Bayesian Methods for Sample Size Re-estimation

这个项目将开发和评估新的贝叶斯方法，以重新估计临床试验所需的样本量。总是假设试验的样本量，如治疗效果的变异性或大小。这些假设用于确定最终试验的特定成功概率(POS)所需的参与者数量。POS直接被制药公司用来做出最佳决策。当设计假设不正确时，样本大小可能不够。样本量重新估计方法引入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)可以结合其他数据来源，结合试验数据，特别感兴趣的领域是：1)贝叶斯SSR能否处理盲重估方法(即不使用臂分配的情况)？2)与频率法相比，贝叶斯SSR方法能否提高第三阶段研究成功的概率？3)方法如何扩展到其他端点类型，如二进制和事件间隔时间？4)我们如何调整方法，以考虑更复杂的设计，如平台、雨伞和篮子？

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?