I am a postdoctoral researcher at the Université de Montréal in the Pharmacy faculty, focusing on health economics. I completed my PhD in Economics at Northwestern University in 2023.
Here is a link to my academic CV.
My research centres around developing and applying novel statistical methods for clinical and health-economic problems.
My overarching goal is to provide healthcare decision-makers the tools to manage diverse patient populations by responsibly accounting for uncertainty and patient heterogeneity. In service of this goal, I have two main research programs. The first direction is develop partial identification methods to infer treatment responses from published trial data in the presence of uncertainty. The second direction is to develop decision-theoretic methods to equitably care for patient populations with diverse needs, particularly vulnerable populations.
In terms of scope, my research covers decision problems in a wide range of health settings, from medical inference, to pharmacoeconomic cost-benefit analysis, to clinical guideline and drug approval decisions.
Advanced prostate cancer has a substantial health impact that has been countered with novel treatments, particularly androgen receptor pathway inhibitors (ARPI). Contemporary research has found ARPIs to provide superior effectiveness in multiple health states, but analogous APRI cost-effectiveness research has only focused on a single health state (i.e. mCSPC) with mixed conclusions. Our contribution is to compare ARPI effectiveness and cost-effectiveness across advanced prostate cancer health states, not just within one health state. To achieve this comparison, our project has the novel feature of modeling and comparing treatment sequences, requiring novel methodological work enabling us to combine trial evidence in sequence.
Currently, the quality-adjusted life-year (QALY) is a leading measure of health used to compare different treatments in health economic analyses. Although the QALY is useful for analyzing treatments’ health-related quality-of-life (HRQoL) effects, it has faced intellectual and legislative scrutiny due to its potential to unfairly discriminate against disadvantaged patients with lower baseline HRQoL, such as patients with disabilities. However, the currently available alternative measures of LY, evLYG, and HYT all have serious issues, including potential to discriminate against disabled patients. To remedy these issues, I present a novel measure of health called the equitable quality-adjusted life-year (EQALY), which retains the QALY’s ability to compare HRQoL while valuing the health of disadvantaged patients equitably.
This paper theoretically examines how clinical guidelines should balance treatment effectiveness and provider expertise. A standard goal of evidence-based clinical guidelines is to inform clinicians and encourage them to use the most effective (or cost-effective) treatments for a group of patients. Despite well-documented learning-by-doing effects in clinical practice, there is limited research about how guidelines should account for their influence on clinician expertise. I explore this question theoretically with a model with a social planner choosing a guideline policy for a group of clinicians who learn by doing and treat a heterogeneous patient population. I show that even in an idealized setting with perfect knowledge and aligned cost preferences, clinical guidelines serve a role as skill management policies. I characterize the social planner's optimal policy as a function of both relative treatment effectiveness and clinicians' learning curves. A key finding is that in some settings, it is optimal for guidelines to recommend a treatment that is less effective on average but is well-practiced. This finding suggests that the prevailing approach of recommending the treatments with superior clinical trial results may harm patients outcomes.