Trusted Resource for Clinical Trials, Protocols & Progress
One of the most complex and sensitive decisions in rare disease clinical trials is determining the appropriate sample size. Unlike trials for common diseases where thousands of participants may be enrolled, rare disease studies often struggle to recruit even dozens of patients globally. This scarcity makes traditional power-based calculations difficult to apply directly.
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Rare disease clinical trials frequently face recruitment challenges due to small patient populations, ethical concerns with placebo groups, or urgency in life-threatening conditions. In such contexts, historical controls—data from previously treated patients not enrolled in the current trial—can serve as comparators to evaluate investigational therapies.
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In the realm of rare and ultra-rare genetic diseases, traditional clinical trial structures often fall short due to limited patient populations and disease heterogeneity. Basket trials provide an innovative solution by testing a single investigational therapy across multiple diseases or indications that share a common genetic mutation or biomarker.
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Rare disease clinical trials often undergo frequent protocol changes due to the evolving understanding of disease mechanisms, adaptive study designs, small patient populations, and safety considerations. These amendments—whether substantial or administrative—must be carefully managed to maintain regulatory compliance, ethical oversight, and data integrity.
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In rare disease clinical trials, defining suitable endpoints is one of the most critical and complex tasks. With small populations, heterogeneous symptoms, and limited natural history data, selecting the right efficacy measure directly impacts trial success and regulatory approval.
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Statistical power—the probability of detecting a true treatment effect—is a cornerstone of robust clinical trial design. In traditional studies, large sample sizes provide the necessary power. However, rare disease trials face the opposite challenge: small and often heterogeneous patient populations that make achieving adequate power difficult.
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