Heterogeneity in treatment effects: Can modelling techniques provide personalized prediction of treatment response and uncover groups of respondents?

新药、卫生技术和非药物干预的随机试验通常旨在测试平均治疗效果。因此，在对这些试验的分析中，通常假定治疗效果在不同个体之间是恒定的。这一强有力的假设往往得不到经验证据或支持干预的理论的支持。虽然干预效果的异质性经常被预期，但对不同治疗效果的评估受到现有统计方法的限制。这项建议旨在开发和比较两种不同的统计方法，以调查如何最好地确定这些不同的影响，并检查这些方法预测个人对治疗的反应的能力。这项工作将利用最近在三个重要医学领域完成的三项研究：中风、癫痫和体力活动，这使得方法学工作、模拟和应用分析能够同时进行，从而创建一个反馈循环，在其中识别应用分析中出现的方法学问题，然后可以通过统计和模拟工作来解决，相互改进。多学科研究小组包括每项研究的有经验的实质性专家以及在随机研究的统计方法方面有经验的方法学家。

Randomized trials of novel drugs, health technologies and non-drug interventions are usually designed to test average treatment effects. In the analysis of these trials it is therefore typically assumed that treatment effects are constant across individuals. This strong assumption is often not supported by empirical evidence or the theories under-girding the intervention. While heterogeneity in intervention effects is often expected, the assessment of differential treatment effects is limited by available statistical methods. This proposal aims to develop and compare two different statistical methods to investigate how these differential effect can best be identified and examines the ability of these approaches to predict an individual's response to treatment. The work will utilize three recently finished studies in three important medical areas: Stroke, epilepsy and physical activity which enables methodological work, simulations, and applied analyses to be carried out simultaneously creating a feedback loop in which methodological problems that arise in applied analyses are identified and then can be addressed through statistical and simulation work, improving each. The multidisciplinary research team includes experienced substantive experts for each study as well as methodologists with experience in statistical methods for randomized studies.