1. Feng Xie Department of Clinical Epidemiology and Biostatistics McMaster University

2. Acknowledgements  Coauthors: Eleanor Pullenayegum, Simon Pickard, Juan Manuel Ramos Goni, Min-Woo Jo, Ataru Igarashi  We thank Drs. Ben Van Hout, Elly Stolk, Nan Luo, Juntana Pattanaphesaj, Juan Manuel Ramos Goñi, Min- Woo Jo, and Ataru Igarashi for sharing their data  This project was sponsored by a fast-track research grant from the EuroQol Research Foundation (#2013180)  Drs. Feng Xie is funded by the Canadian Institutes for Health Research New Investigator Award (MSH #122801). Dr. Feng Xie is also supported by McMaster University and St. Joseph’s Healthcare Hamilton.  None of the sponsors had any involvement in the design and conduct of the study, collection, analysis, and interpretation of the data, preparation, review and approval of the work.

3. EQ-5D-5L Valuation Study  An international initiative by the EuroQol Group  Standardized protocol – EuroQol Valuation Technology (EQ-VT)  Canada, Spain, UK, the Netherlands, Japan, Thailand, Korea, and China  More countries…

4. Discrete choice experiment (DCE)

5. DCE vs TTO Full health Dead State 1 State 2 1.0 0.0 health utility DCE latent utility  Cognitive challenge  Online vs face-to-face interview  Health utilities from TTO vs latent utilities from DCE

6. The motivation  Feasibility issues in conducting interviews with a national representative sample in geographically-spread countries or those with resource constraint  DCE could be a practical alternative if an existing transforming function can be used

7. Hypothesis and objective  The relationship between different methods in eliciting health preference may be similar across countries given the same underlying construct being elicited  To compare generic functions with country-specific functions in transforming latent utilities to health utilities

8. The data sets  Valuation study data from the 8 countries  TTO –derived health utilities for 86 health states  196 state pairs using DCE  Each participant was asked to value 10 health states using TTO and 7 pairs of states using DCE

9. Transforming L to U 1 Conditional logit model to derive latent utilities using DCE data 2 Calculating mean TTO-derived health utility for each of 86 states 3 fractional polynomial models to transform L to U (e.g. E(U|L)= β 0 + β 1L a ) 4 Calculating mean absolute error (MAE) between predicted and observed health utility for each state without including the data from that state in modeling

10. Criteria for MAEs  The standard deviations (SDs) of the MAEs from 18 EQ-5D (3 level) TTO- based valuation studies  ≤1 SD (0.02): acceptable;  1 SD<~<2 SDs (0.02 to 0.04): applied with caution  ≥2 SDs (0.04): unacceptable.

11. Study and respondent characteristics CanadaU.K.SpainNetherlandsChinaThailandKoreaJapan No of respondents* 1209122110009831299121610801026 No of interviewers116033192162731 Use of commercial survey company N YYNNNYY Age, years, mean±SD 47.5± 17.4 51.0 ± 17.9 43.8 ± 17.3 47.2 ± 16.8 42.3 ± 16.2 43.5 ± 15.1 45.0 ± 14.3 44.9 ± 14.9 Female, n(%) 667 (55.0%) 710 (58,2%) 525 (52.5%) 507 (51.6%) 649 (50.0%) 630 (51.8%) 548 (50.7%) 511 (49.8%) EQ-VAS, mean±SD82.3 ± 14.2 78.6 ± 19.0 82.3 ± 14.5 80.5 ± 14.886.0 ± 11.4 83.1 ± 11.9 83.0 ± 10.0 84.9 ± 11.2

14. Country-specific functions

15. Regional functions

16. Global function

18. The findings  The differences were larger in the four eastern countries than those in the four western countries  A global generic transforming function was associated with large increase in prediction errors  A generic function for western countries may work

19. Discussion  DCE could be used as the sole technique in western countries where using TTO is not feasible  Provincial value set could be derived using the national transforming function applied to provincial DCE data  Trade-off between prediction precision for health state utilities and amount of research resources to spend must be made