PRICING, INNOVATION, AND THE VALUE OF LARGER PHASE III TRIALS
Author(s)
David Glynn, PhD.
University of Galway, Galway, Ireland.
University of Galway, Galway, Ireland.
OBJECTIVES: This paper extends the classical Value of Information (VOI) framework to evaluate the value of conducting further research, specifically, optimizing Phase III trial sample sizes. An optimal pricing rule for innovative technologies will choose a payment based on the Phase III result which balances static and dynamic efficiency (endogenous pricing). However, finite Phase III sample sizes leave an irreducible "approval problem," exposing health insurers to risks of false-positive and false-negative approvals. The objective is to define the value of larger Phase III trials within this context and investigate the policy options available to health systems.
METHODS: Adopting the perspective of a universal health insurer, we construct a value function which maps trial sample size (n) to net health gains (reflecting both immediate patient benefits and benefits from future innovations). Under current arrangements private firms bear trial costs, so we evaluate two policy interventions available to insurers: mandating a socially optimal minimum sample size (n*) and offering an optimal per-patient enrolment subsidy (sub*).
RESULTS: Larger Phase III trials generate net health gains by reducing decision errors, thereby increasing the expected effectiveness of approved treatments. However, there are distinct trade-offs from policies which increase the size of these trials. Directly mandating a larger sample size increases firm entry costs, which can negatively impact dynamic efficiency. Conversely, providing a subsidy lowers a firm's marginal sampling costs and boosts technology profitability, though paying this subsidy introduces direct health opportunity costs for the healthcare system.
CONCLUSIONS: Accounting for endogenous pricing and dynamic efficiency expands classical VOI methodology. Creating the greatest health benefit requires insurers to choose policies which carefully balance immediate risk reduction against long-term pharmaceutical innovation incentives and health opportunity costs.
METHODS: Adopting the perspective of a universal health insurer, we construct a value function which maps trial sample size (n) to net health gains (reflecting both immediate patient benefits and benefits from future innovations). Under current arrangements private firms bear trial costs, so we evaluate two policy interventions available to insurers: mandating a socially optimal minimum sample size (n*) and offering an optimal per-patient enrolment subsidy (sub*).
RESULTS: Larger Phase III trials generate net health gains by reducing decision errors, thereby increasing the expected effectiveness of approved treatments. However, there are distinct trade-offs from policies which increase the size of these trials. Directly mandating a larger sample size increases firm entry costs, which can negatively impact dynamic efficiency. Conversely, providing a subsidy lowers a firm's marginal sampling costs and boosts technology profitability, though paying this subsidy introduces direct health opportunity costs for the healthcare system.
CONCLUSIONS: Accounting for endogenous pricing and dynamic efficiency expands classical VOI methodology. Creating the greatest health benefit requires insurers to choose policies which carefully balance immediate risk reduction against long-term pharmaceutical innovation incentives and health opportunity costs.
Conference/Value in Health Info
2026-11, ISPOR Europe 2026, Vienna, Austria
Value in Health, Volume 29, Issue 12S
Code
MSR279
Topic
Economic Evaluation, Health Technology Assessment, Methodological & Statistical Research
Disease
No Additional Disease & Conditions/Specialized Treatment Areas