Evaluating Policy Options for New Treatments Under Uncertain External Evidence

Health policymakers in many jurisdictions must decide whether to adopt new medical treatments based on clinical evidence generated abroad, despite uncertainty about the applicability of those results to local populations. This creates a fundamental trade-off: enabling early access to promising therapies versus delaying adoption until locally relevant evidence becomes available.

We evaluate four policy options under uncertain external clinical evidence: delayed adoption pending a local confirmatory trial, conditional adoption with a standard trial requirement, immediate adoption without a trial, and immediate rejection. To compare these strategies, we extend a Bayesian value-of-information framework using a power prior. The power prior parameter determines the weight assigned to external evidence based on its applicability to the local population. The framework distinguishes pre- and postconfirmation periods and accounts for potential delays in trial completion when conditional approval is granted. We illustrate the approach using a lung cancer therapy trialed exclusively in China and considered for use in the United States.

The case study shows that optimal policy depends jointly on uncertainty about external evidence applicability, trial duration, delays in trial completion, and price. High uncertainty favors delayed adoption, whereas longer trial durations make conditional adoption more attractive by increasing the cost of waiting. Longer delays in trial completion can shift the optimal policy toward delayed adoption. Price reductions shift the policy frontier toward earlier access.

Policies for treatments supported by external evidence should be tailored to the evidentiary context, accounting for applicability uncertainty, trial timelines, delays in trial completion, and pricing.