Bayesian augmented clinical trials in TB therapeutic vaccination

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.

Metadata

Authors/Creators:

Kiagias, D.
Russo, G.
Sgroi, G.
Pappalardo, F.
Juárez, M.A. https://orcid.org/0000-0002-5128-0976

© 2021 Kiagias, Russo, Sgroi, Pappalardo and Juárez. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (http://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Keywords:

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

Dates: