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Abstract:
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In recent years, the idea of augmenting randomized clinical trial data with real-world data has emerged as a particularly attractive technique for health organizations and drug developers to accelerate the drug development process. Major regulatory authorities such as the Food and Drug Administration and European Medicines Agency have recognized the potential of utilizing real-world data and are advancing toward making regulatory decisions based on real-world evidence. In recent years, several statistical methods have been developed for borrowing data from real-world sources such as electronic health records, products and disease registries, and claims and billing data. We develop a novel approach to augment single-arm clinical trials with real-world data derived from single or multiple data sources. Furthermore, we illustrate the proposed method in the presence of missing data and conduct simulation studies to evaluate the performance of the proposed method in diverse settings.
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