Optimizing the Population Representativeness of Older Adults in Alzheimer's Disease and Related Dementia Clinical Trials

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，阿尔茨海默病和相关痴呆症（ADRD）临床试验的参与者是 系统性地比一般人群中的ADRD患者更年轻。过于严格的资格标准是 可以说是最大但可修改的障碍，导致普遍性低。FDA推出了多项 倡议，主要是通过扩大资格标准，以促进招募做法，使试验 参与者可以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不愿意扩大资格标准， 严重不良事件（SAE）风险的潜在增加及其对研究的负面影响 药物的安全性和有效性。因此，许多老年患者被排除在ADRD试验之外， 明确地通过年龄限制或隐含地通过排除在 老人在扩大试验标准的需要与满足以下需要的现有方法之间存在差距： 实践以往的研究，包括我们的研究，已经验证和使用的概化指数的学习特质 （GIST），最好的可用定量，杀伤力驱动，先验的普遍性措施，在一些 疾病领域。GIST评分可能用于指导对标准的调整， 人口代表性。然而，在实践中，特别是在ADRD中， 试验：（1）缺乏一个标准化的、可计算的合格标准（CEC）框架来将标准转化为数据 查询-为普遍性评估确定总体的必要步骤，（2）缺乏验证 评估GIST在ADRD试验中的可靠性和有效性的研究，以及（3）绘制数学模型的必要性。 资格标准与GIST以及患者结局（即SAE）之间的关系，回答了 关键问题是，扩大标准将如何同时影响试验的普遍性和患者结局。 为了消除这些障碍，我们建议系统地分析clinicaltrails.gov上现有的ADRD试验， 为ADRD试验创建一个本体驱动的标准化CEC库，在ADRD试验中验证GIST， 开发统计模型，说明资格标准（尤其是年龄）的调整将如何影响（1）试验 由GIST测量的概括性，和（2）结果（即，SAE），使用 真实世界的电子健康记录（EHR）数据。我们将回答一个关键的研究问题：什么和如何 排除标准超出明确的年龄标准限制了老年人参与ADRD试验。

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 and related dementias (ADRD) clinical trials are systematically younger than ADRD 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 ADRD 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 ADRD 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, (2) the lack of a validation study that assesses GIST’s reliability and validity in ADRD trials, and (3) 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 trial’s generalizability and patient outcomes simultaneously. To remove these barriers, we propose to systematically analyze existing ADRD trials in clinicaltrails.gov to create an ontology-driven, standardized library of CEC for ADRD trials, validate GIST among ADRD 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 electronic health record (EHR) data. We will answer a key research question: what and how exclusion criteria beyond the explicit age criterion limit older adults’ participation in ADRD trials.