Abstract:
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There is a wealth of data available for commonly used control treatments and, if properly integrated, the external control data could provide increased power, lowered costs, and other benefits to a study. We discuss a variety of methods to integrate historical control data to augment the current control arm of a study in a Bayesian and frequentist frameworks. We also discuss the potential of combining frequentist "all or nothing" methods like test-then-pool with Bayesian methods, and provide guidelines on how to compare these methods using simulations-based sensitivity analyses.
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