贝叶斯概率
计算机科学
控制(管理)
贴现
贝叶斯统计
贝叶斯推理
数据科学
风险分析(工程)
数据挖掘
机器学习
人工智能
医学
财务
经济
作者
Emmanuel Spanakis,Martina Kron,Mareike Bereswill,Snehasis Mukhopadhyay
标识
DOI:10.1080/10543406.2022.2152833
摘要
Conducting a well-powered and adequately controlled clinical trial in children is often challenging. Bayesian approaches are an attractive option for addressing such challenges as they provide a quantitatively rigorous and integrated framework that makes use of current control data to check and borrow information from historical control data. However various practical concerns and related statistical issues emerge when implementing such Bayesian borrowing approaches. In this manuscript we use a motivating case study to discuss a rigorous stepwise approach on how to address those issues within the Bayesian framework. Specifically, a comprehensive quantitative framework is proposed to assess the extent, synergy, and impact of borrowing. Steps on computing the measures to interpret borrowing are illustrated. Those measures can further help to determine whether additional discounting of external information is necessary.
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