Development of a fully Bayesian framework for the identification and estimation of subgroup effects in Randomised Controlled Trials

开发一个完整的贝叶斯框架，用于识别和估计随机对照试验中的亚组效应

• 批准号: MR/N015630/1
• 负责人: Neil Hawkins
• 金额: $ 61.69万
• 依托单位: University of Glasgow
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FN015630%2F1
• 关键词: Development; fully; Bayesian; framework; identification

Population health can be improved by identifying treatments that work particularly well for individual patients. This is commonly described as stratified, precision, or personalised medicine. Personalised medicine is underpinned by subgroup analyses where the effectiveness of treatments are assess in subsets of study patients with defined characteristics. However, we need to account for the effects of chance, if we compare treatment response in a large number of subgroups we are likely to observe differences that arise purely by chance. For example, a study reported in 2005 listed ten cases where apparently clinically important subgroup effects were subsequently been shown to be false, such as the claim that aspirin for secondary prevention of stroke was ineffective in women. Unreliable subgroup analysis can lead to patients suffering harm, the waste of scarce resources, and futile confirmatory trials. The conventional approach is to advise caution, apply qualitative checklists to assess credibility, and apply statistical corrections. However, these approaches do not directly address the biases caused by multiplicity and leave the burden on the decision-maker to balance the risk of accepting "spurious" subgroup effects against the risk of rejecting true subgroup effects. This challenge can be seen in the FDA deliberations over the PLATO trial, where a treatment effect appeared to reverse in North American patients, and the differing decisions made by NICE and IQ-WiG based on subgroup analysis from the CAPRIE trial. Prior plausibility is identified as an important criterion for the credibility subgroup effects, but there is little guidance regarding how and when these judgements should be elicited. They are rarely elicited and recorded before trials commence, leaving decision-makers attempting to judge plausibility after trial results have been published. In this project, we will develop a fully Bayesian framework for subgroup analysis comprising a statistical model that adjusts for the effects of multiple testing and a framework for elicitation of judgements regarding the plausibility of subgroup effects. Prior plausibility is an important criterion for judging the credibility of subgroup effects but there is little guidance as to how and when plausibility should be assessed and reported. Importantly the statistical model will allow for the observed subgroup effects to represent a mixture of "true" reproducible subgroups and spurious effects caused purely by random chance. The Bayesian model will provide a direct estimate of the probability that a subgroup effect occurred by chance and will also be able to incorporate informative priors representing belief in the plausibility of subgroup effects. The framework will be tested in a number of real life case studies representing study design, commissioning of confirmatory studies based on subgroup analysis, and technology appraisal. Guidance on the use of the framework, to facilitate translation, will be developed based on the case studies.

通过确定对个体患者特别有效的治疗方法可以改善人群健康。这通常被描述为分层、精准或个性化医疗。个性化医疗以亚组分析为基础，在具有明确特征的研究患者亚组中评估治疗的有效性。然而，我们需要考虑机会的影响，如果我们比较大量亚组的治疗反应，我们可能会观察到纯粹偶然产生的差异。例如，2005 年报道的一项研究列出了 10 个明显具有临床重要亚组效应的案例，随后被证明是错误的，例如声称阿司匹林用于中风二级预防对女性无效。不可靠的亚组分析可能导致患者遭受伤害、稀缺资源的浪费以及无效的验证性试验。传统的方法是建议谨慎行事，应用定性检查表来评估可信度，并应用统计修正。然而，这些方法并没有直接解决由多重性引起的偏差，而是给决策者留下了平衡接受“虚假”子组效应的风险与拒绝真实子组效应的风险的负担。这一挑战可以从 FDA 对 PLATO 试验的审议中看出，其中北美患者的治疗效果似乎出现逆转，以及 NICE 和 IQ-WiG 根据 CAPRIE 试验的亚组分析做出的不同决定。先前的合理性被认为是可信亚组效应的重要标准，但关于如何以及何时得出这些判断几乎没有指导。它们很少在试验开始前被引出和记录，使得决策者试图在试验结果公布后判断其合理性。在这个项目中，我们将开发一个用于亚组分析的完整贝叶斯框架，其中包括一个根据多重测试的影响进行调整的统计模型，以及一个用于得出有关亚组效应合理性判断的框架。先前的合理性是判断亚组效应可信度的重要标准，但对于如何以及何时评估和报告合理性几乎没有指导。重要的是，统计模型将允许观察到的子组效应代表“真实”可再现子组和纯粹由随机机会引起的虚假效应的混合。贝叶斯模型将提供对子组效应偶然发生的概率的直接估计，并且还能够结合代表对子组效应的合理性的信念的信息先验。该框架将在一些现实生活案例研究中进行测试，这些案例研究代表研究设计、基于亚组分析的验证性研究的委托以及技术评估。将根据案例研究制定框架使用指南，以促进翻译。