Dean Regier

Faculty of Medicine

Research Classification

Medical, health and life sciences

Research Interests

Health economics

Regulatory Science

Causal inference

Discrete choice experiments

Natural Language Processing

Health services research

Econometrics

Federated Learning

Relevant Thesis-Based Degree Programs

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Affiliations to Research Centres, Institutes & Clusters

Academy of Translational Medicine

Centre for Health Services and Policy Research

Biography

Research Methodology

Recruitment

Looking to recruit:

Master's students

Desired start dates:

2027

Potential research project areas:

Research Areas for Graduate Students in Translational Medicine, Health Economics, and Regulatory Science

Graduate students in the lab engage in projects spanning regulatory science, health economics, causal inference, and generative AI, with a shared goal of transforming discovery into scalable, equitable, and cost-effective healthcare solutions. These areas offer not only intellectual depth but also direct relevance to policy, regulatory decision-making, and patient care, making them ideal for master’s and PhD training.

1. Generative AI and Real-World Evidence (RWE) in Health Systems

With health systems generating unprecedented amounts of clinical and administrative data, graduate projects can apply generative AI to advance evidence generation. This includes developing tools that simulate trial populations, synthesize qualitative insights from patients and clinicians, and support regulatory submissions based on real-world evidence. Research can also examine fairness, reproducibility, and explainability of generative AI models in healthcare, ensuring they enhance rather than hinder equitable access.

2. Causal Inference for Translational Medicine

Robust causal inference methods are essential for understanding treatment effects outside of randomized trials. Students can develop and apply methods such as target trial emulation, clone-censor-weighting, and causal survival analysis to estimate treatment effects from observational data. Projects may compare real-world evidence to trial findings, test policy-relevant assumptions, and assess how causal inference can strengthen regulatory and reimbursement decisions.

3. Health Economics of Precision Medicine

Students can evaluate the cost-effectiveness of emerging technologies such as genomic sequencing, liquid biopsies, and tumour-agnostic therapies. This includes developing economic models that estimate lifetime costs, quality-adjusted life years (QALYs), and distributional consequences across patient populations. Projects may involve calculating willingness-to-pay thresholds for novel therapies, exploring opportunity costs at the health system level, and examining how reimbursement policies influence technology adoption.

4. Regulatory Science and Life-Cycle Evidence Generation

Another area is the study of regulatory decision-making across the product life cycle—from pre-market trial evidence through real-world performance. Students can investigate accelerated approval pathways (e.g., conditional approvals in oncology), post-market evidence requirements, and international harmonization of standards. Comparative analyses of regulatory language used by Health Canada, the FDA, and EMA can provide insight into how benefit–risk assessments evolve. There is also scope for evaluating how adaptive licensing and real-world data influence patient access.

5. Patient Preferences and Decision Science

Students with training in economics or statistics can design and analyze discrete choice experiments (DCEs) to quantify patient and caregiver preferences. Projects might involve understanding trade-offs patients make between treatment efficacy, toxicity, and cost, or estimating part-worth utilities using machine learning. This research directly informs health technology assessment (HTA) agencies by incorporating patient voices into reimbursement decisions.

6. Equity and Access in Translational Medicine

Graduate work can also focus on distributional health impacts, ensuring that innovations benefit underserved populations. This may involve evaluating equity-efficiency trade-offs in funding decisions, examining the role of social determinants in access to genomic technologies, and proposing frameworks to integrate equity considerations into regulatory and reimbursement processes. Students could also analyze how provincial differences in coverage across Canada shape access to personalized oncology or rare-disease therapies.

7. Learning Health Systems and Implementation Science

Finally, students can study how health systems themselves become engines of innovation. This includes designing learning health systems that continuously evaluate technology uptake, clinical outcomes, and economic performance. Projects may integrate implementation science frameworks with regulatory and health economic perspectives, testing models for scaling up high-value innovations while de-implementing low-value care.

Ideal applicant profile:

Graduate students in the lab will be motivated by the opportunity to bridge discovery science, regulatory science, and health economics and implementation to accelerate equitable access to innovation. We are looking for applicants who bring both strong technical training and a curiosity about how evidence shapes policy, regulation, and patient care.

Academic Background

Master’s applicants: A bachelor’s degree with high honours in economics, health sciences, public health, statistics, engineering, computer science, or a related discipline.
PhD applicants: A completed or near-completed master’s degree in health economics, economics, biostatistics, epidemiology, computer/data science, regulatory science, or a related field.
Coursework or research experience in econometrics, causal inference, or health economic evaluation is an asset.

Technical Skills

Strong quantitative methods: proficiency in econometrics, statistical inference, and/or causal inference frameworks.
Familiarity with health economic modeling
Competence in programming languages such as R, Python, or Stata; exposure to machine learning methods is desirable.
Interest or experience in generative AI and large language models, particularly their application in health data, regulatory science, or qualitative research synthesis.

Personal Attributes

Analytical and creative thinker: able to apply rigorous methods while innovating in approach.
Lateral thinking: approaching challenges from unconventional, indirect, or unexpected perspectives
Collaborative and interdisciplinary: enjoys working across teams that include clinicians, economists, data scientists, and policymakers.
Mission-driven: motivated by the goal of transforming discovery into equitable and cost-effective patient care.
Resilient and adaptive: comfortable working in areas where evidence, methods, and policy are evolving rapidly.

Professional Development

Students in the lab will:

Receive fully funded scholarships to attend UBC, with additional opportunities to earn additional support through research assistantships
Train in cutting-edge methods (causal inference, generative AI, advanced economic modeling).
Contribute to real-world policy and regulatory discussions in Canada and internationally.
Experience presenting findings to regulatory bodies,and health system leaders.
Mentorship to prepare for careers in academia, government, health systems, and industry.

Other options:

I support public scholarship, e.g. through the Public Scholars Initiative, and am available to supervise students and Postdocs interested in collaborating with external partners as part of their research., I support experiential learning experiences, such as internships and work placements, for my graduate students and Postdocs., I am open to hosting Visiting International Research Students (non-degree, up to 12 months)., I am interested in hiring Co-op students for research placements.

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ADVICE AND INSIGHTS FROM UBC FACULTY ON REACHING OUT TO SUPERVISORS

These videos contain some general advice from faculty across UBC on finding and reaching out to a potential thesis supervisor.

If you have reviewed some of this faculty member's publications, understand their research interests and have reviewed the admission requirements, you may submit a contact request to this supervisor.

Graduate Student Supervision

Doctoral Student Supervision

Dissertations completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest dissertations.

Methods for the economic evaluation of personalized medicine : a case study in advanced colorectal cancer (2023)

With the use of precision medicine in oncology, where choice of treatment is informed by the molecular characteristics of the disease, we expect to see heterogeneity in the effectiveness and costs of interventions. New precision medicine interventions are often costly and evidence from randomized controlled trials may not be available, yet decision-makers must be able to evaluate these interventions appropriately to inform efficient health resource allocation. The goal of my thesis is to explore methods to quantify the value and impact of identifying heterogeneity, using real-world observational data. The use of third-line anti-EGFR therapy (cetuximab and panitumumab) informed by RAS mutation status for patients with metastatic colorectal cancer is used as the example throughout the work. The analysis uses linked administrative data for a historical cohort of patients with metastatic colorectal cancer who were potentially-eligible for third-line systemic therapy. Using these data I conducted a cost-effectiveness analysis of anti-EGFR therapy informed by KRAS testing, and a cost-effectiveness analysis of panel-based expanded RAS testing vs. simple KRAS testing. I also conducted a literature review to identify and compare alternative frameworks for valuing heterogeneity-informed treatment decisions in the context of precision medicine. Based on this review, I selected the value of heterogeneity framework to evaluate alternative RAS-based subgrouping strategies to inform anti-EGFR therapy.The results of the analysis indicate that at the lower range of cost-effectiveness thresholds, anti-EGFR therapy would not be considered cost-effective regardless of subgrouping strategy. Value of heterogeneity analysis indicates that at a threshold of $100,000/LYG the value gained from subgroup-based decisions exceeds the costs of the genomic testing required to define the subgroups. Resolving uncertainty, or reducing the costs of testing and treatment, could provide considerable additional value.The value of heterogeneity framework can complement conventional methods for economic evaluation by describing and valuing the heterogeneity that arises with the use of precision medicine in a more comprehensive way. This research also demonstrates the strengths of using real-world data to conduct value of heterogeneity analysis. The precision medicine landscape is continuously evolving, and embracing new methods and sources of evidence will help decision-makers keep pace with these changes.

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Master's Student Supervision

Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.

Heuristics and health data : a qualitative study of cancer patients' data sharing preferences (2023)

Introduction: To advance the evaluation of precision oncology requires greater access to currently siloed patient data. The infrastructure supporting this access rests on patient consent, and as a result must be responsive to patients’ considerations when deciding whether to share their data. Data sharing considerations have been studied by researchers through the lens of heuristic theory, but heuristics have not been studied in the health data sharing context. This thesis addresses this gap, exploring how cancer patients employ heuristics when assessing the risks and benefits of sharing their data with researchers. Methods: We conducted a qualitative investigation of the data sharing preferences of cancer patients and survivors in Canada. A semi-structured question guide led the groups through discussions of opinions, anecdotes, and preferences that revealed underlying heuristic processes. Transcripts were analyzed using a codebook developed from a literature review on data sharing heuristics. Heuristic instances were connected to related attitudes and intentions to share and were then grouped in decision-making themes. Results: We ran three focus groups with 19 participants in total. We identified 12 heuristics underlying their preferences and intentions for data sharing, and 17 attitudes related to these heuristics. We generated four themes that reflect patterns of heuristic processing: (1) altruism as a social rule, (2) trust as a measure of legitimacy, (3) gaining power and security through control, and (4) framing risk and benefit through personal experiences. A cross-cutting interpretation of these themes highlighted the influence that certain attitudes and heuristics have across different decision preferences and patterns of cancer patients. Discussion: The findings revealed new relationships between heuristics and well-known preferences for data sharing. Our study provides a novel perspective on the preferences influencing health data sharing decisions and how they may sometimes be based on heuristic as opposed to rational processing. Further research can expand on this, testing actual behaviour patterns and validating the influence of heuristics on decision-making. These findings implicate the design and communication of data sharing infrastructure by recognizing the role that non-deliberative, intuitive processes play in a cancer patient’s assessment of risk and benefit when making data-sharing decisions.

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Value-based real option analysis to support early-stage drug development (2020)

Background: Value-based frameworks link costs with health outcomes and are considered in drug reimbursement, suggesting the increasing need to estimate commercial performance of novel drugs in relation to demonstrating cost-effectiveness. Objective: To develop a value-based drug development framework and evaluate a commercialization strategy for a phase 1 drug candidate for hypoglycemia in type 1 diabetes (T1D).Methods: A value-based real options analysis (VB-ROA) framework was developed to incorporate payer and for-profit investor perspectives by integrating cost-effectiveness analysis (CEA) with real options analysis (ROA). The framework was applied to commercially evaluate a phase 1 drug candidate to prevent hypoglycemia.The VB-ROA framework was constructed in two stages: 1. Value-based price was estimated using headroom analysis based a Markov model assuming a US payers’ willingness to pay (WTP, λ) of $50,000 per quality-adjusted life year (QALY) and payers’ discount rate (rd) of 3%. The drug candidate’s target product profile (TPP) was based on clinician reports on meaningful health improvements.2. ROA via the binomial lattice option pricing model (BOPM) using revenues based on value-based pricing and a cost of capital (rc) of 13.2%.Data to populate model parameters were gathered from published clinical, regulatory, and market data.Results: The value-based drug price was $5,178 (95% CI $4,437, $5,956) per year per patient. The phase 1 development option value was $0 (V₀,₁). The development strategy could be abandoned or revised, which may involve partnering non-profit institutions. If successful, the development option for phase 2 is $67 Million (V₁,₁) or $0 (V₁,₂). If development leads to regulatory approval, the option value to launch ranges from $8,716 Million (V₇,₁) to $127 Million (V₇,₈). Sensitive parameters to option value include investors’ cost of capital (rc), drug price, development risks (θt), market share, λ, health-related quality of life (HRQoL) weights, and the relative risk of non-severe hypoglycemia (RRNSHday & RRNSHnoc).Conclusions: The VB-ROA framework aligns patient, payer, and investor incentives to assess the impact of clinical and cost-effectiveness parameters on the commercial potential of novel drugs, which further enables the development novel drugs that are affordable for payers and patients, while profitable for investors.

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