Innovative precision medicine methods in subgroup identification for Alzheimer's disease

阿尔茨海默病亚组鉴定的创新精准医学方法

• 批准号: 10740649
• 负责人: Lei Liu
• 金额: $ 23.33万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10740649
• 关键词: Acceleration; Address; Aducanumab; Affect; Aging; Alzheimer' s Disease; American; Area; Behavioral; Biological; Cause of Death; Characteristics; Clinical Data; Clinical Trials; Cognitive; Collaborations; Communities; Computer software; Country; Data; Dementia; Development; Disease; Disease Progression; Future; Goals; Grouping; Health; Healthcare; Individual; Investigational Drugs; Investigational Therapies; Knowledge; Machine Learning; Measures; Methodology; Methods; Modeling; Outcome; Patient Selection; Patients; Persons; Pharmaceutical Preparations; Pharmacotherapy; Population; Population Heterogeneity; Public Health; Reporting; Research; Research Personnel; Selection for Treatments; Subgroup; Therapeutic Agents; Treatment outcome; Trees; analytical method; analytical tool; data mining; design; donepezil; evidence base; flexibility; improved; individual variation; individualized medicine; innovation; interest; novel; novel therapeutics; payment; precision medicine; prevent; primary outcome; prospective; response; tool; treatment effect; treatment research; treatment response; treatment trial; user-friendly

is a major and rapidly increasing public health concern: over 30 million individuals worldwide suffer from AD, which is projected to quadruple by 2050. AD has been reported to be the third leading cause of death in the US. With this impending global public health crisis, treatments that prevent onset or slow progression of AD are urgently needed but rarely available until the recent accelerated approval for aducenumab. Therefore, it is of great interest to identify subpopulations which benefit most from a medication when the overall treatment effect is minimum or not clinically meaningful. If such subpopulations can be identified, some of the treatments from the negative trials can be proven to at least help a portion of the AD population. In this proposal we will employ non-parametric interaction tree (IT)-based methods on mixed models for repeated measures (MMRM) and regression-based methods to identify such subpopulations. IT for MMRM builds on the assessment of the treatment-by-covariates interactions and can automatically seek subgroups of individuals in whom the treatment shows heterogeneous effects. We also explore a new and more attractive fusion penalty approach for final tree determination without any prior knowledge of grouping information. The regression-based methods aim to identify subpopulations who will benefit from AD treatment based on their characteristics, which is very flexible to make individualized treatment selection. Finally, we will develop and disseminate a user-friendly statistical software package that will enable researchers to implement these methods with ease. Our extensions will better capture individual heterogeneity in disease progression and facilitate evidence-based precision medicine in future AD studies and other research areas.

是一个主要且迅速增加的公共卫生问题：超过3000万个人 全球遭受广告的困扰，预计到2050年将在四倍上进行四倍。 美国死亡原因。由于这种即将发生的全球公共卫生危机，预防发作或缓慢的治疗 迫切需要AD的进展，但很少能获得ADUCENUMAB的加速批准。 因此，确定在整个药物中受益最大的亚群是非常有趣的 治疗效果最少或没有临床意义。如果可以确定这样的亚群 可以证明来自负试验的治疗至少有助于部分广告人群。在此提案中 我们将在混合模型上采用非参数相互作用树（IT）的方法进行重复测量 （MMRM）和基于回归的方法来识别此类亚群。 IT用于MMRM在评估的基础上建立 逐疗的相互作用，并可以自动寻找个人的亚组 治疗显示出异质作用。我们还探索了一种新的，更具吸引力的融合惩罚方法 最终树确定，没有任何对信息进行分组的知识。基于回归的方法目的 确定将根据其特征从AD治疗中受益的亚群，这非常灵活 进行个性化的治疗选择。最后，我们将开发并传播一个用户友好的统计 软件包将使研究人员轻松实施这些方法。我们的扩展将更好 捕获疾病进展中的个人异质性，并促进基于证据的精确医学 未来的广告研究和其他研究领域。