An important challenge in rare disease clinical trials is to demonstrate a clinically meaningful and statistically significant response to treatment. Selecting the most appropriate and sensitive efficacy endpoints for a treatment trial is part art and part science. The types of endpoints should align with the stage of development (e.g., proof of concept vs. confirmation of clinical efficacy). The patient characteristics and disease stage should reflect the treatment goal of improving disease manifestations or preventing disease progression. For rare diseases, regulatory approval requires demonstration of clinical benefit, defined as how a patient, feels, functions, or survives, in at least one adequate and well-controlled pivotal study conducted according to Good Clinical Practice. In some cases, full regulatory approval can occur using a validated surrogate biomarker, while accelerated, or provisional, approval can occur using a biomarker that is likely to predict clinical benefit. Rare disease studies are small by necessity and require the use of endpoints with large effect sizes to demonstrate statistical significance. Understanding the quantitative factors that determine effect size and its impact on powering the study with an adequate sample size is key to the successful choice of endpoints. Interpreting the clinical meaningfulness of an observed change in an efficacy endpoint can be justified by statistical methods, regulatory precedence, and clinical context. Heterogeneous diseases that affect multiple organ systems may be better accommodated by endpoints that assess mean change across multiple endpoints within the same patient rather than mean change in an individual endpoint across all patients.
Keywords: biomarker; efficacy; endpoint; minimal important difference; proof of concept; rare disease; standardized response mean.
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