Valuing Trial Designs from a Pharmaceutical Perspective Using Value-Based Pricing.

Area of Science:

• Health Economics
• Pharmaceutical Development
• Clinical Trial Design

Background:

• Traditional expected net benefit of sampling (ENBS) frameworks require adaptation for pharmaceutical industry drug development.
• Drug pricing is often conditional on clinical trial outcomes and cost-effectiveness (CE) data.

Purpose of the Study:

• To adapt the ENBS framework for pharmaceutical drug development trials.
• To incorporate value-based pricing (VBP) and Bayesian updating of CE model parameters.
• To optimize expected commercial net benefit for clinical trial designs.

Main Methods:

• Modified the traditional ENBS framework to include drug price conditional on trial outcomes.
• Utilized a value-based pricing (VBP) criterion with Bayesian updating of CE model parameters.
• Simulated 10,000 trial outcomes for various Phase III trial sample sizes and durations, calculating expected commercial net benefit (expected profits minus trial costs).

Main Results:

• Clinical trials with shorter follow-up periods and larger sample sizes yielded the highest expected commercial net benefit.
• Increasing trial follow-up duration had a minimal impact on profit forecasts.
• The adapted ENBS framework can guide optimization of commercial net benefit in pharmaceutical clinical trials.

Conclusions:

• The adapted ENBS framework provides a valuable tool for optimizing pharmaceutical clinical trial design.
• Value-based pricing and Bayesian updating are key components for estimating commercial net benefit.
• Complex CE models can make these analyses computationally intensive, requiring efficient methodologies.