Lookup NU author(s): Dr Michael Grayling,
Professor James Wason
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.
Author(s): Grayling MJ, Wason JMS, Mander AP
Publication type: Article
Publication status: Published
Journal: Contemporary Clinical Trials
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.
Altmetrics provided by Altmetric