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

通过确定对个别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、精确或个性化的医学。个人化医学的基础是分组分析，其中治疗的有效性是在具有明确特征的研究患者的子集中进行评估的。然而，我们需要考虑偶然性的影响，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出现的差异。例如，2005年报告的一项研究列出了10个病例，其中明显具有临床重要性的亚组效应随后被证明是错误的，例如声称用于中风二级预防的阿司匹林对女性无效。不可靠的亚组分析可能会导致患者遭受伤害，浪费稀缺的资源，以及徒劳的验证性试验。传统的方法是建议谨慎，应用定性核对清单来评估可信度，并应用统计更正。然而，这些方法并没有直接解决多样性造成的偏见，而是让决策者承担起平衡接受“虚假”分组效应的风险和拒绝真正分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，在北美患者中的治疗效果似乎发生了逆转，NICE和IQ-WIG根据CAPRIE试验的亚组分析做出了不同的决定。先前的似是而非被认为是可信度小组效应的一个重要标准，但关于如何以及何时应该得出这些判断，几乎没有指导意见。它们很少在审判开始之前被引出和记录下来，使得决策者试图在审判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子组分析的完全贝叶斯框架，包括一个调整多重测试影响的统计模型和一个关于子组效应的可信判断的框架。先前的似是而非是判断分组效应可信度的重要标准，但对于应如何以及何时评估和报告似然性，几乎没有指导意见。重要的是，统计模型将允许观察到的子群效应代表“真实的”可重现的子群和纯粹由随机机会引起的虚假效应的混合。贝叶斯模型将提供子群效应偶然发生的概率的直接估计，并将能够纳入代表对子群效应的似是而非的信念的信息性先验。该框架将在一些现实生活中的案例研究中进行测试，这些案例包括研究设计、基于分组分析的验证性研究委托以及技术评估。将在个案研究的基础上制定关于使用该框架的指导意见，以促进翻译。

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.