OBJECTIVES

Economic evaluation for a target population is usually based on average clinical and economic outcomes. However, real-world patients within one target population are heterogeneous. Failure to accommodate patient heterogeneity in clinical outcomes, health state preferences and/or resource use in economic evaluation can bias cost-effectiveness results, leading to suboptimal reimbursement decisions. This study aims to review updates from the national pharmacoeconomic guidelines (NPGs) on patient heterogeneity. It also explores how heterogeneity issues have been dealt within the NICE Highly Specialized Technology (HST) program for rare diseases.

METHODS

A targeted literature review of NPGs (n=26) identified updates since January 2013. Updated guidance was extracted based on how guidelines advise to analyze and prespecify the sources of heterogeneity. To assess the current practice of dealing with heterogeneity in rare disease, all NICE HST recommendations were obtained. Data on the manufacturer’s analysis, comments from ERGs and the committee, and final recommendations were extracted.

RESULTS

Since January 2013, 11 of 26 NPGs have been updated, of which six updates had explicit and/or implicit implications on how patient heterogeneity should be addressed. Of these, two expanded on existing guidance and four added new guidance for heterogeneity. Australia has included a checklist that considers the sources of heterogeneity between trials and jurisdictions. All HSTs (n=14) received positive recommendations by NICE. Eight were targeted at a patient subgroup, of which five addressed heterogeneity using a subgroup analysis by the manufacturer.

CONCLUSIONS

Updates of NPGs to reflect heterogeneity remain limited and non-explicit. Only Australia has made explicit improvements on their guidance. Whilst the majority of HST submissions have tried to address heterogeneity, the extent of this is limited and often addressed through subgroups. Improving guidance on addressing heterogeneity is increasingly important in optimal resource allocation under constraints, particularly for high cost rare diseases.