We apply Bayesian decision analysis (BDA) to incorporate patient preferences in the regulatory approval process for new therapies. By assigning weights to type I and type II errors based on patient preferences, the significance level (α) and power (1-β) of a randomized clinical trial (RCT) for a new therapy can be optimized to maximize the value to current and future patients and, consequently, to public health. We find that for weight-loss devices, potentially effective low-risk treatments have optimal αs larger than the traditional one-sided significance level of 5%, whereas potentially less effective and riskier treatments have optimal αs below 5%. Moreover, the optimal RCT design, including trial size, varies with the risk aversion and time-to-access preferences and the medical need of the target population.
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