Using MRI whole brain atrophy and clinically reported outcomes in combination to assess interim treatment response in multi-arm multi-stage trials in progressive multiple sclerosis

Background: Interim stage outcomes in multi-arm multi-stage trials need not be the same as the final primary outcome, and should be selected with the goal of providing the best chance of continuing with an effective treatment in the study during the early stage where there is also potential to drop an ineffective arm. Jointly considering multiple outcomes can enhance that ability to detect an emerging signal. Methods: The Optimal Clinical Trials Platform for Progressive Multiple Sclerosis (OCTOPUS) study is a randomised, placebo-controlled, double-blind, phase 3, MAMS trial testing treatments for people with progressive multiple sclerosis. The interim analysis was to be based solely on an MRI outcome; reduction in whole brain atrophy rate. Accumulating data from external trials led to concern that this outcome may result in prematurely rejecting an effective treatment. As a solution we propose adding 3 clinical outcomes to the MRI outcome and propose a multivariate mixed model to accommodate them jointly, despite significant differences in scale and even measurement type. We show how use of the model-derived covariances allows us to linearly combine the treatment effects of disparate outcomes into a single treatment effect. We also describe how to analytically calculate power for this combination and compared it to the individual components' performance. Results: Based on variance data from a previous study, we found power was increased moderately by 7%, from 83% to our target 90% given the assumed respective treatment effect sizes for OCTOPUS. When considering observed effect sizes from other trials these power improvements were maintained, despite there being great variability between the outcome effects. Conclusions: Whilst not greatly boosting power, we argue that this strategy improves the interim outcome measure by also making it more resilient to the uncertainty surrounding effect size, and mitigating against unexpected negative results by spreading the liability across related but distinct outcomes.