Presentation (CTI)

OBJECTIVES: Global randomized controlled trials (RCTs) are commonly used to inform cost-effectiveness models in the United States (US). However, trial populations often fail to reflect the demographic and clinical diversity of the US population. These discrepancies raise concerns about the validity of transferring global RCT treatment effects to real-world US decision-making, and even more to other ex-US markets. The objective is to propose a structured framework to assess and correct representativeness bias of global RCT populations relative to the US population, and other countries.
METHODS: A framework was developed based on a targeted review of methodological guidance on treatment effect heterogeneity, and transportability methods. It integrates interaction analyses, and statistical adjustment techniques.
RESULTS: A three-step framework was developed. First, key effect modifiers are identified using clinical expertise and trial data. Second, the representativeness of the RCT population is assessed by comparing the distribution of these modifiers between the RCT population and a representative US or local target population. When imbalances are identified, statistical methods can be applied to adjust RCT data to better reflect the US/local population. Third, to assess representativeness for ex-US target markets, the framework incorporates additional considerations. If Japan is the target market, the Asian RCT subgroup can be evaluated, to account for contextual differences such as lifestyle, healthcare systems, and clinical management.
CONCLUSIONS: This framework provides a structured and transparent approach to assessing and addressing population representativeness when using global RCT data to inform US and ex-US cost-effectiveness analyses. By explicitly identifying key effect modifiers and leveraging real-world data and statistical adjustment methods, the framework allows decision-makers to quantify potential bias and improve the relevance of estimated treatment effects for real-world populations. Early consideration on representativeness and targeted investment in local real-world data can support more robust and decision-relevant health economic evaluations across jurisdictions.