Bayesian Augmented Clinical Trials in TB Therapeutic Vaccination

Area of Science:

• Biostatistics
• Computational Biology
• Clinical Trial Methodology

Background:

• Integrating diverse data sources in clinical trials is crucial for robust inference.
• In silico methods offer potential for augmenting traditional in vivo studies.
• Bayesian approaches provide a flexible framework for combining evidence.

Purpose of the Study:

• To develop and illustrate a Bayesian hierarchical method for combining in silico and in vivo data in clinical trials.
• To formalize the contribution and impact of in silico information within an augmented trial framework.
• To apply the method to tuberculosis (TB) infection data.

Main Methods:

• A Bayesian hierarchical model was developed to combine in silico and in vivo data.
• The joint posterior distribution from in silico experiments served as a weighted prior.
• Compatibility measures were used to weight the in silico prior based on shared characteristics with in vivo data.

Main Results:

• The proposed method successfully integrated in silico data from the UISS-TB simulator with synthetic in vivo patient data.
• The approach allows for formal quantification of in silico evidence within the augmented clinical trial.
• Demonstrated the utility of the method for inference in trials with binary endpoints.

Conclusions:

• The Bayesian hierarchical method provides a robust framework for incorporating in silico data into augmented clinical trials.
• This approach enhances the evidence base by leveraging virtual patient simulations alongside real-world data.
• The method is applicable to various diseases, including tuberculosis, with binary endpoints.