An optimised multi-arm multi-stage clinical trial design for unknown variance.
Authors
Grayling, Michael J
Wason, James MS
Mander, Adrian P
Publication Date
2018-04Journal Title
Contemp Clin Trials
ISSN
1551-7144
Publisher
Elsevier BV
Volume
67
Pages
116-120
Language
eng
Type
Article
This Version
VoR
Physical Medium
Print-Electronic
Metadata
Show full item recordCitation
Grayling, M. J., Wason, J. M., & Mander, A. P. (2018). An optimised multi-arm multi-stage clinical trial design for unknown variance.. Contemp Clin Trials, 67 116-120. https://doi.org/10.1016/j.cct.2018.02.011
Abstract
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.
Keywords
Humans, Endpoint Determination, Analysis of Variance, Data Interpretation, Statistical, Monte Carlo Method, Sample Size, Research Design, Clinical Trials as Topic
Sponsorship
MRC (unknown)
MRC (unknown)
Embargo Lift Date
2100-01-01
Identifiers
External DOI: https://doi.org/10.1016/j.cct.2018.02.011
This record's URL: https://www.repository.cam.ac.uk/handle/1810/280278
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