15 0 0 0 OA ベイズ流決定理論を用いる臨床試験:効用とサンプルサイズ設計
- 著者
- 坂巻 顕太郎 兼清 道雄 大和田 章一 松浦 健太郎 柿爪 智行 高橋 文博 高沢 翔 萩原 駿祐 森田 智視
- 出版者
- 日本計量生物学会
- 雑誌
- 計量生物学 (ISSN:09184430)
- 巻号頁・発行日
- vol.41, no.1, pp.55-91, 2020 (Released:2020-12-04)
- 参考文献数
- 44
It is common to use hypothesis testing to decide whether an investigational drug is ineffective and to determine sample size. However, it may not be good practice that only hypothesis testing is used for sample size determination, go/no-go decision making, and drug development decisions, especially in exploratory clinical trials. That is because important factors for decision making, such as treatment effects, drug development costs, and gains after launch, are not considered in hypothesis testing. The Bayesian decision theory is one of the approaches to consider such factors for decision making. The utility, which is defined by using important information such as cost, benefit, and disease severity, is used for decision making in the decision theory. In consideration of uncertainties of data and parameters, the expected value of the utility is used for decision making in the Bayesian decision theory. In this article, we explain basic concepts of the Bayesian decision theory, backward induction for calculation of expected value of utility in sequential decision-making, and introduce some approaches using the Bayesian decision theory in clinical trials. We summarize actions, utilities and sample size determination for applications of Bayesian decision theory in future clinical trials.
10 0 0 0 OA 臨床試験におけるヒストリカルコントロールデータの利用
- 著者
- 武田 健太朗 大庭 真梨 柿爪 智行 坂巻 顕太郎 田栗 正隆 森田 智視
- 出版者
- 日本計量生物学会
- 雑誌
- 計量生物学 (ISSN:09184430)
- 巻号頁・発行日
- vol.36, no.1, pp.25-50, 2015-07-20 (Released:2015-09-08)
- 参考文献数
- 47
- 被引用文献数
- 6 6
It is expected to develop new drug more efficiently by incorporating historical data into the current study data. Borrowing historical data which is sufficiently similar to the current data allows increasing power and improving the accuracy of the estimated treatment effect. On the other hand, if the historical data is not similar to the current data, there is a potential for bias and inflated type I error rate. Power prior and hierarchical model are widely known as the Bayesian approaches with borrowing strength from historical information. They have the advantage of deciding the amount of historical information continuously depending on the similarity between historical data and current data. Our goal is to introduce power prior and hierarchical model while showing some examples, and provide a review of points to keep in mind when these approaches are used in the clinical trials.
