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    Rights statement: Copyright © 2014 ©?2014 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Optimising the data combination rule for seamless phase II/III clinical trials

Research output: Contribution to journalJournal articlepeer-review

Published
<mark>Journal publication date</mark>01/2015
<mark>Journal</mark>Statistics in Medicine
Issue number1
Volume34
Number of pages20
Pages (from-to)39-58
Publication StatusPublished
Early online date15/10/14
<mark>Original language</mark>English

Abstract

We consider seamless Phase II/III clinical trials which compare K treatments with a common control in Phase II, then test the most promising treatment against control in Phase III. The final hypothesis test for the selected treatment can use data from both Phases, subject to controlling the familywise type I error rate. We show that the choice of method for conducting the final hypothesis test has a substantial impact on the power to demonstrate that an effective treatment is superior to control. To understand these differences in power we derive optimal
decision rules, maximising power for particular configurations of treatment effects. Rules with optimal frequentist properties are found as solutions to multivariate Bayes decision problems. Although the optimal rule depends on
the configuration of treatment means considered, we are able to identify two decision rules with robust efficiency: a rule using a weighted average of the Phase II and Phase III data on the selected treatment and control, and a closed testing procedure using an inverse normal combination rule and a Dunnett test for intersection hypotheses. For the first of these rules, we find the optimal division of a given total sample size between Phase II and Phase III.We also assess the value of using Phase II data in the final analysis and find that for many plausible scenarios, between 50% and 70% of the Phase II numbers on the selected treatment and control would need to be added to the Phase III sample size in order to achieve the same increase in power.

Bibliographic note

Copyright © 2014 © 2014 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.