Bayesian Methods for Sample Size Re-estimation

样本量重新估计的贝叶斯方法

• 批准号: 2884699
• 金额: --
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2884699
• 关键词: Bayesian; Methods; Sample; Size; Re

This project will develop and evaluate new Bayesian approaches for re-estimating the sample size needed for a clinical trial as it progresses.There are always assumptions made to determine the sample size of a trial, such as the variability or size of treatment effect. These assumptions are used to decide the number of participants needed for a specified probability of success (PoS) for the definitive trial. PoS is used directly by pharmaceutical companies to make optimal decisions. When design assumptions are incorrect then the sample size may not be sufficient. Sample size reestimation approaches introduce an interim analysis to assess assumptions and alter the sample size using actual trial data.Bayesian Sample Size Reestimation (SSR) can allow incorporating other sources of data, in conjunction with the trial data, to improve the re-estimation.Areas of particular interest are:1) Can Bayesian SSR handle blinded reestimation approaches (i.e. where arm assignment is not used)?2) Would a Bayesian SSR approach improve the probability of a successful phase 3 study ascompared to frequentist approaches?3) How do methods extend to other endpoint types like binary and time-to-event?4) How can we adapt approaches to consider more complex designs such as platform, umbrellaand basket?

该项目将开发和评估新的贝叶斯方法，用于重新估计临床试验所需的样本量。在确定试验样本量时，总是会做出假设，例如治疗效果的变异性或大小。这些假设用于确定确定性试验的特定成功概率（PoS）所需的受试者人数。制药公司直接使用PoS来做出最佳决策。当设计假设不正确时，样本量可能不足。样本量重新估计方法引入了中期分析来评估假设并使用实际试验数据改变样本量。贝叶斯样本量重新估计（SSR）可以允许将其他数据来源与试验数据结合起来，以改善重新估计。特别感兴趣的领域是：1）贝叶斯SSR可以处理盲态重新估计方法（即不使用臂分配的情况）吗？2)与频率论方法相比，贝叶斯SSR方法是否能提高3期研究成功的概率？3)方法如何扩展到其他端点类型，如二进制和事件时间？4)我们如何调整方法来考虑更复杂的设计，如平台，雨伞和篮子？