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Updated: Sep 2, 2025

Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets
Published on: February 23, 2024
The Effect of the Drug Life Cycle Price on Cost-Effectiveness: Case Studies Using Real-World Pricing Data
Marcel H Schöttler1, Friso B Coerts2, Maarten J Postma3
1Health-Ecore B.V., Zeist, The Netherlands; Unit of Global Health, Department of Health Sciences, University Medical Center Groningen (UMCG), University of Groningen, Groningen, The Netherlands.
Objectives:
Cost-effectiveness analyses (CEAs) generally assume constant drug prices throughout the model time horizon, yet it is known that prices are not constant, often with price decreases near loss of exclusivity (LOE). This study explores the impact of using dynamic drug-specific prices on the incremental cost-effectiveness ratio (ICER) using selected reproduced case studies.
Methods:
Case studies were selected following explicit criteria to reflect a variety of drug characteristics. For each drug, a published CEA model was identified, replicated, and modified with dynamic real-world pricing data, to compare ICERs based on constant drug prices with estimates obtained when including drug life cycle pricing. The impact of dynamic real-world pricing-inclusive LOE-was analyzed using a single patient cohort and multiple cohorts over time.
Results:
Fluvastatin, alendronic acid + colecalciferol combination therapy, letrozole and clopidogrel were selected as case studies. Inclusion of real-world pricing data compared with applying constant prices reduced the ICER in a single-cohort setting up to 43%. In the multicohort analyses, further reductions of the ICERs were observed of up to 113%. The ICERs were sensitive to the period of drug usage relative to the models' time horizons, the relative proportions of drug costs in the overall treatment costs, and timing of LOE compared with the cost year of the original analysis.
Conclusions:
Assuming dynamic drug prices may lead to more representative ICER estimates. Future CEAs for drugs could account for predicted and disaggregated life cycle price developments based on retrospective data.
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