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Abstract:
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Real-world data collected from medical products, including drugs, biological products and medical devices, are becoming "big" as a result of advances in biomedical science, information technology and engineering. High-quality real-world data can be transformed into scientific evidence for regulatory and healthcare decision makings using proven analytical methods and techniques. We introduce approaches both in frequentist and Bayesian paradigm that use propensity score methodology for leveraging real-world data in the design and analysis of clinical studies. Case studies are provided to demonstrate the proposals in the context of regulatory review. A software that implements the proposed methods is also illustrated.
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