Objectives:
     
     Since 2021, the Danish Medicines Council recommends the use of the Danish EQ-5D-5L value set when estimating utilities. The aim of this research was to develop and validate an algorithm that can accurately predict mean Danish EQ-5D-5L utilities based on published mean UK EQ-5D-3L utilities.
   

Methods:
     
     The study design incorporated a secondary analysis of patient-level UK EQ-5D-3L utility index scores from 11 oncology clinical trials. The EQ-5D-3L responses were mapped to EQ-5D-5L responses with the van Hout and Shaw preferred mapping algorithm. Model fitting and internal cross-validation were completed on a pooled dataset formed from eight trials including a total of 30,755 EQ-5D-3L responses. Three other trials were used for external validation (21,587 EQ-5D-3L observations).
   

Results:
     
     From the model fitting phase, a simple linear model for mean utility scores exhibited good fit and was selected as the optimal prediction algorithm. External validation using the algorithm to predict mean Danish EQ-5D-5L utilities was excellent, with the largest absolute prediction error being 0.020 (observed UK EQ-5D-3L means: 0.628-0.835).
   

Conclusions:
     
     The prediction algorithm developed in this research can increase analysts' ability to apply utilities aligned with the Danish EQ-5D-5L value set and guideline recommendations, reducing decision uncertainty. Many health technology assessment (HTA) institutions are transitioning from the EQ-5D-3L to the EQ-5D-5L in the coming years; therefore, prediction algorithms are likely of interest to additional HTA institutions in the near future. This study can provide a blueprint for future studies.