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

缩小阿尔茨海默病预防的观察性研究和随机试验之间的差距 疾病与痴呆症 MPI：Gymour和Power对PAR-17-054.html的回应 摘要 开展预防阿尔茨海默病(AD)的随机对照试验(RCT)迫在眉睫 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，在最佳资格标准、干预细节、持续时间或 结果评估。许多旨在预防阿尔茨海默病的重大试验一直令人失望。一种可能 这些令人失望的原因是观察性研究没有提供足够的信息来 预测拟议的随机对照试验是否会成功。观察性研究很少指定人群， 暴露和持续时间，并提供足够的细节来指导随机对照试验的发展。大多数观察性研究 没有足够的信息来为RCT的发展提供详细的指导。跨区域集成 要实现样本大小、多样性和多样性，必须使用不同的观测数据源 指导随机对照试验发展所需的措施。在其他研究领域，模拟已被证明是有用的 结合不同证据来源的工具，但在AD预防方面，我们目前缺乏系统性的工具 结合来自不同数据来源的证据，以指导试验设计。这项提议需要 利用数据整合的因果方法的最新进展，克服以前的障碍和 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。在目标1中，我们结合了来自8个观测数据的信息 将包括队列、生物库和登记在内的研究纳入统一、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。我们首先使用来自 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、结果和协变量测量。然后，我们将合并数据 来自6个其他来源，总共有160万人的信息。我们将使用潜变量方法 纳入暴露、结果和协变量的替代衡量标准。在AIM 2中，预防 将通过将模拟结果与实际结果进行比较来测试、改进和验证模拟模型 合意、手风琴-意念、SYST-EUR、HYVET-COG、SCOPE、SHEP和Sprint-Mind试验。 目标3将比较一系列糖尿病和高血压治疗的假设性试验，以确定 最有可能成功的干预措施，考虑到资格标准、干预强度和持续时间，以及 结果衡量标准。在AIM 4中，我们为模型开发了用户友好的界面，允许合并新的 来自其他数据集的证据，可能涉及新的风险因素、新的结果和对 备选的拟议试验设计。预防模拟引擎将识别哪些是AD预防RCT 有可能取得成功，从而加快制定成功的预防AD战略的进展。

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.