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

人们普遍认识到，实验性疗法可能只会使患者群体中的一部分受益。一种有效的临床试验设计，以测试一种实验性疗法是否有效，以及对哪部分人群是两阶段适应性浓缩设计。该试验最初从全部患者群体中招募新人，然后在试验进行到一半时，进行中期分析以选择实验疗法有希望的亚群。中期分析后的招聘仅限于这一亚群。有时，现有的计算效果大小的可信区间(CI)的方法给出的CI是已知不准确的，该区间包含具有指定概率的真实效果大小。这对决策者没有帮助，因此博士项目的目标是开发出具有更好性质的可信区间的方法。

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.