Optimizing care for older adults in the new treatment era for type 2 diabetes and heart failure: Strengthening causal inference through novel approaches and evidence triangulation

项目总结/摘要 K 01指导研究科学家奖的申请由Xiaojuan Li博士提交，以回应 PA-20-190。李博士是一名药物流行病学家，也是美国国家卫生研究院人口医学系的讲师。 哈佛医学院和哈佛朝圣者医疗保健研究所。她的长期目标是发展一个 独立的研究生涯，有助于在患者中适当和最佳地使用药物治疗 复杂的医疗需求。李博士具有药物流行病学方法和因果关系的背景 推论这种指导性的研究和培训经验将她的方法研究技能整合到 临床老年医学研究。在该部富有成效和支持性的研究环境中， 人口医学，李博士将与一个高度敬业和协作的跨学科团队合作， 在其拟议培训的具体领域具有深厚专业知识和丰富经验的导师： 临床老年病学、糖尿病学、虚弱、半参数方法和机器学习。总体 本K 01申请的目的是了解新药物的长期比较有效性和安全性 常规护理中老年人的降糖药，同时应用、开发和传播 最先进的分析和因果推理方法，最终优化老年人的临床护理决策 患有糖尿病和心力衰竭虽然这些新的抗高血糖药已报告心血管疾病 在安慰剂对照、随机对照试验（RCT）中， 为那些经常被排除在外或代表性不足的老年患者提供更广泛的药物选择。 这些试验也没有提供头对头的比较。这项建议旨在填补 证据库利用高维电子医疗数据库中丰富的信息， 试验仿真框架，以及新颖的因果推理和统计工具。目标1将完善试验模拟 通过使用现代因果和统计方法和基准模拟两个已发表的RCT框架 这些方法通过比较从每个RCT的效果估计与那些从他们的观察仿真。 效果估计之间的一致程度衡量了仿真框架的有效性， 分析方法，并将指导我们对其他目标试验的观察模拟的信心，以评估 具有不同资格标准的新型药物的安全性和有效性比较，头对头 治疗比较，以及实际RCT不可用或不可行的结果（目标2和3）。的 研究结果将改善临床医生治疗老年患者的决策依据， 促进有效和安全的药物治疗，并最终改善老年患者的护理， 国家老龄化研究所的使命和倡议。完成拟议的职业发展和 指导的研究将使李博士成功地竞争未来的R 01资金，并取得重大进展。 为老年药物流行病学研究做出贡献，并改善老年人的生活。

PROJECT SUMMARY/ABSTRACT This application for a K01 Mentored Research Scientist Award is submitted by Xiaojuan Li, PhD in response to PA-20-190. Dr. Li is a pharmacoepidemiologist and Instructor in the Department of Population Medicine at Harvard Medical School and Harvard Pilgrim Health Care Institute. Her long-term goal is to develop an independent research career contributing to the appropriate and optimal use of medical treatments in patients with complex healthcare needs. Dr. Li has a background in pharmacoepidemiologic methods and causal inference. This mentored research and training experience will integrate her methodological research skills into clinical geriatric research. Within the highly productive and supportive research environment at the Department of Population Medicine, Dr. Li will work with an interdisciplinary team of highly committed and collaborative mentors that have deep expertise and extensive experience in the specific areas of her proposed training: clinical geriatrics, diabetology, frailty, semiparametric methods, and machine learning. The overarching objective of this K01 application is to understand the long-term comparative effectiveness and safety of newer antihyperglycemic agents in older adults in routine care while applying, developing, and disseminating state-of- the-art analytical and causal inference methods, ultimately optimizing clinical care decisions for older adults with diabetes and heart failure. While these newer antihyperglycemic agents have reported cardiovascular benefit in placebo-controlled, randomized controlled trials (RCTs), little is known about how to choose among an expanded range of medication choices for older patients who are often excluded or underrepresented. These trials do not provide head-to-head comparisons either. This proposal seeks to fill the critical gaps in the evidence base by utilizing the rich information in high-dimensional electronic healthcare databases, the target trial emulation framework, and novel causal inference and statistical tools. Aim 1 will refine the trial emulation framework by emulating two published RCTs using modern causal and statistical approaches and benchmark these methods by comparing effect estimates from each RCT with those from their observational emulation. The extent of agreement between the effect estimates measures the validity of the emulation framework and analytical methods and will guide our confidence in the observational emulation of other target trials to assess comparative safety and effectiveness of the newer agents with different eligibility criteria, head-to-head treatment comparisons, and outcomes for which actual RCTs are not available or infeasible (Aims 2 & 3). The findings will improve the evidence base for decision-making available for clinicians treating older patients, promote effective and safe drug therapy, and ultimately improve the care of older patients, which aligns with the National Institute on Aging’s missions and initiatives. Completion of the proposed career development and mentored research will position Dr. Li to successfully compete for future R01 funding and make significant contributions to geriatric pharmacoepidemiology research and improve the lives of older adults.