Predicting Danish EQ-5D-5L Utilities Based on United Kingdom EQ-5D-3L Utilities for Use in Health Economic Models

Einar B. Torkilseng (), Nathan Clarke, Liza Sopina, Lars Oddershede, Rasmus Trap Wolf, Rachael Lawrance, Andrew Trigg, Bryan Bennett and James W. Shaw
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Einar B. Torkilseng: Bristol Myers Squibb
Nathan Clarke: Adelphi Values Ltd
Liza Sopina: University of Southern Denmark
Lars Oddershede: Bristol Myers Squibb
Rasmus Trap Wolf: Bristol Myers Squibb
Rachael Lawrance: Adelphi Values Ltd
Andrew Trigg: Adelphi Values Ltd
Bryan Bennett: Bristol Myers Squibb
James W. Shaw: Bristol Myers Squibb

PharmacoEconomics - Open, 2025, vol. 9, issue 3, No 9, 433-443

Abstract: Abstract 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.

Date: 2025
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DOI: 10.1007/s41669-025-00562-6

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