Bayesian Augmented Clinical Trials in TB Therapeutic Vaccination

We propose a Bayesian hierarchical method for combining and data onto an augmented clinical trial with binary end points. The joint posterior distribution from the experiment is treated as a prior, weighted by a measure of compatibility of the shared characteristics with the data. We also formalise...

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Bibliographic Details
Published inFrontiers in medical technology Vol. 3; p. 719380
Main Authors Kiagias, Dimitrios, Russo, Giulia, Sgroi, Giuseppe, Pappalardo, Francesco, Juárez, Miguel A
Format Journal Article
LanguageEnglish
Published Switzerland Frontiers Media S.A 22.10.2021
Subjects
Bayesian hierarchical model
clinical trials
in silico experiments
information sharing
Medical Technology
power prior
tuberculosis
therapeutic vaccine
Bayesian hierarchical model
power prior
information sharing
tuberculosis
clinical trials
in silico experiments
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Summary:We propose a Bayesian hierarchical method for combining and data onto an augmented clinical trial with binary end points. The joint posterior distribution from the experiment is treated as a prior, weighted by a measure of compatibility of the shared characteristics with the data. We also formalise the contribution and impact of information in the augmented trial. We illustrate our approach to inference with data from the UISS-TB simulator, a bespoke simulator of virtual patients with tuberculosis infection, and synthetic physical patients from a clinical trial.
Bibliography:ObjectType-Article-1
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Reviewed by: Vasileios C. Pezoulas, University of Ioannina, Greece; Daniele E. Schiavazzi, University of Notre Dame, United States
Edited by: Giancarlo Pennati, Politecnico di Milano, Italy
This article was submitted to Cardiovascular Medtech, a section of the journal Frontiers in Medical Technology
ISSN:2673-3129
2673-3129
DOI:10.3389/fmedt.2021.719380