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

Currently funded projects include:

• Analyzing the operational impact of a patient portal with propensity score matching

• Assessing the public health value of blood donor data with Bayesian modeling

• Developing an individualized approach to managing risk of iron deficiency in repeat blood donors using machine learning, optimization, and simulation

Prospective lab members

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

Prospective McGill students: For prospective PhD students in Epidemiology, Biostatistics, or Quantitative Life Sciences, please send me (Alton) your CV 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 in Epidemiology or Biostatistics, 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. Developing a plan to apply for graduate funding (fellowships, studentships) can improve your probability of success.

Prospective postdoctoral researchers or research staff: If we have open research staff opportunities, these will be advertised on McGill’s Workday platform. Prospective postdoctoral researchers are welcome to reach out at any time, particularly if you have identified an external fellowship for which you’d like to co-develop an application.

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 National Blood Foundation

• Fonds de recherche Québec – Santé

• Natural Sciences and Engineering Research Councel of Canada

• COVID-19 Immunity Task Force