Optimizing Alzheimer's Disease Clinical Trial Generalizability

摘要 临床试验通常在理想化和严格控制的条件下进行，以确保内部 有效性，但这些条件，矛盾的是，妥协试验外部有效性（即，目标概括性 人口）。长期以来，试验普遍性低一直是一个问题，并在不同的临床试验中广泛记录。 地区例如，阿尔茨海默病（AD）临床试验的参与者系统地比AD年轻 一般人群中的患者。过于严格的资格标准可以说是最大的，但可以修改的 造成低普及性的障碍。FDA发起了许多倡议，主要是通过 扩大合格标准，促进入组实践，使试验参与者能够更好地反映 如果获得批准，最有可能使用该治疗的人群。尽管如此，试验申办者和研究者 由于担心严重不良反应风险的潜在增加， 严重不良事件（SAE）及其对研究药物安全性和有效性的负面影响。作为 结果，许多老年患者被排除在AD试验之外，无论是通过明确的年龄限制，还是隐含的 通过排除老年人更常见的临床特征。在需要与需要之间存在差距， 拓宽审判标准和方式，以满足实践需要。以前的研究，包括我们的研究， 验证并使用研究特征的概化指数（GIST），最好的定量，资格- 驱动的，先验的普遍性措施，在一些疾病领域。GIST评分可用于 指导对标准的调整，以提高人口的代表性。然而，存在一些关键障碍 在实践中，特别是在AD试验中采用该方法的原因：（1）缺乏标准化的、可计算的合格标准 (CEC)将标准转化为数据查询的框架-这是确定 概括性评估，以及（2）需要映射资格标准之间的数学关系 和GIST以及患者结局（即SAE），这回答了如何扩大标准的关键问题 将同时影响AD试验的推广性和患者结局。为了消除这些障碍，我们 建议系统分析ClinicalTrials.gov上现有的AD试验，以创建标准化的CEC库 并开发统计模型，说明如何调整资格标准，特别是年龄， 影响（1）GIST测量的试验普遍性，和（2）结果（即，严重不良事件）， 使用来自OneFlorida网络的真实世界数据（RWD）进行近似。OneFlorida包含链接 电子健康记录（EHR），索赔和癌症登记数据约1500万佛罗里达州。本研究将 提供必要的数据，以支持未来开发试验合格性标准设计工具， 优化试验的普遍性，同时平衡目标人群中SAE风险的潜在增加。

ABSTRACT Clinical trials are often conducted under idealized and rigorously controlled conditions to ensure internal validity, but such conditions, paradoxically, compromise trials external validity (i.e., generalizability to the target population). Low trial generalizability has long been a concern and widely documented across different clinical areas. For instance, participants of Alzheimer's disease (AD) clinical trials are systematically younger than AD patients in the general population. Overly restrictive eligibility criteria are arguably the biggest yet modifiable barriers causing low generalizability. The FDA has launched numerous initiatives, primarily through broadening eligibility criteria, to promote enrollment practices so that trial participants can better reflect the population who would most likely use the treatment if approved. Nevertheless, trial sponsors and investigators are reluctant to broaden eligibility criteria due to concerns over potential increases in risk of serious adverse events (SAEs) and its negative impact on the investigational drug’s safety and effectiveness profile. As a result, many elderly patients are excluded from AD trials either explicitly through an age restriction or implicitly through excluding clinical characteristics more prevalent in the elderly. There is a gap between the need to broaden trial criteria and ways available to fulfill the need in practice. Previous studies, including ours, have validated and used the Generalizability Index of Study Traits (GIST), the best available quantitative, eligibility- driven, a priori generalizability measure, in a number of disease domains. GIST scores can potentially be used to guide adjustments to criteria towards better population representativeness. However, there are key barriers for its adoption in practice, especially in AD trials: (1) the lack of a standardized, computable eligibility criteria (CEC) framework to translate criteria to data queries – a necessary step to define the populations for generalizability assessment, and (2) the need to map the mathematical relationships between eligibility criteria and GIST as well as patient outcomes (i.e. SAE), which answers the critical question how broadened criteria will affect AD trial’s generalizability and patient outcomes simultaneously. To remove these barriers, we propose to systematically analyze existing AD trials in ClinicalTrials.gov to create a standardized library of CEC for AD trials and develop statistical models on how adjustments to eligibility criteria, especially age, would affect (1) trial generalizability measured by GIST, and (2) outcomes (i.e., SAEs) of the target population, approximated using real-world data (RWD) from the OneFlorida network. OneFlorida contains linked electronic health record (EHR), claims, and cancer registries data for ~15 million Floridians. This study will provide the necessary data to support future development of a trial eligibility criteria design tool that can optimize trial generalizability while balancing potential increases in risk of SAEs in the target population.