Supplementary figures: Mapping the EORTC QLQ-C30 onto the EQ-5D-5L index for patients with paroxysmal nocturnal hemoglobinuria in France

These are peer-reviewed supplementary materials for the article 'Mapping the EORTC QLQ-C30 onto the EQ-5D-5L index for patients with paroxysmal nocturnal hemoglobinuria in France' published in the Journal of Comparative Effectiveness Research.Supplementary Figure 1: Markov model diagram.Supplementary Figure 2:. Histogram of EORTC QLQ-C30 subscale scores.Supplementary Figure 3: Histogram of EQ-5D-5L domain scores.Supplementary Figure 4: Model performance.Supplementary Figure 5: Estimated utilities by visit and treatment.Supplementary Table 1: Types of regression models.Supplementary Table 2: Tobit model results.Supplementary Table 3: Health-state utilities by modelAim: To map patient-level data collected on the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC) QLQ-C30 to EQ-5D-5L data for estimating health-state utilities in patients with paroxysmal nocturnal hemoglobinuria (PNH). Materials & methods: European cross-sectional PNH patient survey data populated regression models mapping EORTC QLQ-C30 domains (covariates: sex and baseline age) to utilities calculated with the EQ-5D-5L French value set. A genetic algorithm allowed selection of the best-fitting between a set of models with and without interaction terms. We validated the selected algorithm using EQ-5D-5L utilities converted from EORTC QLQ-C30 data collected in the PEGASUS phase III, randomized controlled trial of pegcetacoplan versus eculizumab in adults with PNH. Results: Selected through the genetic algorithm, the ordinary least squares model without interactions provided highly stable results across study visits (mean [±SD] utilities 0.58 [±0.42] to 0.89 [±0.10]), and showed the best predictive validity. Conclusion: The new PNH EQ-5D-5L direct mapping developed using a genetic algorithm enabled calculation of reliable health-state utility data required for cost–utility analysis in health technology assessments supporting treatments of PNH.

本数据集为发表于《比较疗效研究杂志》的论文《将欧洲癌症研究与治疗组织（EORTC）QLQ-C30量表映射至EQ-5D-5L指数，以评估法国阵发性睡眠性血红蛋白尿症患者的健康状况》的同行评审补充材料。补充图1：马尔可夫模型图；补充图2：EORTC QLQ-C30子量表得分直方图；补充图3：EQ-5D-5L领域得分直方图；补充图4：模型性能；补充图5：按访问和治疗估计的效用；补充表1：回归模型类型；补充表2：Tobit模型结果；补充表3：按模型的健康状态效用。研究目标：旨在将欧洲癌症研究与治疗组织（EORTC）QLQ-C30问卷在患者层面的数据映射至EQ-5D-5L数据，以估算阵发性睡眠性血红蛋白尿症（PNH）患者的健康状况效用。研究方法：利用欧洲横断面PNH患者调查数据，构建回归模型，将EORTC QLQ-C30领域（协变量：性别和基线年龄）映射至使用EQ-5D-5L法国值集计算出的效用。遗传算法允许在包含和不包含交互项的模型集中选择最佳拟合模型。通过将来自PEGASUS III期随机对照试验（pegcetacoplan与eculizumab在PNH成人患者中的比较）的EORTC QLQ-C30数据转换为EQ-5D-5L效用，验证了所选算法。结果：通过遗传算法选出的普通最小二乘模型（无交互项）在研究访问中提供了高度稳定的成果（平均[±SD]效用为0.58[±0.42]至0.89[±0.10]），并显示出最佳的预测效度。结论：利用遗传算法开发的新PNH EQ-5D-5L直接映射技术，能够计算可靠的健康状态效用数据，这对于健康技术评估中的成本-效用分析，以及支持PNH治疗的决策具有重要意义。