Quality of Life Research ( IF 3.3 ) Pub Date : 2023-02-23 , DOI: 10.1007/s11136-023-03359-4 Clare B Kelly 1, 2 , Marina Soley-Bori 1, 3, 4 , Raghu Lingam 5 , Julia Forman 1, 2 , Lizzie Cecil 1, 2 , James Newham 6 , Ingrid Wolfe 1, 2 , Julia Fox-Rushby 3, 7
Purpose
The Child Health Utility-9 Dimensions (CHU9D) is a patient-reported outcome measure to generate Quality-Adjusted Life Years (QALYs), recommended for economic evaluations of interventions to inform funding decisions. When the CHU9D is not available, mapping algorithms offer an opportunity to convert other paediatric instruments, such as the Paediatric Quality of Life Inventory™ (PedsQL), onto the CHU9D scores.
This study aims to validate current PedsQL to CHU9D mappings in a sample of children and young people of a wide age range (0 to 16 years of age) and with chronic conditions. New algorithms with improved predictive accuracy are also developed.
Methods
Data from the Children and Young People’s Health Partnership (CYPHP) were used (N = 1735). Four regression models were estimated: ordinal least squared, generalized linear model, beta-binomial and censored least absolute deviations. Standard goodness of fit measures were used for validation and to assess new algorithms.
Results
While previous algorithms perform well, performance can be enhanced. OLS was the best estimation method for the final equations at the total, dimension and item PedsQL scores levels. The CYPHP mapping algorithms include age as an important predictor and more non-linear terms compared with previous work.
Conclusion
The new CYPHP mappings are particularly relevant for samples with children and young people with chronic conditions living in deprived and urban settings. Further validation in an external sample is required.
Trial registration number NCT03461848; pre-results.
中文翻译:
将 PedsQL™ 分数映射到多种族和贫困都市人口中患有慢性病的儿童的 CHU9D 效用权重
目的
儿童健康实用程序 9 维度 (CHU9D) 是一种患者报告的结果衡量标准,用于生成质量调整生命年 (QALY),建议用于干预措施的经济评估,为资助决策提供信息。当 CHU9D 不可用时,映射算法提供了将其他儿科仪器(例如儿科生活质量量表™ (PedsQL))转换为 CHU9D 分数的机会。
本研究旨在在广泛年龄范围(0 至 16 岁)和患有慢性疾病的儿童和年轻人样本中验证当前的 PedsQL 到 CHU9D 映射。还开发了具有更高预测准确性的新算法。
方法
使用来自儿童和青少年健康合作伙伴关系 (CYPHP) 的数据 ( N = 1735)。估计了四种回归模型:序数最小二乘、广义线性模型、β-二项式和删失最小绝对偏差。标准拟合优度测量用于验证和评估新算法。
结果
虽然以前的算法表现良好,但性能还有待提高。OLS 是最终方程在总分、维度和项目 PedSQL 分数级别的最佳估计方法。与之前的工作相比,CYPHP 映射算法包括年龄作为重要的预测因子和更多的非线性项。
结论
新的 CYPHP 映射对于生活在贫困和城市环境中患有慢性病的儿童和年轻人的样本特别相关。需要在外部样本中进一步验证。
试用注册号NCT03461848;预结果。