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Bayesian hierarchical model enables combining information from in silico and in vivo experiments

With the last technical deliverable from Work Package 3, the group coordinated by Dr Miguel A. Juárez from the University of Sheffield presented the Bayesian hierarchical models for the main endpoints of the clinical trial, i.e. the proportion of patients with sputum culture negative, either when focusing on a single source of information or after combining both sources of in silico and in vivo data in an augmented clinical trial.

The primary objective of this work package is precisely to formally supply information from the in silico experiments to the clinical trials, thus the document first describes how to formally combine in silico and in vivo information with a hierarchical Bayesian approach, followed by the implementation of a model that allows for the combination of both sources of information.

The documents describes the key elements of the formal models and the implementation of the fitting algorithms. The basic implementation steps of both algorithms are summarised in their corresponding pseudo-code: single source and combination of information, along with an illustration to accessing and using the fitted models for inference.

See all technical deliverables here

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