Developing new methods for computing confidence intervals for clinical trials with subpopulation selection

人们普遍认为，一种实验性疗法可能只对一小部分患者有益。一种有效的临床试验设计，用于测试实验性治疗是否有效，以及对哪一部分人群采用两阶段适应性富集设计。该试验最初从整个患者群体中招募，然后在试验进行到一半的时候，进行中期分析，以选择实验治疗有希望的亚群体。中期分析后的招募仅限于这一亚群。有时，现有的计算效应大小置信区间（CI）的方法给出的CI已知是不准确的，CI是一个以特定概率包含真实效应大小的区间。这对决策者没有帮助，因此博士项目的目标是开发具有更好性质的置信区间的方法。

It is widely recognised that an experimental therapy may only benefit a subset of a patient population. An efficient clinical trial design to test whether an experimental therapy is effective and for which part of the population is the two-stage adaptive enrichment design. The trial initially recruits from the full patient population and then partway through the trial, an interim analysis is performed to select the subpopulation where the experimental therapy is promising. Recruitment after the interim analysis is restricted to this subpopulation. Sometimes, the existing method for computing a confidence interval (CI) for the effect size, which is an interval that contains true effect size with a specified probability, gives CIs that are known to be inaccurate. This is not helpful to decision makers so that the aim of the PhD project is to develop methods that give confidence intervals with better properties.