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

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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.