A Bayesian Screening Approach for Hepatocellular Carcinoma Using Two Longitudinal Biomarkers

• Activity Number: 503
• Type: Contributed
• Date/Time: Wednesday, August 12, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Nabihah Tayob* and Francesco Stingo and Kim-Anh Do and Ziding Feng
• Companies: and MD Anderson Cancer Center and MD Anderson Cancer Center and MD Anderson Cancer Center
• Keywords: Biomarkers ; posterior risk ; hierarchical models ; MCMC methods ; early detection

Abstract

Advanced heptocelluar carcinoma (HCC) has limited treatment options and poor survival. Early detection of HCC is critical to improve the prognosis of patients. Current guidelines for high-risk patients include six-month ultrasound screenings but these are not sensitive for early HCC. Alpha-fetoprotein (AFP) is a widely used diagnostic biomarker but has shown limited use in HCC screening with a fixed threshold. Approaches that incorporate longitudinal AFP have shown potentially higher and earlier detection of HCC. AFP is not elevated in all HCC cases so we incorporate a second biomarker, des-gamma-carboxy prothrombin (DCP), to improve detection. The posterior risk of HCC, given longitudinal values of AFP and DCP, is used to determine whether a patient has a positive screen. A joint hierarchical mixture model with random changepoint is fit using data from the Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) Trial for AFP and DCP in both cases and controls. Markov chain Monte Carlo methods are used to calculate posterior distributions used in risk calculations among future patients. The screening approach is implemented in the independent North America study data.