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

在 2 型糖尿病和心力衰竭的新治疗时代优化老年人护理：通过新方法和证据三角测量加强因果推理

基本信息

批准号：
10673040
负责人：
Xiaojuan Li
金额：
$ 12.46万
依托单位：
HARVARD PILGRIM HEALTH CARE, INC.
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-01 至 2027-04-30
项目状态：
未结题

项目摘要

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.

项目摘要/摘要这项申请K01指导的研究科学家奖由Xiaojuan Li，博士提交 PA-20-190。李博士是人口医学系的药物ePIDEMIOSTOSS和教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独立的研究职业为患者的适当和最佳用途做出贡献有复杂的医疗保健需求。 Li博士在药物ePidemiologic方法和催化剂方面具有背景推理。这种指导的研究和培训经验将使她的方法论研究技能纳入临床老年研究。在部门高产和支持性的研究环境中在人口医学上，李博士将与一支由高度致力和协作的跨学科团队合作在她拟议的培训的特定领域中具有深厚专业知识和丰富经验的导师：临床老年病，糖尿病学，脆弱，半参数方法和机器学习。总体该K01应用的目的是了解更新的长期比较效力和安全性在申请，发展和传播最终状态的同时，老年人的抗血糖药物在常规护理中 ART分析和因果推理方法，最终优化了老年人的临床护理决策患有糖尿病和心力衰竭。尽管这些较新的抗血糖剂报告了心血管在安慰剂控制的，随机对照试验（RCT）中的好处，关于如何在对于经常被排除或代表性不足的老年患者的药物选择范围扩大。这些试验也不提供正面比较。该建议旨在填补通过在高维电子保健数据库中使用丰富信息的证据基础试验模拟框架以及新的因果推断和统计工具。 AIM 1将完善试验仿真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框架这些方法通过将每个RCT的效果估计与其检查仿真的效果估计进行比较。效果估计之间的一致程度衡量仿真框架的有效性和分析方法，并将指导我们对其他目标试验的观察仿真的信心，以评估具有不同资格标准的新代理人的比较安全性和有效性治疗比较以及实际RCT不可用或不可行的结果（目标2和3）。调查结果将改善可用于治疗老年患者的临床医生的决策基础，促进有效且安全的药物治疗，并最终改善对老年患者的护理，这与国家老化任务和倡议研究所。完成拟议的职业发展和指导的研究将使李博士成功竞争未来的R01资金，并获得大量对老年药物学研究的贡献，并改善老年人的生活。