Characterizing Population Differences between Clinical Trial and Real World Populations

描述临床试验和真实世界人群之间的人群差异

• 批准号: 10703711
• 负责人: Brett K Beaulieu-Jones
• 金额: $ 24.9万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-15 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10703711
• 关键词: Affect; Age; Age of Onset; Area; Automobile Driving; Award; Biological; Caring; Clinical; Clinical Data; Clinical Trials; Complex; Computerized Medical Record; Data; Data Set; Data Sources; Development; Dimensions; Disease; Drug Approval; Effectiveness; Eligibility Determination; Ensure; Epilepsy; Equity; Estrogens; Ethnic Origin; Exclusion; Exhibits; Foundations; Future; Gait; Genes; Genetic; Geography; Goals; Guidelines; Healthcare promotion; Heterogeneity; Hormonal; Hospitals; Individual; Informatics; Insurance; Intervention; Machine Learning; Measures; Menarche; Menopause; Mentors; Methodology; Methods; Modernization; Mutation; Neurologic; Neurology; Parkinson Disease; Participant; Patients; Performance; Perimenopause; Perinatal; Pharmaceutical Preparations; Physiology; Placebos; Policies; Polypharmacy; Population; Positioning Attribute; Pregnancy; Price; Procedures; Randomized; Randomized, Controlled Trials; Rare Diseases; Research; Research Personnel; Safety; Science; Seizures; Socioeconomic Status; Subgroup; Symptoms; Target Populations; Testing; Therapeutic Effect; Training; Treatment Efficacy; Tremor; Underrepresented Populations; Validation; Woman; arm; birth control; cost; disease diagnosis; disorder subtype; disparity reduction; effectiveness analysis; efficacy clinical trial; glucosylceramidase; health care disparity; improved; inclusion criteria; interest; member; multiple data sources; oncology trial; patient population; reproductive; sex; socioeconomic diversity; standard measure; standard of care; statistics; success; therapeutic development; treatment effect; trial comparing; unethical

Randomized controlled trials are the gold standard for measuring the effect of a treatment or intervention. Unfortunately, it is not feasible to conduct a randomized controlled trial to test all research questions, whether due to cost, achieving sufficient subject sizes or when administering an arm of the trial would be unethical. To understand the effects of therapeutics, policy changes, and other interventions where it is not possible to administer a clinical trial, researchers have developed approaches that attempt to simulate clinical trials in observational data. Despite sophisticated statistical methodologies, it is not clear whether it is possible to reliably simulate a randomized controlled in observational data. We aim to quantify one potential driver of these different results, differences between the clinical trial and real-world populations. In Aim 1, we compare trials that have individual level data available to three real- world data sources. In Aim 2, we develop methodologies to infer most likely individual-level statistics from aggregate trial statistics using real world data. Finally, in Aim 3 we compare neurological trials that do not release individual level data to real world data. We then estimate the transportability of treatment estimates across different populations including: the population eligible for the trial in RWD and the population ineligible for the trial but receiving the treatment in RWD. This allows for the study of indication drift and treatment heterogeneity. By uncovering differences between these groups, we may be able to identify groups that are underrepresented in clinical trials to help reduce healthcare disparities. The K99/R00 award will allow me to gain expertise in using regulatory sciences (with mentor Dr. Florence Bourgeois and advisor Dr. Deborah Schrag) for biologic discovery (with mentors Dr. Tianxi Cai and Dr. Isaac Kohane) within neurology (with mentors Dr. Page Pennell and Dr. Clemens Scherzer). My background in statistics, informatics, genetics, and machine learning with clinical data sources ideally positions me for the proposed project. The proposed training plan, mentoring and project will provide a strong foundation for a successful transition to independent research.

随机对照试验是衡量治疗或干预效果的黄金标准。不幸的是，进行随机对照试验来测试所有研究问题是不可行的，无论是由于成本、达到足够的受试者规模还是因为进行试验的一个分支是不道德的。为了了解治疗方法、政策变化和其他无法进行临床试验的干预措施的效果，研究人员开发了尝试在观察数据中模拟临床试验的方法。尽管统计方法很复杂，但尚不清楚是否可以可靠地模拟观察数据中的随机对照。我们的目标是量化这些不同结果的一个潜在驱动因素，即临床试验与现实世界人群之间的差异。在目标 1 中，我们将具有可用个体水平数据的试验与三个现实世界数据源进行比较。在目标 2 中，我们开发了使用真实世界数据从汇总试验统计数据中推断出最可能的个人层面统计数据的方法。最后，在目标 3 中，我们将不发布个体水平数据的神经学试验与现实世界数据进行比较。然后，我们估计不同人群中治疗估计的可移植性，包括：符合 RWD 试验资格的人群和不符合试验资格但接受 RWD 治疗的人群。这允许研究适应症漂移和治疗异质性。通过揭示这些群体之间的差异，我们也许能够识别出临床试验中代表性不足的群体，以帮助减少医疗保健差异。 K99/R00 奖项将使我能够获得利用监管科学（与导师 Florence Bourgeois 博士和顾问 Dr. Deborah Schrag 博士）在神经学领域（与导师 Tianxi Cai 博士和 Isaac Kohane 博士）进行生物发现的专业知识（与导师一起）佩奇·彭内尔博士和克莱门斯·谢尔泽博士）。我在统计学、信息学、遗传学和机器学习以及临床数据源方面的背景使我能够胜任所提议的项目。拟议的培训计划、指导和项目将为成功过渡到独立研究奠定坚实的基础。