Bayesian Augmented Clinical Trials in TB Therapeutic Vaccination

Dimitrios Kiagias; Giulia Russo; Giuseppe Sgroi; Francesco Pappalardo; Miguel A Juárez

doi:10.3389/fmedt.2021.719380

Bayesian Augmented Clinical Trials in TB Therapeutic Vaccination

Front Med Technol. 2021 Oct 22:3:719380. doi: 10.3389/fmedt.2021.719380. eCollection 2021.

Authors

Dimitrios Kiagias¹, Giulia Russo², Giuseppe Sgroi³, Francesco Pappalardo², Miguel A Juárez¹

Affiliations

¹ School of Mathematics and Statistics, University of Sheffield, Sheffield, United Kingdom.
² Department of Drug Sciences, University of Catania, Catania, Italy.
³ Department of Mathematics and Computer Science, University of Catania, Catania, Italy.

Abstract

We propose a Bayesian hierarchical method for combining in silico and in vivo data onto an augmented clinical trial with binary end points. The joint posterior distribution from the in silico experiment is treated as a prior, weighted by a measure of compatibility of the shared 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, a bespoke simulator of virtual patients with tuberculosis infection, and synthetic physical patients from a clinical trial.

Keywords: Bayesian hierarchical model; clinical trials; in silico experiments; information sharing; power prior; therapeutic vaccine; tuberculosis.