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长的公共卫生问题：超过 3000 万人 全世界患有 AD 的人数预计到 2050 年将增加四倍。 据报道 AD 是第三大疾病 美国的死因。随着这场迫在眉睫的全球公共卫生危机，预防发病或减缓发病的治疗方法 AD 进展的研究是迫切需要的，但在 aducenumab 最近加速批准之前很少可用。 因此，当整体情况 治疗效果极小或没有临床意义。如果能够识别出这样的亚群，那么其中一些 阴性试验的治疗方法已被证明至少可以帮助一部分 AD 人群。在这个提案中 我们将在混合模型上采用基于非参数交互树（IT）的方法来进行重复测量 （MMRM）和基于回归的方法来识别此类亚群。 MMRM 的 IT 建立在评估的基础上 协变量相互作用的治疗，并且可以自动寻找个体的亚组，其中 治疗显示出异质性效果。我们还探索了一种新的、更具吸引力的融合惩罚方法 最终的树确定无需任何分组信息的先验知识。基于回归的方法的目标 根据亚群的特征来确定哪些亚群将从 AD 治疗中受益，这是非常灵活的 从而做出个体化的治疗选择。最后，我们将开发并传播一个用户友好的统计数据 软件包将使研究人员能够轻松实施这些方法。我们的扩展会更好 捕捉疾病进展中的个体异质性并促进基于证据的精准医学 未来的AD研究和其他研究领域。