DEVELOPING AN EQ-5D-5L VALUE SET FOR QATAR
Author(s)
Anas Hamad, MSc, PhD1, Dina Abushanab, PhD2, Daoud Al-Badriyeh, PhD3, Bram Roudijk, PhD4, Samar Farid, PhD5, Caline Saade, PharmD6, Fatima Al Sayah, PhD6.
1Drug Supply Department, Hamad Medical Corporation, Doha, Qatar, 2Office of Vice President for Medical and Health Sciences, QU Health, Qatar University, Doha, Qatar, 3College of Pharmacy, QU Health, Qatar University, Doha, Qatar, 4EuroQol Research Foundation, Rotterdam, Netherlands, 5Cairo University, Cairo, Egypt, 6Center of Clinical, Health Economics and Outcomes Research, Dubai, United Arab Emirates.
1Drug Supply Department, Hamad Medical Corporation, Doha, Qatar, 2Office of Vice President for Medical and Health Sciences, QU Health, Qatar University, Doha, Qatar, 3College of Pharmacy, QU Health, Qatar University, Doha, Qatar, 4EuroQol Research Foundation, Rotterdam, Netherlands, 5Cairo University, Cairo, Egypt, 6Center of Clinical, Health Economics and Outcomes Research, Dubai, United Arab Emirates.
OBJECTIVES: This study aims to develop the EQ-5D-5L value set for Qatar based on preferences elicited from the general adult population.
METHODS: The study follows the EuroQol EQ-5D-5L valuation protocol and targets a sample of 1,000 adults residing in Qatar. Participants are recruited using an age and sex-based quota sampling strategy across the country’s eight municipalities. Interviews are conducted either face-to-face or online, in Arabic or English, using the EuroQol Valuation Technology (EQ-VT) platform. Preference elicitation combines composite time trade-off (cTTO) and discrete choice experiment with duration (DCEd) methods. Each respondent values 10 EQ-5D-5L health states using cTTO and completes 15 split-triplet DCE tasks involving health states of varying durations. Data collection is paused at 20%, 40%, and 60% of the target sample to assess accumulated DCEd data and update the experimental DCE design.
RESULTS: As of 26 June 2026, 444 participants have completed the interviews, with a mean (SD) age of 31.8 (10.9) years; 45.3% are female and 3.4% are Qatari nationals. Several modelling approaches are being evaluated, including random-intercept and random-intercept Tobit models for cTTO data, with and without adjustments for heteroskedasticity; mixed logit models for DCEd data based on choice pairs with equal duration; and hybrid models combining cTTO and DCEd data through a Tobit link while accounting for heteroskedasticity in cTTO responses. Interim results indicate that the relative importance of EQ-5D-5L dimensions was Pain/Discomfort > Anxiety/Depression > Mobility > Self-Care > Usual Activities, although the differences among PD, AD, and MO were modest. All estimated coefficients were logically ordered across severity levels, demonstrating good model consistency. The estimated value for the worst health state based on CTTO models (55555) was −0.973.
CONCLUSIONS: The resulting EQ-5D-5L value set will provide Qatar-specific preference weights to support the use of EQ-5D-5L in health technology assessment, population health measurement, and health system performance assessment.
METHODS: The study follows the EuroQol EQ-5D-5L valuation protocol and targets a sample of 1,000 adults residing in Qatar. Participants are recruited using an age and sex-based quota sampling strategy across the country’s eight municipalities. Interviews are conducted either face-to-face or online, in Arabic or English, using the EuroQol Valuation Technology (EQ-VT) platform. Preference elicitation combines composite time trade-off (cTTO) and discrete choice experiment with duration (DCEd) methods. Each respondent values 10 EQ-5D-5L health states using cTTO and completes 15 split-triplet DCE tasks involving health states of varying durations. Data collection is paused at 20%, 40%, and 60% of the target sample to assess accumulated DCEd data and update the experimental DCE design.
RESULTS: As of 26 June 2026, 444 participants have completed the interviews, with a mean (SD) age of 31.8 (10.9) years; 45.3% are female and 3.4% are Qatari nationals. Several modelling approaches are being evaluated, including random-intercept and random-intercept Tobit models for cTTO data, with and without adjustments for heteroskedasticity; mixed logit models for DCEd data based on choice pairs with equal duration; and hybrid models combining cTTO and DCEd data through a Tobit link while accounting for heteroskedasticity in cTTO responses. Interim results indicate that the relative importance of EQ-5D-5L dimensions was Pain/Discomfort > Anxiety/Depression > Mobility > Self-Care > Usual Activities, although the differences among PD, AD, and MO were modest. All estimated coefficients were logically ordered across severity levels, demonstrating good model consistency. The estimated value for the worst health state based on CTTO models (55555) was −0.973.
CONCLUSIONS: The resulting EQ-5D-5L value set will provide Qatar-specific preference weights to support the use of EQ-5D-5L in health technology assessment, population health measurement, and health system performance assessment.
Conference/Value in Health Info
2026-11, ISPOR Europe 2026, Vienna, Austria
Value in Health, Volume 29, Issue 12S
Code
PCR97
Topic
Methodological & Statistical Research, Patient-Centered Research
Topic Subcategory
Health State Utilities, Patient-reported Outcomes & Quality of Life Outcomes
Disease
No Additional Disease & Conditions/Specialized Treatment Areas