Closing the gap between observational research and randomized trials for prevention of Alzheimer's Disease and dementia

缩小预防阿尔茨海默病和痴呆症的观察性研究和随机试验之间的差距

• 批准号: 9765125
• 负责人: Medellena Maria Glymour
• 金额: $ 77.9万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9765125
• 关键词: Address; Alzheimer disease prevention; Alzheimer' s Disease; Alzheimer' s disease related dementia; Alzheimer’s disease biomarker; Area; Atherosclerosis Risk in Communities; California; Clinical; Data; Data Set; Data Sources; Dementia; Development; Diagnosis; Eligibility Determination; Enrollment; Evaluation; Future; Heterogeneity; Hypertension; Individual; International; Intervention; Literature; Measurement; Measures; Meta-Analysis; Methods; Mind; Modeling; Observational Study; Outcome; Outcome Assessment; Outcome Measure; Participant; Persons; Population; Population Intervention; Prevention; Prevention Research; Prevention trial; Randomized Clinical Trials; Randomized Controlled Trials; Registries; Research; Research Design; Research Personnel; Risk Factors; Role; Sample Size; Sampling; Source; Specific qualifier value; Specificity; Statistical Models; Structural Models; Structure; Testing; Time; Uncertainty; biobank; brain health; cardiovascular health; cardiovascular risk factor; cognitive change; cohort; cost effective; data integration; data registry; design; diabetes management; epidemiology study; flexibility; follow up assessment; follow-up; hypertension control; improved; member; models and simulation; novel; novel strategies; prevent; protective factors; public health priorities; randomized trial; response; simulation; tool; trial comparing; trial design; user-friendly; vascular risk factor

Closing the gap between observational research and randomized trials for prevention of Alzheimer's Disease and dementia MPI: Glymour and Power in response to PAR-17-054.html Summary Launching randomized controlled trials (RCTs) for Alzheimer’s disease (AD) prevention is an urgent public health priority. Although cardiovascular risk factor management is among the most promising intervention strategies, there is considerable uncertainty about the optimal eligibility criteria, intervention details, duration, or outcome assessments. Many major trials targeting AD prevention have been disappointing. One possible reason for these disappointments is that observational research has not provided enough information to anticipate whether a proposed RCT would succeed. Observational studies rarely specify populations, exposures, and duration of follow-up with enough detail to guide RCT development. Most observational studies do not have enough information to provide detailed guidance for RCT development. Integration across heterogeneous observational data sources is necessary to achieve the sample size, diversity, and variety of measurements necessary to guide RCT development. In other research areas, simulations have proven useful tools to combine diverse sources of evidence, but in AD prevention, we currently lack tools to systematically combine evidence from heterogeneous data sources in order to guide trial design. This proposal takes advantage of recent advances in causal methods for data integration to overcome the previous barriers and develop a simulation model leveraging all of the information from diverse data sources, including cohorts, clinical administrative data, and registry information. In AIM 1, we combine information from 8 observational studies, including cohorts, biobanks, and registries, into a unified, flexible, prevention simulation model. This model can simulate effects of hypothetical trials and thereby provide specific guidance for development of effective RCTs for AD prevention. We begin by estimating a structural model using data from the Cardiovascular Health Study (CHS, n=5,888) and the Atherosclerosis Risk in Communities (ARIC, n=15,792) cohorts, which include detailed exposure, outcome, and covariate measures. We will then incorporate data from 6 other sources, with information in total on 1.6 million individuals. We will use a latent variable approach to incorporate alternative measures of exposures, outcomes, and covariates. In AIM 2, the prevention simulation model will be tested, refined, and validated by comparing simulated and actual findings of the ACCORD-MIND, ACCORDION-MIND, SYST-EUR, HYVET-COG, SCOPE, SHEP, and SPRINT-MIND trials. AIM 3 will compare a range of hypothetical trials for diabetes and hypertension management to identify interventions most likely to succeed, considering eligibility criteria, intensity and duration of intervention, and outcome measures. In AIM 4 we develop user-friendly interfaces for the model, allowing incorporation of new evidence from additional data sets, potentially addressing new risk factors, new outcomes, and evaluation of alternative proposed trial designs. The prevention simulation engine will identify which AD prevention RCTs are likely to succeed and thereby accelerate progress towards successful strategies to prevent AD.

缩小观察性研究和随机试验之间的差距以预防阿尔茨海默病 病和痴呆症 MPI：响应PAR-17-054.html的GC 100和Power 总结 开展阿尔茨海默病（AD）预防的随机对照试验（RCTs）是公众的当务之急 健康优先。尽管心血管危险因素管理是最有希望的干预措施之一， 战略，有相当大的不确定性，最佳的资格标准，干预细节，持续时间，或 成果评估。许多针对AD预防的重大试验都令人失望。一个可能 令人失望的原因是，观察性研究没有提供足够的信息， 预测拟议的RCT是否会成功。观察性研究很少指定人群， 暴露和随访持续时间，并提供足够的详细信息以指导RCT开发。大多数观察性研究 没有足够的信息为RCT开发提供详细的指导。之间的集成 异构观测数据源是必要的，以实现样本大小，多样性和多样性， 指导RCT开发所需的测量。在其他研究领域，模拟已被证明是有用的 联合收割机结合不同证据来源的工具，但在AD预防方面，我们目前缺乏系统地 联合收割机从不同的数据源收集证据，以指导试验设计。这项提议需要 利用因果关系方法的最新进展进行数据整合，以克服以前的障碍， 开发一个模拟模型，利用来自不同数据源的所有信息，包括队列， 临床管理数据和注册信息。在AIM 1中，我们将来自8个观测数据的联合收割机信息 研究，包括队列，生物库和登记，到一个统一的，灵活的，预防模拟模型。这 模型可以模拟假设试验的效果，从而为开发 预防AD的有效RCT。我们开始通过使用来自 心血管健康研究（CHS，n= 5，888）和社区动脉粥样硬化风险（ARIC，n= 15，792） 队列，包括详细的暴露、结局和协变量指标。然后我们将数据 来自其他6个来源的信息，共涉及160万人。我们将使用潜变量方法 纳入暴露、结局和协变量的替代措施。在AIM 2中，预防 将通过比较模拟结果和实际结果，对模拟模型进行测试、改进和验证。 ACCORD-MIND、ACCORDION-MIND、HYVET-COG、SCOPE、SHEP和SPRINT-MIND试验。 AIM 3将比较一系列糖尿病和高血压管理的假设试验， 最有可能成功的干预措施，考虑到资格标准、干预措施的强度和持续时间，以及 结果测量。在AIM 4中，我们为模型开发了用户友好的界面，允许将新的 来自其他数据集的证据，可能涉及新的风险因素、新的结局和 替代拟议试验设计。预防模拟引擎将识别哪些AD预防RCT 这可能会取得成功，从而加快成功预防AD战略的进展。