Characterizing Population Differences between Clinical Trial and Real World Populations

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，进行一项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、获得足够的受试者规模，还是管理试验的一个部分是不道德的。为了了解治疗方法、政策变化和其他不可能进行临床试验的干预措施的影响，研究人员开发了试图在观察性数据中模拟临床试验的方法。尽管采用了复杂的统计方法，但尚不清楚是否有可能可靠地模拟观测数据中的随机对照。我们的目标是量化这些不同结果的一个潜在驱动因素，即临床试验和现实世界人群之间的差异。在目标1中，我们将具有个人水平数据的试验与三个真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总体试验统计中推断最可能的个人统计的方法。最后，在Aim 3中，我们比较了没有发布个人水平数据和真实世界数据的神经学试验。然后，我们估计治疗估计在不同人群中的可移植性，包括：符合RWD试验条件的人群和不符合试验条件但接受RWD治疗的人群。这允许对适应症漂移和治疗异质性进行研究。通过揭示这些群体之间的差异，我们可能能够确定在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奖将使我获得在使用监管科学（与导师Florence Bourgeois博士和顾问Deborah Schrag博士一起）进行生物发现（与导师Tianxi Cai博士和Isaac Kohane博士一起）和神经学（与导师Page Pennell博士和Clemens Scherzer博士一起）方面的专业知识。我在统计学、信息学、遗传学和临床数据源机器学习方面的背景使我成为拟议项目的理想人选。拟议的培训计划、指导和项目将为成功过渡到独立研究提供坚实的基础。

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.