Life-Cycle Economic Modeling to Determine the Evolving Value of a Tumor-Agnostic Precision Oncology Therapy With Conditional Marketing Authorization
Author(s)
Emanuel Krebs, MA1, Gemma Cupples, PhD2, Deirdre Weymann, MA2, Dean Regier, PhD3.
1Head of Health Economics, Regulatory Science Lab, BC Cancer Research Institute, Vancouver, BC, Canada, 2BC Cancer Research Institute, Vancouver, BC, Canada, 3School of Population and Public Health, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada.
1Head of Health Economics, Regulatory Science Lab, BC Cancer Research Institute, Vancouver, BC, Canada, 2BC Cancer Research Institute, Vancouver, BC, Canada, 3School of Population and Public Health, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada.
OBJECTIVES: Precision oncology challenges health technology assessment (HTA) with small benefiting populations and non-randomized comparators. Life-cycle assessment integrating real-world evidence (RWE) can address uncertainty in evolving value for reimbursement decisions. We determined the value of a tumour-agnostic targeted therapy for NTRK fusion-positive advanced cancer patients compared to second-line therapies using RWE-informed, patient-centric modeling. We compared cost-effectiveness against the initial Canadian HTA evaluation.
METHODS: Our individual-level semi-Markov model consisted of six health states, reflecting variations in tumour-specific care. We simulated 5,000 patients representing seven tumour groups using monthly transitions. We derived clinical inputs by combining regulatory data for entrectinib (STARTRK/ALKA-372-001) with cross-jurisdictional real-world data from the United States (Flatiron Health) and British Columbia, Canada. We derived utility weights using Bayesian hierarchal modeling combining weights from regulatory EQ-5D-3L responses and estimates from EQ-5D instruments from systematic reviews. We estimated costs (CAD2024, public payer perspective) and quality-adjusted life years (QALYs) over 10 years (1.5% discounting), and calculated incremental net monetary benefit (INMB) at $100,000/QALYs. To compare life-cycle value, we replicated the initial HTA evaluation, conducted cross-model validation, and used a real-world clinical case mix for patient-centric modeling.
RESULTS: With patient-centric modeling, incremental costs and QALYs were $30,810 (95%CI: -$8,130, $70,857) and 0.01 (-0.36, 0.38). INMB was -$29,997 (95%CI: -$65,944, $9,629), and 41.1% simulations had lower QALYs and higher costs. Heterogeneity across tumours was substantial: lung had the largest QALY gains (0.18), and colorectal the highest cost-effectiveness probability (31.4%). Relative to the initial evaluation, patient-centric modeling resulted in 55.0% lower incremental costs and 92.1% lower QALY gains, with a higher probability of being cost-effective (10.2% v. 0.0%).
CONCLUSIONS: Life-cycle evaluation with RWE and patient-centric modelling reshapes our understanding of value for therapies targeting rare genetic alterations. Case mix is a critical determinant in assessing the impact of precision oncology interventions on health system resources.
METHODS: Our individual-level semi-Markov model consisted of six health states, reflecting variations in tumour-specific care. We simulated 5,000 patients representing seven tumour groups using monthly transitions. We derived clinical inputs by combining regulatory data for entrectinib (STARTRK/ALKA-372-001) with cross-jurisdictional real-world data from the United States (Flatiron Health) and British Columbia, Canada. We derived utility weights using Bayesian hierarchal modeling combining weights from regulatory EQ-5D-3L responses and estimates from EQ-5D instruments from systematic reviews. We estimated costs (CAD2024, public payer perspective) and quality-adjusted life years (QALYs) over 10 years (1.5% discounting), and calculated incremental net monetary benefit (INMB) at $100,000/QALYs. To compare life-cycle value, we replicated the initial HTA evaluation, conducted cross-model validation, and used a real-world clinical case mix for patient-centric modeling.
RESULTS: With patient-centric modeling, incremental costs and QALYs were $30,810 (95%CI: -$8,130, $70,857) and 0.01 (-0.36, 0.38). INMB was -$29,997 (95%CI: -$65,944, $9,629), and 41.1% simulations had lower QALYs and higher costs. Heterogeneity across tumours was substantial: lung had the largest QALY gains (0.18), and colorectal the highest cost-effectiveness probability (31.4%). Relative to the initial evaluation, patient-centric modeling resulted in 55.0% lower incremental costs and 92.1% lower QALY gains, with a higher probability of being cost-effective (10.2% v. 0.0%).
CONCLUSIONS: Life-cycle evaluation with RWE and patient-centric modelling reshapes our understanding of value for therapies targeting rare genetic alterations. Case mix is a critical determinant in assessing the impact of precision oncology interventions on health system resources.
Conference/Value in Health Info
2025-11, ISPOR Europe 2025, Glasgow, Scotland
Value in Health, Volume 28, Issue S2
Code
P7
Topic
Economic Evaluation, Health Policy & Regulatory, Health Technology Assessment
Topic Subcategory
Reimbursement & Access Policy
Disease
Oncology