Implementing treatment-selection rules for multi-arm multi-stage trials using nstage

Session

Multi-arm multi-stage (MAMS) randomised trial designs offer an efficient and practical framework for addressing multiple research questions. Typically, standard MAMS designs employ pre-specified interim stopping boundaries based on lack-of-benefit and/or over-whelming efficacy. To facilitate implementation, we have developed nstage suite of commands, which calculates the required sample sizes and trial timelines for a MAMS design.

In this talk, we introduce the MAMS selection design, integrating an additional treatment selection rule to restrict the number of research arms progressing to subsequent stages, in the event all demonstrate a promising treatment effect at interim analyses. The MAMS selection design streamlines the trial process by merging traditionally early-phase treatment selection with the late-phase confirmatory trial. As a result, it gains efficiency over the standard MAMS design by reducing overall trial timelines and required sample sizes. We present an update to the nstagebin Stata command which incorporates this additional layer of adaptivity, calculates required sample sizes, trial timelines, and overall familywise type I error rate and power for MAMS selection designs.

Finally, we illustrate how a MAMS selection design can be implemented using the nstage suite of commands and outline its advantages, using the ongoing trials in surgery (ROSSINI-2) and maternal health (WHO RED).