Dean Regier

Associate Professor

Relevant Thesis-Based Degree Programs


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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