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.

我们应用贝叶斯决策分析（BDA）将患者偏好纳入新疗法的监管批准流程中。通过根据患者的喜好为I型和II型错误分配权重，可以优化一项新疗法的随机临床试验（RCT）的显着性水平（α）和功效（1-  β），以最大程度地提高当前治疗方法和临床治疗方法的价值。未来的患者，进而影响公共健康。我们发现，对于减肥设备，潜在有效的低风险治疗方法的最佳α值比传统的单侧显着性水平5％大，而潜在无效的较低风险的治疗方法的最佳α值低于5％。此外，最佳的RCT设计（包括试验规模）会随着风险规避和获取时间的偏好以及目标人群的医疗需求而变化。