Do we need to adjust for interim analyses in a Bayesian adaptive trial design?

BMC Medical Research Methodology
Elizabeth G RyanDaniel Slade

Abstract

Bayesian adaptive methods are increasingly being used to design clinical trials and offer several advantages over traditional approaches. Decisions at analysis points are usually based on the posterior distribution of the treatment effect. However, there is some confusion as to whether control of type I error is required for Bayesian designs as this is a frequentist concept. We discuss the arguments for and against adjusting for multiplicities in Bayesian trials with interim analyses. With two case studies we illustrate the effect of including interim analyses on type I/II error rates in Bayesian clinical trials where no adjustments for multiplicities are made. We propose several approaches to control type I error, and also alternative methods for decision-making in Bayesian clinical trials. In both case studies we demonstrated that the type I error was inflated in the Bayesian adaptive designs through incorporation of interim analyses that allowed early stopping for efficacy and without adjustments to account for multiplicity. Incorporation of early stopping for efficacy also increased the power in some instances. An increase in the number of interim analyses that only allowed early stopping for futility decreased the type I e...Continue Reading

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Citations

Aug 8, 2021·International Journal of Environmental Research and Public Health·Paola BerchiallaIleana Baldi
Dec 3, 2021·Intensive Care Medicine·Anders GranholmMorten Hylander Møller
Dec 7, 2021·Conservation Biology : the Journal of the Society for Conservation Biology·Deborah G Mayo

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