PDF(Pubmed)
Abstract：
Bayesian statistics plays a pivotal role in advancing medical science by enabling healthcare companies, regulators, and stakeholders to assess the safety and efficacy of new treatments, interventions, and medical procedures. The Bayesian framework offers a unique advantage over the classical framework, especially when incorporating prior information into a new trial with quality external data, such as historical data or another source of co-data. In recent years, there has been a significant increase in regulatory submissions using Bayesian statistics due to its flexibility and ability to provide valuable insights for decision-making, addressing the modern complexity of clinical trials where frequentist trials are inadequate. For regulatory submissions, companies often need to consider the frequentist operating characteristics of the Bayesian analysis strategy, regardless of the design complexity. In particular, the focus is on the frequentist type I error rate and power for all realistic alternatives. This tutorial review aims to provide a comprehensive overview of the use of Bayesian statistics in sample size determination, control of type I error rate, multiplicity adjustments, external data borrowing, etc., in the regulatory environment of clinical trials. Fundamental concepts of Bayesian sample size determination and illustrative examples are provided to serve as a valuable resource for researchers, clinicians, and statisticians seeking to develop more complex and innovative designs.
摘要：
贝叶斯统计在推动医疗科学发展方面发挥着关键作用，监管者,和利益相关者评估新疗法的安全性和有效性，干预措施，和医疗程序。贝叶斯框架比经典框架具有独特的优势，特别是当将先前的信息与高质量的外部数据结合到新的试验中时，例如历史数据或其他共同数据源。近年来,由于其灵活性和为决策提供有价值的见解的能力，使用贝叶斯统计的监管提交显著增加，解决临床试验频率不足的现代复杂性。对于监管提交，公司通常需要考虑贝叶斯分析策略的频繁经营特征，不管设计的复杂性。特别是,重点是所有现实替代方案的I型频繁错误率和功率。本教程综述旨在全面概述贝叶斯统计在样本量确定中的使用，控制I型错误率，多重性调整，外部数据借用，等。,在临床试验的监管环境中。提供了贝叶斯样本量确定的基本概念和说明性示例，作为研究人员的宝贵资源，临床医生,和统计学家寻求开发更复杂和创新的设计。
