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

• 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
• 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
• 来源: https://reporter.nih.gov/project-details/10673040
• 关键词: Accounting; Address; Adult; Agonist; Agreement; Area; Benchmarking; Cardiac; Cardiovascular Diseases; Cardiovascular system; Caring; Cause of Death; Cessation of life; Characteristics; Clinical; Clinical Data; Clinical Research; Complex; Data; Data Set; Decision Making; Diabetes Mellitus; Doctor of Philosophy; EFRAC; Effectiveness; Elderly; Eligibility Determination; Environment; Event; Exclusion; Feedback; Fracture; Funding; Future; GLP-I receptor; Geriatrics; Glucose; Goals; Head; Health Care Costs; Healthcare; Heart failure; Heterogeneity; Hospitalization; Hypoglycemia; Hypoglycemic Agents; Individual; Laboratories; Link; Long-Term Effects; Lower Extremity; Machine Learning; Measures; Medical; Medicine; Mentors; Methods; Mission; Modernization; National Institute on Aging; New Agents; Non-Insulin-Dependent Diabetes Mellitus; Observational Study; Older Population; Outcome; Patients; Pharmaceutical Preparations; Pharmacoepidemiology; Pharmacotherapy; Placebo Control; Placebos; Population; Positioning Attribute; Productivity; Publishing; Randomized, Controlled Trials; Reporting; Research; Research Methodology; Research Scientist Award; Risk; Safety; Sample Size; Sodium; Test Result; Time; Training; Urinary tract infection; Vascularization; Work; analytical method; career; career development; clinical care; clinical practice; comparative effectiveness; comparative safety; design; effectiveness outcome; electronic health database; evidence base; experience; follow-up; frailty; head-to-head comparison; high dimensionality; human old age (65+); improved; inhibitor; instructor; limb amputation; medical schools; mortality; novel; novel strategies; older patient; personalized medicine; preservation; prevent; response; routine care; safety outcomes; semiparametric; skills; symporter; tool; treatment comparison; treatment effect; treatment strategy

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.

项目概要/摘要 本 K01 指导研究科学家奖申请由李晓娟博士提交，以回应 PA-20-190 李博士是人口医学系的药物流行病学家和讲师。 哈佛医学院和哈佛朝圣者医疗保健研究所的长期目标是建立一个 独立的研究生涯有助于对患者进行适当的最佳治疗和使用 李博士具有药物流行病学方法和因果关系的背景。 这种指导性的研究和培训经验将她的方法论研究技能融入其中。 在该部门高产和支持性的研究环境中。 李博士将与一支高度敬业和协作的跨学科团队合作 在她提议的培训的特定领域拥有深厚专业知识和经验的导师： 临床老年病学、糖尿病、虚弱、半参数方法和机器学习。 此 K01 应用程序的目的是了解较新药物的长期比较有效性和安全性 在日常护理中老年人中使用抗高血糖药物，同时应用、开发和传播现状 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 虽然这些新的降糖药已报道了心血管疾病。 安慰剂对照、随机对照试验 (RCT) 的益处，但对于如何选择却知之甚少 为经常被排除在外或代表性不足的老年患者提供更多的药物选择。 这些试验也不提供直接比较。该提案旨在填补该研究中的关键空白。 利用高维电子医疗数据库中丰富的信息建立证据基础，目标 试验模拟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随机对照试验来构建框架 这些方法通过将每个随机对照试验的效果估计与其观察模拟的效果估计进行比较。 效果估计之间的一致程度衡量了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模拟进行评估的信心 具有不同资格标准的新药物的安全性和有效性的比较 治疗比较以及实际随机对照试验不可用或不可行的结果（目标 2 和 3）。 研究结果将改善治疗老年患者决策的证据基础， 促进有效和安全的药物治疗，并最终改善老年患者的护理，这与 国家老龄化研究所的使命和举措的完成和建议的职业发展。 指导研究将使李博士能够成功竞争未来的 R01 资金并取得重大进展 对老年药物流行病学研究和改善老年人的生活做出贡献。

项目成果

Labor Unionization Among Physicians in Training.

接受培训的医生加入工会。

• 发表时间: 2023-11-21
• 作者: Ahmed, Ahmed;Li, Xiaojuan
• 通讯作者: Li, Xiaojuan

Prognostic Factors of COVID-19: An Umbrella Review Endorsed by the International Society for Pharmacoepidemiology.

COVID-19 的预后因素：国际药物流行病学协会认可的总体审查。

• 发表时间: 2023-09
• 影响因子: 6.7
• 作者: Sarri, Grammati;Liu, Wei;Zabotka, Luke;Freitag, Andreas;Claire, Ravinder;Wangge, Grace;Elvidge, Jamie;Dawoud, Dalia;Bennett, Dimitri;Wen, Xuerong;Li, Xiaojuan;Rentsch, Christopher T;Uddin, Md Jamal;Ali, M Sanni;Gokhale, Mugdha;Déruaz
• 通讯作者: Déruaz