Comparing the MAMS framework with the combination method in multi-arm adaptive trials with binary outcomes

Abery, J. E. and Todd, S. ORCID: https://orcid.org/0000-0002-9981-923X (2019) Comparing the MAMS framework with the combination method in multi-arm adaptive trials with binary outcomes. Statistical Methods in Medical Research, 28 (6). pp. 1716-1730. ISSN 0962-2802 doi: 10.1177/0962280218773546

Abstract/Summary

In multi-arm adaptive trials, several treatments are assessed simultaneously and accumulating data are used to inform decisions about the trial, such as whether treatments are dropped or continued. Different methodological approaches have been developed for such trials and research has compared the performance of different subsets of these. One particular approach, for which we use the acronym MAMS(R), has generally not been included in these comparisons because control of the family-wise error rate (FWER) could not be guaranteed. Recently, the MAMS(R) approach has been extended to facilitate the generation of efficient designs which strongly control the FWER. We consider multi-arm two-stage trials with binary outcomes and propose parameterising treatment effects using the log odds ratio. We conduct a simulation study comparing the extended MAMS(R) framework with the well-established combination method both for trials where a different outcome is used for mid-trial analysis and for trials where the same outcome is used throughout. We show how the MAMS(R) framework compares favourably only in scenarios where the same outcome is used. We propose a hybrid selection rule within MAMS(R) methodology and demonstrate that this makes it possible to use the MAMS(R) framework in trials incorporating comparative treatment selection.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/76420
Identification Number/DOI	10.1177/0962280218773546
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics > Applied Statistics
Publisher	SAGE
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