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As the treatment landscapes have grown increasingly competitive across disease areas, personalized medicine has emerged and drug development costs have soared, there is an increasing interest from sponsors and health authorities (e.g. 21st Century Cures Act) in leveraging all available information to reduce patient burden, making crucial development decisions faster and getting drugs to patients sooner.
RWD and/or historical data often provide valuable information to design a study and to increase certainty during analyses or making interim decisions. Applications of leveraging such data show a wide range of opportunities throughout all phases of development.
Bayesian meta-analytic approaches have been developed to leverage such data for various endpoints including binary and time-to-event endpoints. The approaches build on robust hierarchical models and allow for various degrees of between-trial heterogeneity. The methods account for leveraging individual as well as aggregate data. For some studies, we discuss analyses, and simulations to assess the operating characteristics of designs leveraging RWD and/or historical data in the presence or absence of prior-data conflict.
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