Personalized screening intervals for biomarkers: a joint-modeling application for heart failure patients

Screening for diseases is common practice in illness detection. The design of optimal, personalized screening intervals has received more attention as personalized medicine has become more popular. In this work, we focus on the modeling of longitudinal biomarker measurements. We extend the framework of joint modeling in the field of screening intervals of Rizopoulos et al. (2016) in two directions. First, we consider a Bayesian model average specification. Second, we allow for the simultaneous scheduling of multiple screenings. We illustrate the use of our adaptions with an application among heart failure patients and the NT-proBNP biomarker. We find that (i) higher levels of the biomarker places the patient at greater risk for cardiac events and (ii) that Bayesian model averaging allows for modeling non-standard biomarker trajectories.