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Clinical trials for Wolfram syndrome neurodegeneration: Novel design, endpoints, and analysis models
Guoqiao Wang1,2, Zhaolong Adrian Li3, Ling Chen2
1Department of Neurology, Washington University in St Louis School of Medicine, St Louis, Missouri, United States of America.
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.
Insights
This study introduces a new clinical trial design for Wolfram syndrome, using real-world data to significantly reduce the number of participants needed for rare disease research.
Area of Science:
- Rare disease clinical trial design
- Statistical methodology for rare diseases
- Real-world data utilization
Background:
- Wolfram syndrome is an ultra-rare genetic disorder with no effective treatments.
- Conducting clinical trials for rare diseases is challenging due to small patient populations and low statistical power.
Purpose of the Study:
- To propose a novel clinical trial design for Wolfram syndrome.
- To reduce the required sample size for clinical trials using real-world data.
Main Methods:
- A new clinical trial design incorporating historical/external controls from a longitudinal study.
- Utilizing run-in data for parameter estimation.
- Employing a multivariate proportional linear mixed effects model for dual endpoint analysis.
Main Results:
- Simulations based on real-world data indicate a substantial reduction in sample size.
- A sample size of approximately 30 per group can achieve over 80% power with a bivariate endpoint and run-in data.
- Sample size increases to approximately 50 per group if placebo progression rates vary.
Conclusions:
- Leveraging existing resources like historical controls and run-in data can expedite drug development for rare diseases.
- Multivariate endpoints enhance the evaluation of comprehensive treatment effects.
- This approach is crucial for advancing therapies for conditions like Wolfram syndrome.
