Bayesian Augmented Clinical Trials in TB Therapeutic Vaccination - Kiagias, Dimitrios; Russo, Giulia; Sgroi, Giuseppe; Pappalardo, Francesco; Juárez, Miguel A. (Frontiers in Medical Technology, 2021)
We propose a Bayesian hierarchical model that combines in silico and in vivo data in an augmented clinical trial with binary end points. The joint posterior distribution from the in silico experiment works as a prior, weighted by a measure of compatibility of those common characteristics with the in vivo data. We also formalise the contribution and impact of in silico information in the augmented trial. We illustrate our approach to inference with in silico data from the UISS-TB simulator —virtual patients with tuberculosis disease—, and synthetic physical patients from a clinical trial.