Istore: a project on innovative statistical methodologies to improve rare diseases clinical trials in limited populations

Abstract

Abstract Background The conduct of rare disease clinical trials is still hampered by methodological problems. The number of patients suffering from a rare condition is variable, but may be very small and unfortunately statistical problems for small and finite populations have received less consideration. This paper describes the outline of the iSTORE project, its ambitions, and its methodological approaches. Methods In very small populations, methodological challenges exacerbate. iSTORE’s ambition is to develop a comprehensive perspective on natural history course modelling through multiple endpoint methodologies, subgroup similarity identification, and improving level of evidence. Results The methodological approaches cover methods for sound scientific modeling of natural history course data, showing similarity between subgroups, defining, and analyzing multiple endpoints and quantifying the level of evidence in multiple endpoint trials that are often hampered by bias. Conclusion Through its expected results, iSTORE will contribute to the rare diseases research field by providing an approach to better inform about and thus being able to plan a clinical trial. The methodological derivations can be synchronized and transferability will be outlined.

Keywords

• Bias assessment with multiple endpoints
• Finite populations
• Multiple endpoints
• Natural history modelling
• Rare disease clinical trials
• Similarity of subgroups