I am currently a research fellow in Epidemiology at the Harvard T.H. Chan School of Public Health. I work on causal inference for comparative effectiveness and real-world evidence in the HSPH Program on Causal Inference. My research is on causal inference methodology for improving evidence-based decision-making by patients, clinicians, and policy makers. I use novel statistical methods to answer comparative effectiveness questions for complex and time-varying treatments using observational data and randomized trials when available, and individual-level simulation modeling when insufficient data exist in the time frame required for decision-making. I am currently applying these methods to a variety of medical conditions including HIV progression, cancer, psychiatric conditions, and cardiovascular disease. I have an ScD in Epidemiology and MSc in Biostatistics from Harvard, an MPH in Epidemiology from Columbia Mailman School of Public Health, and a BSc in Biology from McGill University.
- Patients and investigators preferred measures of absolute risk in subgroups for pragmatic randomized trials
- Improved adherence adjustment in the Coronary Drug Project
- Using observational data to calibrate simulation models
- A comparison of agent-based models and the parametric g-formula for causal inference