Harmonizing multiple clinical trials for Alzheimer's disease to investigate differential responses to treatment via federated counterfactual learning

协调阿尔茨海默病的多项临床试验，通过联合反事实学习研究对治疗的差异反应

• 批准号: 10714797
• 负责人: Xiaoqian Jiang
• 金额: $ 67.93万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 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 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10714797
• 关键词: Address; Affect; Agreement; Alzheimer' s Disease; Alzheimer' s disease pathology; Alzheimer' s disease patient; Antihypertensive Agents; Atrophic; Characteristics; Chronic; Clinical Data; Clinical Trials; Cognitive; Data; Data Set; Disease Progression; Ensure; Failure; Friends; Future; Galantamine; Gender; Gold; Grain; Hippocampus; Impaired cognition; Individual; Knowledge; Learning; Modality; Modeling; Nerve Degeneration; Obesity; Outcome; Outsourcing; Patients; Pharmaceutical Preparations; Pharmacologic Substance; Phase II Clinical Trials; Phenotype; Plasma; Policies; Population; Prediction of Response to Therapy; Reporting; Research; Risk; Sample Size; Subgroup; Subjects Selections; Training; comorbidity; data access; deep learning; distributed data; drug development; federated learning; forest; individual response; informatics tool; innovation; machine learning model; machine learning prediction; outcome prediction; patient population; patient response; patient subsets; phase III trial; predictive modeling; privacy preservation; prospective; randomized, clinical trials; response; sex; success; therapy development; transmission process; treatment effect; treatment response

Drug development for treating Alzheimer's disease (AD) has been challenging and expensive. Drug failures are very likely due, in large part, to the differential responses of patients to different treatments. Some subsets of patients have treatment moderators and respond differently. Identifying such responsive subsets has been challenging due to limited sample size in one clinical trial or may be beyond the scope of the ad-hoc analyses in individual clinical trials, considering the complexity of AD. Another important subset of patients are rapid progressors, who have faster rates of cognitive decline in a defined period and may respond differently to treatments than other AD patients. Predicting the rapid progressors and their differential responses is very challenging. Machine learning prediction has been no better than random guesses due to volatility of cognitive scores and insufficiency of comprehensive and fine-grained longitudinal clinical data. Pooling patient-level data from multiple clinical trials data may address the above challenges by increasing sample size and obtaining a better coverage/representation of the patient population. However, many clinical trials data are stored in distributed data access servers, and data use agreements often prohibit exporting the patient- level data out of the local servers. We aim to address the challenges via advanced informatics tools using AI/ML models. We will develop privacy-preserving federated models to harmonize local counterfactual effect estimation models into a global model without exchanging patient- level data. Aim 1 focuses on developing a federated subgrouping model based on differential responses. Aim 2 focuses on developing a federated counterfactual regression model using deep learning to predict rapid progressors and their differential responses. Aim 3 focuses on verifying and refining the subgroups prediction using real-world observation in nation-wide consortium data. If successful, this project will contribute to identifying patient subgroups that respond differently, which will result in smaller, less expensive, and more targeted AD clinical trials that expose fewer patients to experimental medications to which they are unlikely to respond.

用于治疗阿尔茨海默病（AD）的药物开发一直具有挑战性且昂贵。 药物失败很可能在很大程度上归因于患者对药物的不同反应 不同的治疗。一些患者亚群有治疗调节剂， 不同.由于样本量有限，识别此类响应子集一直具有挑战性 在一项临床试验中，或可能超出个体临床试验中的特别分析范围 试验，考虑到AD的复杂性。另一个重要的患者亚群是快速 进展者，在规定的时间内认知能力下降的速度更快， 与其他AD患者的治疗不同。预测快速进展者及其 差异化反应是非常具有挑战性的。机器学习预测并不比 由于认知分数的波动性和综合性不足， 细粒度的纵向临床数据。合并来自多项临床试验数据的患者水平数据 可以通过增加样本量和获得更好的 患者人群的覆盖率/代表性。然而，许多临床试验数据被存储在 在分布式数据访问服务器中，数据使用协议通常禁止导出患者- 从本地服务器中删除数据。我们的目标是通过先进的信息学来应对挑战 使用AI/ML模型的工具我们将开发保护隐私的联邦模型， 将局部反事实效应估计模型转换为全局模型，而无需交换患者- 水平数据。Aim 1的重点是开发一个基于差分 应答Aim 2的重点是开发一个联邦反事实回归模型， 深度学习来预测快速进展者及其差异反应。目标3侧重于 在全国范围内使用真实世界观测来验证和改进子组预测 财团数据。如果成功，该项目将有助于确定患者亚组， 不同的反应，这将导致更小，更便宜，更有针对性的AD临床 试验使较少的患者接触到他们不太可能接受的实验性药物， 回答。