Zhihao Yang1, Nan Luo2, Mark Oppe3, Gouke Bonsel4, Jan Busschbach5, Elly Stolk6. 1. College of Pharmacy, Jinan University, Guangzhou, China; Medical Psychology and Psychotherapy, Erasmus Medical Center, Rotterdam, the Netherlands. Electronic address: zhihao_yang_cn@126.com. 2. Saw Swee Hock School of Public Health, National University of Singapore, Singapore. 3. Axentiva Solutions, Tacoronte, Spain. 4. Department of Public Health, Erasmus Medical Center, Rotterdam, the Netherlands; EuroQol Research Foundation, Rotterdam, the Netherlands. 5. Medical Psychology and Psychotherapy, Erasmus Medical Center, Rotterdam, the Netherlands. 6. EuroQol Research Foundation, Rotterdam, the Netherlands.
Abstract
BACKGROUND: To construct an EQ-5D-5L value set, the EuroQol Group developed a standard protocol named EuroQol Valuation Technology (EQ-VT), prescribing the valuation of 86 health states utilizing the composite time trade-off (cTTO) approach, and subsequently modeled the observed values to yield values for all 3125 states. OBJECTIVE: A recent study demonstrated that a 25-state orthogonal design could provide as accurate predictions as the EQ-VT design applying visual analogue scale data. We aimed to test that design using time trade-off (TTO) data. METHOD: We collected TTO values utilizing EQ-VT, orthogonal, and D-efficient designs. The EQ-VT design included 86 health states distributed over 3 blocks of 30 states with some duplicates. The orthogonal and D-efficient designs each comprised 1 block of 30 states. A total of 525 university students were asked to value a random block of health states using EQ-PVT (a PowerPoint replica of EQ-VT software), which generated 100 observations per health state in all 3 designs. We modeled data by design and compared the root mean square error (RMSE) between observed and predicted values within and across the designs. RESULTS: The EQ-VT design had the lowest RMSE of 0.052; the RMSEs for the orthogonal and the D-efficient designs were 0.066 and 0.063, respectively. RMSE results between designs differed for more severe health states. Some coefficients differed between designs. CONCLUSION: Smaller designs did not lead to significant increases in prediction errors when modeling TTO data (measuring 0.01 on a utility scale). Resource-constrained countries may use small designs for valuation studies, especially when other types of preference data, such as those from discrete choice experiments, are collected and modeled jointly.
BACKGROUND: To construct an EQ-5D-5L value set, the EuroQol Group developed a standard protocol named EuroQol Valuation Technology (EQ-VT), prescribing the valuation of 86 health states utilizing the composite time trade-off (cTTO) approach, and subsequently modeled the observed values to yield values for all 3125 states. OBJECTIVE: A recent study demonstrated that a 25-state orthogonal design could provide as accurate predictions as the EQ-VT design applying visual analogue scale data. We aimed to test that design using time trade-off (TTO) data. METHOD: We collected TTO values utilizing EQ-VT, orthogonal, and D-efficient designs. The EQ-VT design included 86 health states distributed over 3 blocks of 30 states with some duplicates. The orthogonal and D-efficient designs each comprised 1 block of 30 states. A total of 525 university students were asked to value a random block of health states using EQ-PVT (a PowerPoint replica of EQ-VT software), which generated 100 observations per health state in all 3 designs. We modeled data by design and compared the root mean square error (RMSE) between observed and predicted values within and across the designs. RESULTS: The EQ-VT design had the lowest RMSE of 0.052; the RMSEs for the orthogonal and the D-efficient designs were 0.066 and 0.063, respectively. RMSE results between designs differed for more severe health states. Some coefficients differed between designs. CONCLUSION: Smaller designs did not lead to significant increases in prediction errors when modeling TTO data (measuring 0.01 on a utility scale). Resource-constrained countries may use small designs for valuation studies, especially when other types of preference data, such as those from discrete choice experiments, are collected and modeled jointly.
Authors: Zhihao Yang; Fredrick Dermawan Purba; Asrul Akmal Shafie; Ataru Igarashi; Eliza Lai-Yi Wong; Hilton Lam; Hoang Van Minh; Hsiang-Wen Lin; Jeonghoon Ahn; Juntana Pattanaphesaj; Min-Woo Jo; Vu Quynh Mai; Jan Busschbach; Nan Luo; Jie Jiang Journal: Qual Life Res Date: 2022-02-18 Impact factor: 3.440