259
Markov chain, Monte Carlo method, algorithms, biomarkers, biometry, blood serum, guidelines, hepatitis C, hepatoma, patients, probabilistic models, risk, screening, ultrasonics
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.