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Abstract:
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Multi-regional clinical trials (MRCTs) provide the benefit of rapidly introducing drugs to the global market, however, current statistical methods pose limitations to the control of information sharing and estimation of regional treatment effects. With the recent publication of the ICH E17 guideline in 2017, the MRCT design is recognized as a viable strategy that can be accepted by regional regulatory authorities, necessitating new statistical methods that overcome the challenge of information sharing. We develop novel methodology for estimating regional and global treatment effects from MRCTs using Bayesian model averaging. This approach can be used for trials that compare two treatment groups with respect to a continuous outcome, and it allows for the incorporation of patient characteristics through the inclusion of covariates. Posterior model probabilities provide a natural assessment of consistency between regions that can be used by regulatory authorities for drug approval. We compare our method to existing methods, including linear regression with common treatment effect and a Bayesian hierarchical random effects model, and the results from simulations are presented.
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