With an increased application of Bayesian methods in clinical trials, a number of researchers have proposed Bayesian group sequential methods.
#Frequentist sequential testing trial#
Frequentist group-sequential designs enable interim analyses to be performed during the conduct of a clinical trial without inflation of the overall type I error rate. ConclusionsĬomparison of frequentist and Bayesian designs can encourage careful thought about design parameters and help to ensure appropriate design choices are made.Īn increasing desire for efficiency in clinical trials has led to growing interest in adaptive designs. In this setting the Bayesian test can only be guaranteed to control the type I error for a specified range of values of the control group treatment effect. In this case Bayesian and frequentist group-sequential tests cannot have the same stopping rule as the Bayesian stopping rule depends on the observed means in the two groups and not just on their difference. If the Bayesian critical values at different looks are restricted to be equal, O’Brien and Fleming’s design corresponds to a Bayesian design with an exceptionally informative negative prior, Pocock’s design to a Bayesian design with a non-informative prior and frequentist designs with a linear alpha spending function are very similar to Bayesian designs with slightly informative priors.This contrasts with the setting of a comparative trial with independent prior distributions specified for treatment effects in different groups. ResultsĪlthough Bayesian and frequentist group-sequential approaches are based on fundamentally different paradigms, in a single arm trial or two-arm comparative trial with a prior distribution specified for the treatment difference, Bayesian and frequentist group-sequential tests can have identical stopping rules if particular critical values with which the posterior probability is compared or particular spending function values are chosen. Focussing on the setting in which data can be summarised by normally distributed test statistics, we evaluate and compare boundary values and operating characteristics. This paper presents a practical comparison of Bayesian and frequentist group-sequential tests. This includes the use of stopping rules based on Bayesian analyses in which the frequentist type I error rate is controlled as in frequentist group-sequential designs. There is a growing interest in the use of Bayesian adaptive designs in late-phase clinical trials.
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