The Research Center of National Drug Policy & Ecosystem, China Pharmaceutical University, Nanjing, China.
Front Public Health. 2023 May 31;11:1166760. doi: 10.3389/fpubh.2023.1166760. eCollection 2023.
The study aims to develop a mapping algorithm from the Pediatric Quality of Life Inventory™ 4. 0 (Peds QL 4.0) onto Child Health Utility 9D (CHU-9D) based on the cross-sectional data of functional dyspepsia (FD) children and adolescents in China.
A sample of 2,152 patients with FD completed both the CHU-9D and Peds QL 4.0 instruments. A total of six regression models were used to develop the mapping algorithm, including ordinary least squares regression (OLS), the generalized linear regression model (GLM), MM-estimator model (MM), Tobit regression (Tobit) and Beta regression (Beta) for direct mapping, and multinomial logistic regression (MLOGIT) for response mapping. Peds QL 4.0 total score, Peds QL 4.0 dimension scores, Peds QL 4.0 item scores, gender, and age were used as independent variables according to the Spearman correlation coefficient. The ranking of indicators, including the mean absolute error (MAE), root mean squared error (RMSE), adjusted R, and consistent correlation coefficient (CCC), was used to assess the predictive ability of the models.
The Tobit model with selected Peds QL 4.0 item scores, gender and age as the independent variable predicted the most accurate. The best-performing models for other possible combinations of variables were also shown.
The mapping algorithm helps to transform Peds QL 4.0 data into health utility value. It is valuable for conducting health technology evaluations within clinical studies that have only collected Peds QL 4.0 data.
本研究旨在基于中国功能性消化不良（FD）儿童和青少年的横断面数据，开发一种将儿科生存质量量表 4.0（PedsQL4.0）映射到儿童健康效用 9 维度量表（CHU-9D）的映射算法。
共有 2152 名 FD 患者完成了 CHU-9D 和 PedsQL4.0 量表。共使用了六种回归模型来开发映射算法，包括普通最小二乘法回归（OLS）、广义线性回归模型（GLM）、MM 估计器模型（MM）、Tobit 回归（Tobit）和 Beta 回归（Beta）用于直接映射，以及多项逻辑回归（MLOGIT）用于响应映射。根据 Spearman 相关系数，将 PedsQL4.0 总分、PedsQL4.0 维度得分、PedsQL4.0 项目得分、性别和年龄作为自变量用于回归模型。使用指标的排序平均绝对误差（MAE）、均方根误差（RMSE）、调整 R 和一致性相关系数（CCC）来评估模型的预测能力。
以选择的 PedsQL4.0 项目得分、性别和年龄为自变量的 Tobit 模型预测最准确。还展示了其他可能变量组合的最佳表现模型。
映射算法有助于将 PedsQL4.0 数据转化为健康效用值。它对于仅收集了 PedsQL4.0 数据的临床研究中的健康技术评估具有价值。
