Evaluating Policy Options for New Treatments Under Uncertain External Evidence.

Area of Science:

• Health economics
• Decision analysis
• Health policy

Background:

• Health policymakers face challenges adopting new medical treatments using foreign clinical evidence due to uncertain local applicability.
• This presents a trade-off between early patient access to therapies and the need for locally relevant data.

Purpose of the Study:

• To evaluate policy options for adopting new medical treatments based on uncertain external clinical evidence.
• To develop a framework for decision-making that incorporates applicability uncertainty and trial parameters.

Main Methods:

• Extended a Bayesian value-of-information framework using a power prior to weight external evidence by local applicability.
• Assessed four policy options: delayed adoption, conditional adoption, immediate adoption, and immediate rejection.
• Illustrated the framework with a lung cancer therapy case study involving China and the United States.

Main Results:

• Optimal policy choice is influenced by uncertainty in external evidence applicability, trial duration, trial completion delays, and treatment price.
• High applicability uncertainty favors delayed adoption; longer trial durations and delays in completion can make conditional adoption or delayed adoption more attractive.
• Price reductions tend to favor earlier adoption of new therapies.

Conclusions:

• Health policies for adopting treatments based on external evidence must be customized to the specific evidentiary context.
• Key factors include applicability uncertainty, trial timelines, potential delays, and cost considerations.