Optimizing the Population Representativeness of Older Adults in Cancer 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）。低普遍性长期以来一直是一个问题，并广泛记录，特别是在癌症 研究社区。某些人口亚群，如老年人，在妇女中的代表性往往不足。 癌症研究由于[[过度限制性（和潜在的不合理）的排除标准，]]这是有争议的， 最大但可修改的障碍，导致普遍性低。监管机构（例如，供资机构 (e.g., NCI）和研究团体（例如，”他说，“他们的要求和指导，使他们能够更好地发挥作用。 资格标准，以促进入组实践，使试验能够更好地反映最有可能 如果批准，使用该药物。尽管如此，试验申办者和研究者不愿意扩大资格 由于担心严重不良事件（SAE）及其 对研究药物的安全性和有效性产生负面影响。因此，在癌症中， 在临床试验中，老年人通常通过排除临床特征而被隐含地排除在外，这些临床特征在 老人在扩大审判标准的必要性与[[确定] 不合理、限制性过强的排除标准，然后在实践中相应调整]]。 以前的研究，包括我们的研究，已经验证和使用了学习特征的概化指数（GIST）， 在一些疾病中， 域. GIST评分可用于指导对标准进行调整， 代表性。然而，在实践中采用它存在关键障碍，特别是在癌症试验中：（1） 缺乏一个标准化的、可计算的资格标准（CEC）框架来将标准转化为数据查询- 一个必要的步骤，以确定人口的普遍性评估，（2）缺乏验证研究 评估GIST在癌症试验中的可靠性和有效性，以及（3）绘制数学模型的必要性。 合格性标准与GIST以及患者结局（即SAE）之间的关系，这回答了 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，我们将系统地分析现有的女性乳腺、肺和结肠直肠试验 在clinicaltrails.gov中创建一个本体驱动的标准化CEC库，验证GIST在癌症中的作用 试验，并开发[[统计模型]，说明如何调整资格标准，特别是那些限制 老年人的参与]]，将影响（1）GIST测量的试验普遍性，和（2）结局（即， 使用大量真实世界数据（RWD）源进行近似计算- OneFlorida网络，包含约1500万佛罗里达人的相关EHR，索赔和癌症登记数据。

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 generalizability has long been a concern and widely documented, especially, in cancer research community. Certain population subgroups, such as older adults, are often underrepresented in cancer studies due to [[overly restrictive (and potentially unjustified) exclusion criteria,]] which are arguably the biggest yet modifiable barriers causing low generalizability. Regulatory agencies (e.g., FDA), funding agencies (e.g., NCI), and research communities (e.g., ASCO) have called and provided guidance to broaden trial eligibility criteria to promote enrollment practices so that trials can better reflecting the population most likely to use the drug if approved. Nevertheless, trial sponsors and investigators are reluctant to broaden eligibility criteria due to concerns over potential increases in the risk of serious adverse events (SAEs) and their negative impact on the investigational drug’s safety and effectiveness profile. As a consequence, in cancer trials, elderlies are often excluded implicitly through excluding clinical characteristics that are more prevalent in the elderly. There is a gap between the need to broaden trial criteria and ways available to [[identify unjustified, overly restrictive exclusion criteria and then adjust them accordingly 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 cancer 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 cancer 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 will systematically analyze existing female breast, lung, and colorectal trials in clinicaltrails.gov to create an ontology-driven, standardized library of CEC, validate GIST among cancer trials, and develop [[statistical models on how adjustments to eligibility criteria, especially those that limit the participation of older adults]], would affect (1) trial generalizability measured by GIST, and (2) outcomes (i.e., SAEs) of the target population, approximated using a large collection of real-world data (RWD) source – the OneFlorida network, that contains linked EHRs, claims, and cancer registry data for ~15 million Floridians.