Efficacy assessment in trials of complex and rare diseases: a comparison between the meta-analytic global statistical...

Choice of the primary outcome can be problematic in 1) diseases with heterogeneous signs and symptoms, 2) trials of disease-modifying treatments (DMTs) that are expected to affect all aspects of the disease, 3) in complex and rare diseases with minimal data on clinical trial outcomes. In such situations, a single outcome measuring a single domain of disease will rarely suffice as a primary outcome. To address this issue, regulatory bodies often suggest co-primary endpoints or require efficacy on both primary and key secondary endpoints for confirmatory trials, to ensure that at least two different domains of disease are affected by treatment. However, obtaining statistical significance on two outcomes is a much stricter requirement than on a single primary outcome. Global statistical tests (GST) combine 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 poorly performing outcome simply because it has not been studied extensively.

We conducted simulations to compare GST to single primary and co-primary endpoint approaches with two moderately or highly correlated outcomes with various effect sizes.

For scenarios with the same true effect size on both outcomes, the 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 the co-primary approach. Unlike the co-primary approach, The GST yielded lower p-values in scenarios with lower correlation between the outcomes due to the independence of the information from each endpoint.

Using GST as a prespecified endpoint is appropriate in trials where a clear primary endpoint has not been identified, sample sizes are insufficient to support multiple primary endpoints, and/or more comprehensive assessments across multiple endpoints are needed to fully evaluate outcomes.