Efficacy Assessment in Trials of Complex and Rare Diseases: A Comparison Between the Meta-Analytic Global Statistical Test and Co-Primary Analysis

Background: Complex and rare diseases often have heterogeneous symptoms, which complicates the selection of an appropriate primary outcome because outcome assessments rarely capture all aspects of disease. Disease-modifying treatments (DMTs) are expected to affect all disease domains, and even symptomatic treatments can affect multiple aspects of disease; therefore, a single outcome will rarely be sufficient to measure the success of a treatment. To address this issue, regulatory bodies often suggest co-primary endpoints. However, obtaining statistical significance on two outcomes is a much stricter requirement, which could create additional hurdles for effective treatments. Global statistical test (GST) combines multiple outcomes into a single score and could provide a viable alternative to the co-primary approach. Importantly for rare diseases, combining multiple assessments reduces the risk of selecting a poor outcome simply because it has not been studied as extensively as those for more common diseases. Here we compare GST to single primary and co-primary methodologies using simulations of a crossover study with two outcomes that may be moderately or highly correlated using several effect sizes. Results: For the same effect size on both outcomes, GST had greater power than single primary and co-primary approaches, regardless of the correlation level between outcomes. This was also true with different effect size combinations at the same correlation level. With an effect observed on one outcome only, GST was more likely to yield statistical significance than theco-primary approach. Unlike the co-primary approach, The GST yielded lower p-values in scenarios with lower correlation between the outcomes. Conclusions: GST favors independent information (ie, outcomes with moderate or poor correlation), does not reduce statistical power, and is not overtly permissible in cases of null effect on one of the outcomes. Compared to co-primary endpoints, the higher statistical power of GST is especially suited for rare diseases withsample size limitation. GST is a viable approach in analyzing data from heterogeneous outcomes and should be considered over co-primary approaches, especially for treatments that target multiple aspects of the disease or multiple symptoms.