REVISITING THE DESIGN OF MANAGED ENTRY AGREEMENTS: A METHODOLOGICAL FRAMEWORK TO STREAMLINE THEIR ADOPTION

Author(s)

Manuel Antonio Espinoza, MSc, PhD, MD1, Carlos Balmaceda, MPhil, MSc2.
1The University of Hong Kong, Hong Kong, Hong Kong, 2Post-doctoral Fellow, Bocconi University, Milan, Italy.
OBJECTIVES: Managed entry agreements (MEAs) are increasingly adopted across health systems to reconcile timely patient access with fiscal sustainability, yet instrument selection remains largely ad hoc. This study proposes a methodological framework grounded in health economics theory that links formal quantification of financial and economic risk to principled MEA instrument class selection.
METHODS: The framework distinguishes two risk dimensions. Financial risk is characterized through the technology expenditure distribution, yielding four metrics: Expected Payer Overspend (EPO), Expected Producer Shortfall (EPS), Mean Excess Expenditure (MEE), and Mean Shortfall (MS). Economic risk under second-order (parameter) uncertainty is operationalized via the Conditional Expected Loss (CEL), benchmarked against the Expected Gain of the Correct Decision (EGCD)—a novel construct introduced here—to determine whether coverage with evidence development is warranted. First-order (stochastic) uncertainty, reflecting genuine patient heterogeneity, is captured through the Expected Value of Individualized Care (EVIC) to guide outcome-based payment decisions. A decision map integrates these seven indicators into an instrument selection algorithm applicable across health systems regardless of income level or institutional context.
RESULTS: Applied to onasemnogene abeparvovec for SMA Type 1, the framework produces differentiated, analytically grounded recommendations: a financial instrument is warranted by expenditure risk, while coverage with evidence development (CEL/EGCD = 0.19) and outcome-based payment (EVIC = 3.3% of |E(INB)|) are not supported. The primary recommendation is price renegotiation—demonstrating the framework's capacity to rule out instruments as well as recommend them.
CONCLUSIONS: Pre-negotiation risk characterization provides the missing analytical foundation for MEA design. The proposed framework is theory-driven, computationally accessible through standard probabilistic modelling tools, and produces transparent, auditable outputs to support bilateral payer-manufacturer negotiation.

Conference/Value in Health Info

2026-11, ISPOR Europe 2026, Vienna, Austria

Value in Health, Volume 29, Issue 12S

Code

MSR287

Topic

Economic Evaluation, Health Technology Assessment, Methodological & Statistical Research

Disease

No Additional Disease & Conditions/Specialized Treatment Areas

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