Regulatory Science Lab
The Regulatory Science Lab develops the methods and evidence that inform regulatory and reimbursement decisions, with a focus on the value, sustainability, and social impacts of precision medicine.
Led by Dr. Dean Regier, the lab forms the methodological foundation of the UBC Academy of Translational Medicine and is based at BC Cancer Research.
Explore the Regulatory Science Lab
Find the lab’s publications, learn about its researchers and current work, or connect with the team about a potential collaboration.
Publications
Explore peer-reviewed research on economic valuation, real-world evidence, regulatory methods, equity, and patient preferences.
People and projects
Meet the lab’s researchers and learn about its current projects, partnerships, and open positions.
Collaborate
Connect with the lab about research collaboration, data partnerships, or media enquiries.
What the lab does
The Regulatory Science Lab develops and tests methods for generating, evaluating, and applying evidence across the life cycle of health technologies.
Its research includes the economic evaluation of genomic and precision technologies, the use of real-world data to estimate effectiveness and cost-effectiveness, approaches to regulatory evidence and flexibility, and the assessment of equity and patient preferences.
This work helps regulators, payers, health systems, and innovators understand what evidence is needed, how uncertainty should be assessed, and how the value and social impacts of new technologies can be considered in decision-making.
Research with a practical application
The methods developed by the lab inform the regulatory, evidence, implementation, and strategic support provided through the Academy of Translational Medicine.
Research themes
The lab’s research addresses four connected areas of regulatory and reimbursement decision-making.
Economic valuation
Economic evaluation of genomic and precision technologies, including multi-gene panel sequencing and tumour-agnostic therapies.
Real-world evidence
Methods that draw causal conclusions from population-level health data, including target trial emulation and life-cycle evidence approaches.
Regulatory evidence and flexibility
What counts as substantial evidence when trials are small, and how regulatory flexibility can be exercised without loosening the standard of proof.
Equity and patient preferences
Who benefits and who is left out when a technology is funded, and how patient preferences can be assessed at scale.
Capabilities and infrastructure
Methodological research is supported by data, tools, and partnerships that allow the lab’s work to reach decision-makers.
Regulatory affairs large language model
A large language model built specifically for regulatory affairs, developed to support evidence review and regulatory submission work.
Population health data
Population-level patient data held at BC Cancer, used to estimate effectiveness, cost-effectiveness, and outcomes in real-world settings.
Learning health systems infrastructure
AI-enabled infrastructure that connects evidence generation to decisions made in routine care.
Regulatory and reimbursement partnerships
Working relationships with Health Canada, the Institut national d’excellence en santé et en services sociaux (INESSS), and Canada’s Drug Agency.
International collaboration
Research and industry partnerships with institutions outside Canada.
How the lab connects to ATM
Research, practice, and training strengthen one another across ATM.
Research informs practice
RSL methods strengthen evidence strategies and decision pathways for innovations.
Practice shapes research
Real-world cases reveal recurring barriers and new research questions.
Knowledge builds capability
Research and practical experience inform training and strengthen the system’s ability to support translation.
Latest Research, 2026-2025
Recent research from the Regulatory Science Lab and its collaborators.
PLOS Digital Health
Advancing the science of qualitative patient preference assessment using large language models
Grover T, Krebs E, Weymann D, Ehman M, Regier DA. Advancing the science of qualitative patient preference assessment using large language models. PLOS Digital Health.
Tests whether an open-source LLM can replicate human thematic analysis of focus-group data on patient attitudes toward health data-sharing platforms, finding moderate-to-strong overlap with human-coded themes across three prompting strategies.
Genetics in Medicine
Forced resilience: Indigenous perspectives on systemic barriers and humanizing genomic medicine in British Columbia, Canada
Ehman M, Montour L, Pollard S, Weymann D, Kirk D, Brown K, Epp S, Wadsworth D, Garrison N, Laberge AM, Caron N, Arbour L, Regier DA. Genetics in Medicine. https://doi.org/10.1016/j.gim.2026.102555.
Examines Indigenous families’ perspectives on diagnostic genomic sequencing in BC, finding that physical and relational access barriers, racism, and persistent demands for self-advocacy impose “forced resilience” as an unspoken condition of care.
JCO Precision Oncology
Clinical effectiveness and cost-effectiveness of multigene panel sequencing in advanced melanoma: A population-level real-world target trial emulation
Krebs E, Weymann D, Ho C, Weppler A, Bosdet I, Karsan A, Hanna TP, Pollard S, Regier DA. 2025; 9:e2400631:1–12.
Uses target trial emulation to estimate effectiveness and cost-effectiveness from population health data.
Frontiers in Medicine
Putting the substance in substantial evidence: An evidence-based approach to flexible drug regulation
Krebs E, Bubela T, McPhail M, McCabe C, Regier DA. 2025; 12:133789.
Sets out how regulators can exercise flexibility without lowering the standard of proof.
Frontiers in Medicine
How Life-Cycle Real-World Evidence Can Bridge Evidentiary Gaps in Precision Oncology
Krebs E, Weymann D, Bubela T, Regier DA. 2025; 12:1563950.
Shows where real-world data can close the evidence gaps that trials in precision oncology leave open.
Health Services Research
Determining the Survival Impact and Cost-Effectiveness of Multi-Gene Panel Sequencing in Metastatic Colorectal Cancer With Super Learning Approaches
Krebs E, Weymann D, Lim H, Yip S, Regier DA. 2025; e70009. Advance online publication.
Applies machine learning methods to population data to estimate survival and cost-effectiveness outcomes.
Learn more and connect
Team, current projects, and open positions are maintained on the Regulatory Science Lab page at BC Cancer Research.
For research collaboration, data partnerships, or media enquiries about the lab’s work, contact the Academy of Translational Medicine.