Kim Hye-Lin, Kim Dam, Jang Eun Jin, Lee Min-Young, Song Hyun Jin, Park Sun-Young, Cho Soo-Kyung, Sung Yoon-Kyoung, Choi Chan-Bum, Won Soyoung, Bang So-Young, Cha Hoon-Suk, Choe Jung-Yoon, Chung Won Tae, Hong Seung-Jae, Jun Jae-Bum, Kim Jinseok, Kim Seong-Kyu, Kim Tae-Hwan, Kim Tae-Jong, Koh Eunmi, Lee Hwajeong, Lee Hye-Soon, Lee Jisoo, Lee Shin-Seok, Lee Sung Won, Park Sung-Hoon, Shim Seung-Cheol, Yoo Dae-Hyun, Yoon Bo Young, Bae Sang-Cheol, Lee Eui-Kyung
School of Pharmacy, Sungkyunkwan University, 2066 Seobu-ro, Jangan-Gu, Suwon, Gyeonggi-do, 440-746, Republic of Korea.
College of Pharmacy, Sahmyook University, Seoul, Republic of Korea.
Rheumatol Int. 2016 Apr;36(4):505-13. doi: 10.1007/s00296-016-3427-1. Epub 2016 Feb 6.
The aim of this study was to estimate the mapping model for EuroQol-5D (EQ-5D) utility values using the health assessment questionnaire disability index (HAQ-DI), pain visual analog scale (VAS), and disease activity score in 28 joints (DAS28) in a large, nationwide cohort of rheumatoid arthritis (RA) patients in Korea. The KORean Observational study Network for Arthritis (KORONA) registry data on 3557 patients with RA were used. Data were randomly divided into a modeling set (80 % of the data) and a validation set (20 % of the data). The ordinary least squares (OLS), Tobit, and two-part model methods were employed to construct a model to map to the EQ-5D index. Using a combination of HAQ-DI, pain VAS, and DAS28, four model versions were examined. To evaluate the predictive accuracy of the models, the root-mean-square error (RMSE) and mean absolute error (MAE) were calculated using the validation dataset. A model that included HAQ-DI, pain VAS, and DAS28 produced the highest adjusted R (2) as well as the lowest Akaike information criterion, RMSE, and MAE, regardless of the statistical methods used in modeling set. The mapping equation of the OLS method is given as EQ-5D = 0.95-0.21 × HAQ-DI-0.24 × pain VAS/100-0.01 × DAS28 (adjusted R (2) = 57.6 %, RMSE = 0.1654 and MAE = 0.1222). Also in the validation set, the RMSE and MAE were shown to be the smallest. The model with HAQ-DI, pain VAS, and DAS28 showed the best performance, and this mapping model enabled the estimation of an EQ-5D value for RA patients in whom utility values have not been measured.
本研究旨在利用健康评估问卷残疾指数(HAQ-DI)、疼痛视觉模拟量表(VAS)和28个关节疾病活动评分(DAS28),为韩国一个大型全国性类风湿关节炎(RA)患者队列估算欧洲五维健康量表(EQ-5D)效用值的映射模型。使用了韩国关节炎观察研究网络(KORONA)中3557例RA患者的登记数据。数据被随机分为建模集(数据的80%)和验证集(数据的20%)。采用普通最小二乘法(OLS)、托比特模型和两部分模型方法构建映射到EQ-5D指数的模型。使用HAQ-DI、疼痛VAS和DAS28的组合,检验了四个模型版本。为评估模型的预测准确性,使用验证数据集计算均方根误差(RMSE)和平均绝对误差(MAE)。无论在建模集中使用何种统计方法,包含HAQ-DI、疼痛VAS和DAS28的模型产生的调整R²最高,同时赤池信息准则、RMSE和MAE最低。OLS方法的映射方程为EQ-5D = 0.95 - 0.21×HAQ-DI - 0.24×疼痛VAS/100 - 0.01×DAS28(调整R² = 57.6%,RMSE = 0.1654,MAE = 0.1222)。在验证集中,RMSE和MAE也显示为最小。包含HAQ-DI、疼痛VAS和DAS28的模型表现最佳,该映射模型能够估算尚未测量效用值的RA患者的EQ-5D值。