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An optimised multi-arm multi-stage clinical trial design for unknown variance

Lookup NU author(s): Dr Michael GraylingORCiD, Professor James WasonORCiD



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


© 2018 The Authors. Multi-arm multi-stage trial designs can bring notable gains in efficiency to the drug development process. However, for normally distributed endpoints, the determination of a design typically depends on the assumption that the patient variance in response is known. In practice, this will not usually be the case. To allow for unknown variance, previous research explored the performance of t-test statistics, coupled with a quantile substitution procedure for modifying the stopping boundaries, at controlling the familywise error-rate to the nominal level. Here, we discuss an alternative method based on Monte Carlo simulation that allows the group size and stopping boundaries of a multi-arm multi-stage t-test to be optimised, according to some nominated optimality criteria. We consider several examples, provide R code for general implementation, and show that our designs confer a familywise error-rate and power close to the desired level. Consequently, this methodology will provide utility in future multi-arm multi-stage trials.

Publication metadata

Author(s): Grayling MJ, Wason JMS, Mander AP

Publication type: Article

Publication status: Published

Journal: Contemporary Clinical Trials

Year: 2018

Volume: 67

Pages: 116-120

Print publication date: 01/04/2018

Online publication date: 21/02/2018

Acceptance date: 19/02/2018

Date deposited: 26/03/2018

ISSN (print): 1551-7144

ISSN (electronic): 1559-2030

Publisher: Elsevier Inc.


DOI: 10.1016/j.cct.2018.02.011


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