期刊
DRUG DISCOVERY TODAY
卷 23, 期 2, 页码 395-401出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.drudis.2017.09.016
关键词
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资金
- MIT Laboratory for Financial Engineering
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 (a) and power (1 (3) 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 as larger than the traditional one-sided significance level of 5%, whereas potentially less effective and riskier treatments have optimal as 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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