The D3Mod lab’s research informs health policy and clinical decisions through data-driven modeling and analysis. We use methods from decision science, optimization, epidemiology, health economics, and machine learning to enable the efficient, effective, and equitable utilization of resources. We collaborate with stakeholders in medicine and health policy to maximize our impact on policy and practice while extending the state of the art in data-driven decision modeling.

A major area of focus is data-driven decision analytic modeling, which integrates individual-level data into models that compare health intervention or policy options. Traditionally, decision analyses either model an ‘average’ patient or a relatively homogeneous cohort of synthetic individuals, extracting values from the literature or expert opinion to characterize the impacted population and estimate the impact of policy alternatives. This assumes risks and costs are not distributed across the population and interventions' treatment effects are homogeneous. Our lab is developing methdos to directly integrate individual-level data to reflect the true heterogeneity in patient populations and capture differences in expected outcomes under different policy alternatives. This enables more accurate estimation of the trade-offs involved with an intervention and allows us to look at the distributional impact of interventions to reveal potential inequities.

Current projects

Current research themes include:

• Enabling individualized approaches to managing risks of iron deficiency for blood donors using machine learning and decision-analytic simulation modeling

• Using simulation modeling to optimize the use of predictive models to prevent service gaps in emergency medical services (paramedics and ambulances)

• Advancing the state-of-the-art in integrating data-driven methods (machine learning, Bayesian statistics) into decision-analytic models

• Developing and assessing methods for population health surveillance, including adjusting for assay and population differences to allow harmonization of data across cohortss

• Model-based economic assessments of health interventions, including cancer screening and surveillance.

Prospective lab members

Affiliated degree programs: I can serve as the thesis supervisor for students in Epidemiology (MSc and PhD), Biostatistics (MSc and PhD), Quantitative Life Sciences (PhD), and Experimental Medicine (MSc and PhD), which includes the MSc in Digital Health Innovation. For students in other degree programs, I may be able to serve as a practicum supervisor, co-supervisor, or committee member.

Current McGill students: If you are interested in working with the lab, please email me (Alton) your CV and a brief note about your interests.

Prospective McGill students: For prospective PhD students please send me (Alton) your CV, your intended degree program, and a brief note about your interests, ideally two to three months before the application deadline. Feel free to reach out earlier if you would like to discuss applying for a specific fellowship or scholarship that is due before the PhD program application. For prospective or admitted MSc students you are welcome to send your CV and a note about your interests. I typically meet with new MSc thesis students in their first semester and have them join the lab in the beginning of their second semester. In some cases, I am able to discuss potential projects and fellowship or studentship opportunities earlier. Applying for graduate funding (fellowships, studentships) cab increase the likelihood of admission to a graduate program and being able to complete your thesis with the D3Mod lab.

Prospective postdoctoral researchers or research staff: Any open research staff opportunities will be advertised on McGill’s Workday platform. Prospective postdoctoral researchers may reach out at any time. Prospective postdoctoral researchers who have external fellowship funding or are willing to apply for funding are more likely to receive a position.

Open science research philospohy

Our group works hard to produce research that is informative, rigorous, transparent, and reproducible. The Decision Modeling Lab Manual describes our approach to open research and dissemination.

Thank you to our funders

Research in the decision modeling lab is supported by grants from:

• Canadian Blood Services

• The AABB Foundation

• Fonds de recherche Québec – Santé

• Natural Sciences and Engineering Research Councel of Canada

• COVID-19 Immunity Task Force