Faculty of Business and Economics, Macquarie University Centre for the Health Economy, Macquarie University, 3 Innovation Road, Sydney, NSW, 2109, Australia.
Department of Economics and Related Studies, University of York, Heslington Road, York, YO10 5DD, UK.
Qual Life Res. 2019 Sep;28(9):2429-2441. doi: 10.1007/s11136-019-02220-x. Epub 2019 Jun 1.
Non-preference-based measures cannot be used to directly obtain utilities but can be converted to preference-based measures through mapping. The only mapping algorithm for estimating Child Health Utility-9D (CHU9D) utilities from Strengths and Difficulties Questionnaire (SDQ) responses has limitations. This study aimed to develop a more accurate algorithm.
We used a large sample of children (n = 6898), with negligible missing data, from the Longitudinal Study of Australian Children. Exploratory factor analysis (EFA) and Spearman's rank correlation coefficients were used to assess conceptual overlap between SDQ and CHU9D. Direct mapping (involving seven regression methods) and response mapping (involving one regression method) approaches were considered. The final model was selected by ranking the performance of each method by averaging the following across tenfold cross-validation iterations: mean absolute error (MAE), mean squared error (MSE), and MAE and MSE for two subsamples where predicted utility values were < 0.50 (poor health) or > 0.90 (healthy). External validation was conducted using data from the Child and Adolescent Mental Health Services study.
SDQ and CHU9D were moderately correlated (ρ = - 0.52, p < 0.001). EFA demonstrated that all CHU9D domains were associated with four SDQ subscales. The best-performing model was the Generalized Linear Model with SDQ items and gender as predictors (full sample MAE: 0.1149; MSE: 0.0227). The new algorithm performed well in the external validation.
The proposed mapping algorithm can produce robust estimates of CHU9D utilities from SDQ data for economic evaluations. Further research is warranted to assess the applicability of the algorithm among children with severe health problems.
非偏好测量不能直接获得效用，但可以通过映射转换为偏好测量。估计儿童健康效用 9 维度（CHU9D）效用的唯一映射算法具有局限性。本研究旨在开发一种更准确的算法。
我们使用来自澳大利亚儿童纵向研究的大量儿童样本（n=6898），数据缺失率可忽略不计。探索性因子分析（EFA）和斯皮尔曼等级相关系数用于评估 SDQ 和 CHU9D 之间的概念重叠。考虑了直接映射（涉及七种回归方法）和响应映射（涉及一种回归方法）方法。通过在十折交叉验证迭代中平均以下十个方面的性能来选择最终模型：平均绝对误差（MAE）、均方误差（MSE）和预测效用值小于 0.50（健康状况差）或大于 0.90（健康）的两个样本的 MAE 和 MSE。使用儿童和青少年心理健康服务研究的数据进行外部验证。
SDQ 和 CHU9D 中度相关（ρ=−0.52，p<0.001）。EFA 表明，CHU9D 的所有领域都与 SDQ 的四个分量相关。表现最佳的模型是包含 SDQ 项目和性别作为预测因子的广义线性模型（全样本 MAE：0.1149；MSE：0.0227）。新算法在外部验证中表现良好。
该映射算法可从 SDQ 数据中为经济评估生成 CHU9D 效用的稳健估计。需要进一步研究来评估该算法在患有严重健康问题的儿童中的适用性。
