Increasing power in the analysis of responder endpoints in rheumatology: a software tutorial

Background: Composite responder endpoints feature frequently in rheumatology due to the multifaceted nature of many of these conditions. Current analysis methods used to analyse these endpoints discard much of the data used to classify patients as responders; they are therefore highly inefficient and result in low power. Methods: We highlight a novel augmented methodology that uses more of the information available to improve the precision of reported treatment effects. Since these methods are more challenging to implement, we have developed free, user friendly software available in a web-based interface. The software consists of two programs: one that supports the analysis of responder endpoints; the second is used for sample size estimation. We demonstrate the augmented analysis method and its software using the MUSE study, a phase IIb trial in patients with systemic lupus erythematosus. Results: We show the software can be used to analyse the trial with efficiency gains translating to a reduction in required sample size of 63%. Furthermore, we illustrate how the software can be used to choose the sample size needed in a future trial that will use the novel approach as the primary analysis method. Conclusion: We encourage trialists to utilise the software we have developed to implement augmented methodology in future studies to improve efficiency.