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

开发计算亚群选择临床试验置信区间的新方法

• 批准号: 2734726
• 金额: --
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2734726
• 关键词: Developing; new; methods; computing; confidence

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.

人们普遍认为，实验性疗法可能仅使患者人群的一部分受益。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部分人群有效，是两阶段适应性富集设计。试验最初从整个患者人群中招募，然后在试验中途进行中期分析，以选择实验治疗有希望的亚群。中期分析后的招募仅限于该亚群。有时，计算效应量置信区间（CI）的现有方法（CI是包含具有指定概率的真实效应量的区间）给出的CI已知是不准确的。这对决策者没有帮助，因此博士项目的目的是开发具有更好属性的置信区间的方法。