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，但矛盾的是，这样的条件考验外部有效性(即对目标的概括性 人口)。长期以来，泛化能力低一直是一个令人担忧的问题，并被广泛记录在案，尤其是在癌症方面。 研究社区。某些人口亚群，如老年人，在 癌症研究由于[[过度严格的(和潜在的不合理的)排除标准，]]，这可以说是 最大但可修改的障碍导致通用性较低。监管机构(例如，FDA)、资助机构 (如NCI)和研究界(如ASCO)呼吁并提供指导以扩大试验范围 推广登记做法的资格标准，以便试验可以更好地反映最有可能 如果获得批准，请使用该药物。然而，试验赞助商和调查人员不愿扩大资格。 由于担心严重不良事件(SAE)风险的潜在增加而制定的标准 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 试验中，老年人通常通过排除更普遍的临床特征而被隐含排除在 老年人。扩大试验标准的需要与可用方法之间存在差距[[确定 不合理、过于严格的排除标准，然后在实践中对其进行相应调整]]。 以前的研究，包括我们的研究，已经验证和使用了研究特征的概化指数(GIST)， 在许多疾病中，最佳可用的量化的、资格驱动的、先验的概化衡量标准 域名。GIST评分可以潜在地用于指导对标准的调整，以实现更好的人口 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)框架来将标准转换为数据查询- 确定总体以进行概化评估的必要步骤，(2)缺乏验证研究 这评估了GIST在癌症试验中的可靠性和有效性，以及(3)需要绘制数学地图 资格标准与GIST以及患者预后(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适性和患者结果。 为了消除这些障碍，我们将系统地分析现有的女性乳房、肺和结直肠试验。 在Clinicaltrails.gov中创建一个本体驱动的标准化CEC文库，在癌症中验证GIST 试验，并开发[[关于如何调整资格标准的统计模型，特别是那些限制 老年人的参与]]将影响(1)GIST衡量的试验概括性，以及(2)结果(即， 目标人群的SAE)，使用大量真实世界数据(RWD)来源-- One佛罗里达网络，包含约1500万佛罗里达州人的关联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.