Direct utility of natural history data in analysis of clinical trials: Propensity match-based analysis of Omaveloxolone in Friedreich ataxia using the FA-COMS dataset

RationaleThe natural history of Friedreich Ataxia (FRDA) is being investigated in a multi-center longitudinal study designated the Friedreich Ataxia Clinical Outcome Measures Study (FA-COMS). To understand the utility of this natural history dataset in analysis of clinical trials, we performed a propensity-matched comparison of the data from the open-label MOXIe Extension (omaveloxolone) with that from FA-COMS. MethodsAll MOXIe Extension patients who had at least one post-baseline assessment were matched to FA-COMS patients using logistic regression to estimate propensity scores based on multiple covariates: sex, baseline age, age of onset, baseline modified Friedreich Ataxia Rating scale (mFARS) score, and baseline gait score. Selection of covariates was based on clinical relevance (i.e., factors considered prognostic for disease progression) and availability. The change from baseline in mFARS at Year 3 for the MOXIe Extension patients compared to the matched FA-COMS patients was analyzed as the primary efficacy endpoint using mixed model repeated measures analysis. ResultsData from the MOXIe Extension show that omaveloxolone provides persistent benefit over three years when compared to an untreated, rigorously matched cohort from FA-COMS. At each year, and in all analysis populations, patients in the MOXIe Extension experienced a smaller change from baseline in mFARS score than the matched FA-COMS patients. In the Primary Pooled Population (136 patients in each group) by Year 3, patients in the FA-COMS matched set progressed 6.6 points whereas patients treated with omaveloxolone in MOXIe Extension progressed 3 points (difference =-3.6; nominal p value =0.0001). Thus, progression in mFARS was slowed by 55% with omaveloxolone treatment. ConclusionsThese results suggest a clinically meaningful slowing of FRDA progression with omaveloxolone, and consequently details how propensity-matched analysis contributes to the understanding of the effects of therapeutic agents. This demonstrates the direct value of natural history studies in the evaluation of clinical trials.