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Abstract:
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Conducting clinical trials for disease with very small patient population has always been challenging due to recruitment difficulties. Therefore, it is hard to obtain sufficient study power to detect a treatment effect. Furthermore, randomization may not be feasible due to timeline, budget, and ethical concerns. To bring break-through therapies to the market quickly, it is important to come up with efficient approaches to utilize individual patient data through improved study design and sound statistical methods. In this talk, we consider three scenarios, i.e., ultra-rare disease, dose-response study in small patient population and a sequential parallel comparison design trial under platform setting, which highlight some of the challenges encountered in small-sized clinical trial development and provide potential statistical approaches to overcome these challenges, such as using Bayesian approaches to flexibly incorporate prior information into the current clinical trials, borrowing historical controls to efficiently conduct trials and using innovative study designs (i.e., platform design) to improve the efficiency and speed of the medical therapy development progress.
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