Comparing the MAMS framework with the combination method in multi-arm adaptive trials with binary outcomes. (June 2019)

• Record Type: Journal Article
• Authors: Abery, Julia E; Todd, Susan
• Is Part Of: Statistical methods in medical research. Volume 28:Number 6(2019)
• Page Start: 1716
• Page End: 1730
• Publication Date: 2019-06
• Subjects: Multi-arm adaptive trials -- MAMS -- combination method -- log odds ratio -- selection rule -- family-wise error rate; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280218773546
• Languages: English
• ISSNs: 0962-2802

Abstract

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.