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Optimal data combination in seamless Phase II/III clinical trials

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If you have a question about this talk, please contact Dr Jack Bowden.

We consider seamless Phase II/III clinical trials which compare K treatments against a common control in stage 1 and select the most promising for further testing against control in stage 2. Such a trial requires careful upfront planning if it is to win regulatory acceptance as a pivotal study. For seamless trials to be attractive, this increased planning should be offset by efficiency gains made possible because data accumulated across the study are combined to make a final decision on the efficacy of the selected treatment. We derive optimal versions of final decision rules maximising power. This is a multivariate decision problem because properties of rules depend on a vector of means. Rules with the correct familywise error rate maximising power for different configurations of means are found as solutions to Bayes decision problems. Different solutions are found as the shape of the mean vector changes but we find only small gains in power are possible by making strong assumptions about the structure of the mean vector. By studying procedures with optimal decision rules, we assess the efficiency of alternative proposals, namely closed testing procedures based on p-value combination rules, and rules using only data on the selected treatment and control for final decisions. For procedures with efficient decision rules, we find that Phase II observations on the selected treatment and control retain between 22-98% of their value as Phase III observations. Thus, efficient seamless designs can offer large savings in sample size which may have important implications, for example, for the feasibility of trials in rare diseases.

This talk is part of the MRC Biostatistics Unit Seminars series.

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