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Abstract:
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(*) Student Paper Award Winner: Multi-regional clinical trials (MRCTs) provide the benefit of more rapidly introducing drugs to the global market, however, small regional sample sizes can lead to poor inference quality of regional effects. With the 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 improve the estimation quality of regional treatment effects. We develop novel methodology for estimating regional and global treatment effects for 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. We propose an approach that uses posterior model probabilities to quantify evidence in favor of consistency of treatment effects across all regions. We show through simulations that our modeling approach results in lower MSE than a fixed-effects linear regression model and better control of type I error rates than a Bayesian hierarchical model.
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