Meacock Rachel, Harrison Mark, McElhone Kathleen, Abbott Janice, Haque Sahena, Bruce Ian, Teh Lee-Suan
The University of Manchester, 4.311 Jean McFarlane Building, Oxford Road, Manchester, M15 4QE, UK,
Qual Life Res. 2015 Jul;24(7):1749-58. doi: 10.1007/s11136-014-0892-4. Epub 2014 Dec 16.
To derive a mapping algorithm to predict SF-6D utility scores from the non-preference-based LupusQoL and test the performance of the developed algorithm on a separate independent validation data set.
LupusQoL and SF-6D data were collected from 320 patients with systemic lupus erythematosus (SLE) attending routine rheumatology outpatient appointments at seven centres in the UK. Ordinary least squares (OLS) regression was used to estimate models of increasing complexity in order to predict individuals' SF-6D utility scores from their responses to the LupusQoL questionnaire. Model performance was judged on predictive ability through the size and pattern of prediction errors generated. The performance of the selected model was externally validated on an independent data set containing 113 female SLE patients who had again completed both the LupusQoL and SF-36 questionnaires.
Four of the eight LupusQoL domains (physical health, pain, emotional health, and fatigue) were selected as dependent variables in the final model. Overall model fit was good, with R(2) 0.7219, MAE 0.0557, and RMSE 0.0706 when applied to the estimation data set, and R(2) 0.7431, MAE 0.0528, and RMSE 0.0663 when applied to the validation sample.
This study provides a method by which health state utility values can be estimated from patient responses to the non-preference-based LupusQoL, generalisable beyond the data set upon which it was estimated. Despite concerns over the use of OLS to develop mapping algorithms, we find this method to be suitable in this case due to the normality of the SF-6D data.
推导一种映射算法，用于从基于非偏好的狼疮生活质量量表（LupusQoL）预测SF-6D效用得分，并在单独的独立验证数据集上测试所开发算法的性能。
从英国七个中心参加常规风湿病门诊预约的320例系统性红斑狼疮（SLE）患者中收集LupusQoL和SF-6D数据。使用普通最小二乘法（OLS）回归来估计复杂度不断增加的模型，以便根据患者对LupusQoL问卷的回答来预测个体的SF-6D效用得分。通过所产生预测误差的大小和模式来判断模型的预测能力。所选模型的性能在一个独立数据集上进行外部验证，该数据集包含113例再次完成LupusQoL和SF-36问卷的女性SLE患者。
最终模型选择了八个LupusQoL领域中的四个（身体健康、疼痛、情绪健康和疲劳）作为因变量。应用于估计数据集时，总体模型拟合良好，R²为0.7219，平均绝对误差（MAE）为0.0557, 均方根误差（RMSE）为0.0706；应用于验证样本时，R²为0.7431，MAE为0.0528，RMSE为0.0663。
本研究提供了一种方法，通过该方法可以根据患者对基于非偏好的LupusQoL的回答来估计健康状态效用值，该方法可推广到其估计所依据的数据集之外。尽管有人担心使用OLS来开发映射算法，但由于SF-6D数据的正态性，我们发现在这种情况下该方法是合适的。
