Bayesian Meta-Analysis to Validate Correlate of Protection for High Vaccine Efficacy Clinical Trials

Abstract

In clinical trials, a correlate (surrogate) of protection (CoP) endpoint must be properly validated through rigorous sound methods before it may be approved for use. The validation of surrogate in the context of high vaccine efficacy trials, however, poses great challenge due to sparse data; and conventional methods for statistical validation of surrogate are no longer adequate. Although idea of surrogacy was developed in the context of a single trial, the meta-analytic approach, which allows both individual and trial level surrogacy, has become well accepted. However, the meta-analytic joint bivariate full models suffer computational issues. To ease the challenge, aggregate data may be used but it leads to loss of information. In this manuscript the direct application of individual level (instead of aggregate) data in a Bayesian Hierarchical Modelling framework was proposed. The proposed method uses reduced bivariate models with trial specific random effects of treatment on the endpoints and no correlated residuals. Simulated data consist several scenarios, each of which has 5000 participants data, 50 subgroups (used as trials) characterised by size of 100 participants per trial randomised in the ratio 1:1 to vaccinated and unvaccinated treatment groups. The meta-analysis showed improved quality of the CoP compared to literature based on aggregate data. There were no computational issues with the proposed hierarchical model.
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