Abstract

Objectives: The SF-6D is a preference-based measure of health developed to generate utility values from the SF-36. The aim of this pilot study was to examine the feasibility and acceptability of using the standard gamble (SG) technique to generate preference-based values for the Arabic version of SF-6D in a Lebanese population. Methods: The SF-6D was translated into Arabic using forward and backward translations. Forty-nine states defined by the SF-6D were selected using an orthogonal design and grouped into seven sets. A gender-occupation stratified sample of 126 Lebanese adults from the American University of Beirut were recruited to value seven states and the pits using SG. The sample size is appropriate for a pilot study, but smaller than the sample required for a full valuation study. Both interviewers and interviewees reported their understanding and effort levels in the SG tasks. Mean and individual level multivariate regression models were fitted to estimate preference weights for all SF-6D states. The models were compared with those estimated in the UK. Results: Interviewers reported few problems in completing SG tasks (0.8% with a lot of problems) and good respondent understanding (5.6% with little effort and concentration), and 25% of respondents reported the SG task was difficult. A total of 992 SG valuations were useable for econometric modeling. There was no significant change in the test–retest values from 21 subjects. The mean absolute errors in the mean and individual level models were 0.036 and 0.050, respectively, both of which were lower than the UK results. The random effects model adequately predicts the SG values, with the worst state having a value of 0.322 compared to 0.271 in the UK. Conclusion: This pilot confirmed that it was feasible and acceptable to generate preference values with the SG method for the Arabic SF-6D in a Lebanese population. However, further work is needed to extend this to a more representative population, and to explore why no utility values below zero were observed.

Highlights

  • The fast growth medical technologies and treatments increasingly requires cost-utility analyses (CUA) and cost-effectiveness analyses (CEA) to decide on the optimal treatment for every health condition [1]

  • One hundred and twenty-six participants were recruited from American University of Beirut (AUB) and belonged to either one of the three following categories: (1) Faculty, (2) staff, and (3) students

  • The discrepancy in educational level and the high total household income are due to our sample population being recruited from an educational institution

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Summary

Introduction

The fast growth medical technologies and treatments increasingly requires cost-utility analyses (CUA) and cost-effectiveness analyses (CEA) to decide on the optimal treatment for every health condition [1]. Agencies that advise on reimbursement such as the National Institute for Health and. Care Excellence (NICE) commonly require a health-related quality of life (HRQoL) outcomes using. Res. Public Health 2020, 17, 1037; doi:10.3390/ijerph17031037 www.mdpi.com/journal/ijerph

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