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.

治疗阿尔茨海默氏病（AD）的药物开发一直具有挑战性且昂贵。 在很大程度上，药物衰竭很可能是由于患者对患者的不同反应 不同的治疗方法。一些患者子集有治疗主持人并做出反应 对不同。由于样本量有限，识别这种响应性子集已经具有挑战性 在一项临床试验中，或可能超出单个临床中临时分析的范围 试验，考虑到AD的复杂性。患者的另一个重要子集很快 经验者，他们在定义的时期内的认知能力下降速度更快，并且可能会做出回应 与其他AD患者的治疗不同。预测快速进步者及其 差异反应非常具有挑战性。机器学习预测并不比 由于认知得分的波动和综合性不足和综合性和 细粒纵向临床数据。从多个临床试验数据中汇总患者级数据 可以通过增加样本量并获得更好的挑战来应对上述挑战 患者人群的覆盖范围/表示。但是，存储了许多临床试验数据 在分布式数据访问服务器中，数据使用协议通常禁止出口患者 - 将数据从本地服务器中排出。我们旨在通过高级信息学应对挑战 使用AI/ML型号的工具。我们将开发保护隐私的联合模型以协调一致 局部反事实效应估计模型中的全球模型，而无需交换患者 - 级别数据。 AIM 1专注于开发基于差异的联合亚组模型 回答。 AIM 2专注于使用使用联合反事实回归模型 深度学习以预测快速进步者及其不同的反应。 AIM 3专注于 在全国范围内使用现实世界观察来验证和完善亚组预测 财团数据。如果成功，该项目将有助于确定患者子组 反应不同，这将导致较小，更便宜且针对性的广告临床 暴露于较少患者的试验中，他们不太可能采取的实验药物 回应。