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Abstract:
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Bayesian dynamic borrowing designs facilitate borrowing information from historical studies. Historical data, when perfectly commensurate with current data, have been shown to reduce the trial duration and the sample size, while inflation in the type I error and reduction in the power have been reported, when imperfectly commensurate. In the area of oncology, dynamic borrowing has been widely implemented in early phase platform trials with ORR as primary endpoints. However, for randomized studies with TTE endpoints, it is much less explored. This presentation will introduce the method of dynamic borrowing in the setting of randomized studies with TTE endpoints.
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