Estimation of an EORTC QLU-C10 Value Set for Spain Using a Discrete Choice Experiment

Abstract

Background The EORTC QLU-C10D is a preference-based measure derived from the EORTC QLQ-C30. For use in economic evaluations, country-specific value sets are needed. This study aimed to generate an EORTC QLU-C10 value set for Spain. Methods A sample of the Spanish general population completed an online discrete choice experiment. An attribute-balanced incomplete block design was used to select 960 choice tasks, with a total of 1920 health states. Each participant was randomly assigned 16 choice sets without replacement. Data were modelled using generalized estimating equations and mixed logistic regressions. Results A total of 1625 panel members were invited to participate, 1010 of whom were included in the study. Dimension decrements were generally monotonic with larger disutilities at increased severity levels. Dimensions associated with larger decrements were physical functioning and pain, while the dimension with the smallest decrement was sleep disturbances. The PITS state (i.e. worst attainable health) for the Spanish population is − 0.043. Conclusions This study generated the first Spanish value set for the QLU-C10D. This can facilitate cost-utility analyses when applied to data collected with the EORTC QLQ-C30.

Danish value sets for the EORTC QLU-C10D utility instrument

“…In our sample, the utility decrement for physical functioning was the largest, which was also the domain with the largest decrement for many other countries; the decrement was comparable to Austria [ 15 ], Australia [ 24 ], and smaller when compared to Italy and Poland [ 15 ]. Following physical functioning, the domain with the second largest decrement was pain, which is a pattern that was, for example, also observed in, for example, Austria [ 15 ], Germany [ 20 ], Spain [ 14 ], France [ 30 ], and the United Kingdom [ 32 ].…”

“…Compared to many other countries however [ 14 , 15 ], the domain with the third largest decrement in the Danish utility weights was not role functioning but emotional functioning. In general, the decrement for emotional functioning was large with − 0.124, indicating that emotional outcomes are more highly valued or considered to have a strong impact on health in the Danish population.…”

“…For this study, we followed the methods for the generation of utility weights established by King et al [ 24 ] which were also used in the valuation surveys for other countries [ 14 , 15 , 18 , 20 , 30 ]. In this cross-sectional valuation survey, participants from the Danish general population were recruited via a third-party provider online panel ( https://surveyengine.com/ ).…”

“…As different countries and healthcare systems have different approaches to health economic evaluations and CUA [ 11 ] and health state preferences are known to differ between countries [ 15 ], country-specific utility weights should be used where available and required by agencies. For the QLU-C10D, utility weights have already been established for a number of European countries [ 14 , 15 , 18 , 20 , 30 ], Australia [ 24 ], the United States [ 36 ], Canada [ 29 ], and first Asian countries [ 28 ].…”

The FACT-8D, a new cancer-specific utility algorithm based on the Functional Assessment of Cancer Therapies-General (FACT-G): a Canadian valuation study

“…The Multi-Attribute Utility in Cancer (MAUCa) Consortium has derived a MAUI to generate utilities from data collected from the QLQ-C30 to produce the European Organization of Research and Treatment in Cancer (EORTC) QLU-C10D [11]. Since its development, a number of country-specific utility weights for the QLU-C10D have followed, including Canada [12][13][14][15][16][17][18][19]. Recently, the MAUCa Consortium derived the health state classification system from items of the FACT-G and the Australian value set for the new FACT-8D has now been produced [20].…”