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

治疗效果的异质性：建模技术能否提供治疗反应的个性化预测并揭示受访者群体？

• 批准号: MR/L010658/1
• 负责人: Thomas Jaki
• 金额: $ 50.34万
• 依托单位: Lancaster University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2014
• 资助国家: 英国
• 起止时间: 2014 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FL010658%2F1
• 关键词: Heterogeneity; treatment; effects; modelling; techniques

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.

新药、卫生技术和非药物干预措施的随机试验通常旨在测试平均治疗效果。因此，在这些试验的分析中，通常假设治疗效果在个体间是恒定的。这种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通常是可以预期的，但对不同治疗效果的评估受到现有统计方法的限制。本提案旨在开发和比较两种不同的统计方法，以研究如何最好地识别这些差异效应，并检查这些方法预测个体对治疗的反应的能力。这项工作将利用三个最近完成的研究在三个重要的医学领域：中风，癫痫和身体活动，使方法学工作，模拟和应用分析同时进行，创造一个反馈回路，其中方法学问题出现在应用分析中被识别，然后可以通过统计和模拟工作来解决，改善每一个。多学科研究团队包括每项研究的经验丰富的实质性专家以及具有随机研究统计方法经验的方法学家。

项目成果

Subgroup identification in clinical trials via the predicted individual treatment effect.

• DOI: 10.1371/journal.pone.0205971
• 发表时间: 2018
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Ballarini NM;Rosenkranz GK;Jaki T;König F;Posch M
• 通讯作者: Posch M

A Permutation Test for Assessing the Presence of Individual Differences in Treatment Effects

用于评估治疗效果个体差异存在的排列检验

• DOI: 10.48550/arxiv.1911.07248
• 发表时间: 2019
• 作者: Chang C