Applying a Targeted Machine Learning and Causal Inference Approach to Analyzing Long-Term Sequelae of COVID-19 Infection Through the National COVID Cohort Collaborative. - Candidate: I am an epidemiologist in the Division of Biostatistics at the University of California, Berkeley School of Public Health, and I completed my Ph.D. in Epidemiology in August 2022 at UC Berkeley. Since my graduation, I have worked with the Center for Targeted Machine Learning and Causal Inference to apply cutting-edge biostatistical and causal inference methods to pressing research questions using electronic health record (EHR) data from the National Clinical Cohort Collaborative (N3C). I aim to become a leader in applying innovative biostatistical, causal inference, and machine learning methods to address impactful research questions in infectious disease epidemiology using electronic health record data. Environment: To achieve my career goals, my training and mentorship plan will focus on recent advances in biostatistics, causal inference, and data science methods, as well as infectious disease epidemiology. I have assembled an interdisciplinary team of expert biostatisticians, epidemiologists, and clinicians who will support my training. Alan Hubbard (primary mentor) and Mark van der Laan (co-mentor) will provide expert guidance and mentorship on biostatistics, data science, and causal inference. Rena Patel (co-mentor) and Jack Colford (scientific advisor) will provide mentorship and guidance in infectious disease epidemiology. Research: Researchers and clinicians have made significant progress in understanding, preventing, and treating the acute stages of viral infections. However, there is considerable uncertainty regarding interventions to prevent or treat the long-term sequelae of viral infections, which are a growing source of morbidity and mortality. In this project, I will evaluate the effectiveness of several promising medications and interventions in preventing long-term sequelae and mortality following viral infection. In Aim 1, I will evaluate the impact of diabetes medications (metformin and GLP-1 receptor agonists) on mortality and long-term symptomatology among patients with comorbid diabetes and viral infection. In Aim 2, I will assess the impact of interleukin-6 modulating drugs (tocilizumab and sarilumab) on mortality and long-term sequelae in a cohort of patients with comorbid moderate rheumatoid arthritis and viral infection. In Aim 3, I will develop, evaluate, and disseminate advanced methods for applying causal inference, machine learning, and biostatistics to EHR data. In Aim 4, I will evaluate the relationship between vaccination timing, relative to acute infection, and long-term sequelae of infection, in order to determine an optimized vaccination schedule. I will apply targeted machine learning methods to achieve these aims, which will prepare me for an R01-level application to utilize these methods in research questions related to infectious disease epidemiology and analysis of EHR data.
