Presentation (CTI)

OBJECTIVES: In the United States, cost-effectiveness analyses (CEAs) are an integral component of reimbursement and coverage decision-making. However, conventional economic models typically assume constant cost parameters over the time horizon, despite real-world United States (US) pricing dynamics where drug prices evolve due to competition, contracting, and policy negotiations. Dynamic drug pricing (DDP) frameworks allow prices to adjust over time in response to observed outcomes and market conditions, offering a potential approach to better support value-based reimbursement decisions. To evaluate the impact of a DDP framework on cost-effective outcomes from a US payer perspective using a hypothetical economic model.
METHODS: A Markov cohort-based cost-effectiveness model was developed to compare a novel therapy with the standard of care over a 10-year horizon from a payer perspective. Model inputs for clinical effectiveness, health-state transitions, and utilities were derived from published literature reflecting plausible real-world outcomes. Incremental quality-adjusted life-years (QALYs) were generated using simulated longitudinal effectiveness data. Drug prices were reassessed every two years and adjusted according to predefined performance thresholds based on cumulative QALYs. Results from the dynamic pricing scenario were compared with a conventional static pricing approach. Costs and outcomes were discounted by 3% annually.
RESULTS: Under a static pricing scenario, the incremental cost-effectiveness ratio (ICER) was estimated at USD 61,000 per QALY gained. Implementation of DDP resulted in price reductions of up to 20% over the 10-year horizon, leading to 14% reduction in cumulative drug costs. This improved the ICER to USD 48,000 per QALY gained. At a willingness-to-pay threshold of USD 50,000 per QALY, the probability of cost-effectiveness increased from 42% under static pricing to 63% under dynamic pricing.
CONCLUSIONS: This analysis demonstrates that incorporating DDP assumptions across the model time horizon can generate more representative and policy-relevant ICER estimates than conventional static pricing approaches while also reducing payer risk in the United States.