Clinical trials for Wolfram syndrome neurodegeneration: Novel design, endpoints, and analysis models

Objective: Wolfram syndrome, an ultra-rare condition, currently lacks effective treatment options. The rarity of this disease presents significant challenges in conducting clinical trials, particularly in achieving sufficient statistical power (e.g., 80%). The objective of this study is to propose a novel clinical trial design based on real-world data to reduce the sample size required for conducting clinical trials for Wolfram syndrome. Methods: We propose a novel clinical trial design with three key features aimed at reducing sample size and improve efficiency: (i) Pooling historical/external controls from a longitudinal observational study conducted by the Washington University Wolfram Research Clinic. (ii) Utilizing run-in data to estimate model parameters. (iii) Simultaneously tracking treatment effects in two endpoints using a multivariate proportional linear mixed effects model. Results: Comprehensive simulations were conducted based on real-world data obtained through the Wolfram syndrome longitudinal observational study. Our simulations demonstrate that this proposed design can substantially reduce sample size requirements. Specifically, with a bivariate endpoint and the inclusion of run-in data, a sample size of approximately 30 per group can achieve over 80% power, assuming the placebo progression rate remains consistent during both the run-in and randomized periods. In cases where the placebo progression rate varies, the sample size increases to approximately 50 per group. Conclusions: For rare diseases like Wolfram syndrome, leveraging existing resources such as historical/external controls and run-in data, along with evaluating comprehensive treatment effects using bivariate/multivariate endpoints, can significantly expedite the development of new drugs.