Contributor: The Role of Dynamic Pricing in Health Technology Assessment for MDD

The life cycle of a drug encompasses phases of market exclusivity, competition, and eventual patent expiration, each influencing its pricing. Health technology assessment (HTA) plays a critical role in evaluating the cost-effectiveness and broader value of these drugs, shaping decisions on access and reimbursement. However, conventional HTA models often overlook how a drug’s price evolves over time, limiting the accuracy of drug costs included in these assessments.¹

Conventional HTA assessments generally assume static prices, which can misrepresent a drug’s long-term value. Dynamic pricing, on the other hand, accounts for price changes throughout a drug’s lifecycle, potentially offering a more realistic view of cost-effectiveness.² Changes in price due to competition, loss of market exclusivity, and patent expiration are integral to understanding a drug's economic impact. Incorporating dynamic pricing into HTA models can improve accuracy by addressing both short- and long-term value. Failing to consider dynamic pricing may lead to overestimating the incremental cost-effectiveness ratio (ICER), potentially resulting in resource misallocation. For instance, dynamic pricing might lower ICER estimates when comparing a branded drug with its generic counterpart or could even increase the ICER if a branded comparator loses patent protection earlier.³ These variations provide a nuanced understanding of cost-effectiveness, supporting a more accurate resource allocation in healthcare.

Applying dynamic pricing to MDD presents unique challenges. | Image Credit: © Kenishirotie - stock.adobe.com

Recognizing the importance of dynamic pricing, we applied it to the major depressive disorder (MDD) open-source value model.⁴ This use case highlights the model’s adaptability to real-world conditions, demonstrating its ability to incorporate dynamic pricing, test new methods, and integrate changing data inputs. However, applying dynamic pricing to MDD presents unique challenges. The MDD market has a limited flow of novel treatments, with most new innovative interventions—such as adjunctive digital therapy—focused on enhancements rather than entirely new mechanisms. Additionally, many existing MDD treatments have lost market exclusivity, creating pricing dynamics driven more by generics than by innovations. Furthermore, projecting dynamic pricing impacts is complicated by uncertainties around price changes before exclusivity loss and the pace of price declines afterward. Such complexities highlight the difficulties in incorporating dynamic pricing for MDD.

Incorporating dynamic pricing into cost-effectiveness analysis could offer significant benefits for equity in health care. More precise cost-effectiveness estimates enable policy makers and payers to allocate resources toward treatments with the best long-term value, promoting equitable access to essential therapies across diverse patient populations. For Medicaid and other government-funded insurance programs, this approach could help ensure that high-cost therapies are covered, even as prices fluctuate, supporting sustainable coverage for the people who need it most.

Incorporating dynamic pricing allows cost-effectiveness models to more accurately reflect drug costs in economic evaluations, ultimately improving resource allocation and treatment access for diverse patient groups. Although challenges remain—including data gaps and uncertainties with novel treatments—dynamic pricing has the potential to enhance efficiency in health care. The Center looks forward to sharing the insights from this MDD model use case in early 2025, offering a valuable demonstration of how real-world, life cycle–based cost assessments can shape health policy and economic evaluations.

References

1. Whittington MD, Neumann PJ, Cohen JT, Campbell JD. The case for including dynamic drug pricing in cost-effectiveness analyses under the IRA. Health Affairs Forefront. October 18, 2023. Accessed November 18, 2024. https://www.healthaffairs.org/do/10.1377/forefront.20231016.197078/full/

2. McQueen RB, Anderson KE, Levy JF, Carlson JJ. Incorporating dynamic pricing in cost-effectiveness analysis: are known unknowns valuable? PharmacoEconomics. 2023;41(3):321-327. doi:10.1007/s40273-022-01230-x

3.Neumann PJ, Podolsky MI, Basu A, Ollendorf DA, Cohen JT. Do cost-effectiveness analyses account for drug genericization? a literature review and assessment of implications. Value Health. 2022;25(1):59-68. doi:10.1016/j.jval.2021.06.014

4. MDD value model: an open source value model for MDD. Center for Innovation & Value Research. Accessed November 18, 2024. https://valueresearch.org/what-we-do/hta-models/major-depressive-disorder/