Institute for Medical Technology Assessment, Erasmus University Rotterdam, J-building—Campus Woudestein, PO Box 1738, 3000 DR Rotterdam, The Netherlands.
Qual Life Res. 2013 Jun;22(5):1045-54. doi: 10.1007/s11136-012-0220-9. Epub 2012 Jun 29.
Although cancer-specific Health-related Quality-of-Life measures are commonly included in randomized clinical trials or other prospective non-randomized clinical studies, it is rare that preference-based instruments are used, which allow the calculation of a Utility weight suitable for estimating Quality-adjusted Life-Years gained.
To test the external validity of a previously published mapping algorithm to transform the EORTC QLQ-C30 questionnaire responses into EQ-5D-derived utilities by predicting EQ-5D utilities from QLQ-C30 scores.
Comparative retrospective data analysis of four multicentre, prospective clinical trials in Breast, Multiple Myeloma, Non-Hodgkin Lymphoma and Non-Small-Cell Lung cancer patients with, respectively, 219, 172, 132 and 172 patients. Regression analysis of individual pairs of EQ-5D and QLQ-C30 scores.
Although the internal predictive power of a previously published mapping equation was high, its external validity when tested on a set of unrelated external data sets in other cancers proved to underestimate both the mean and variance of the mapped EQ-5D utilities. Furthermore, it appears that the relationship between QLQ-C30 scores and EQ-5D values is not stable across the different data sets.
Validation of the proposed algorithm in other external clinical data sets should be encouraged as well as the application of other more complex mapping methods to enhance accuracy of mapping. In the meanwhile, direct mapping from QLQ-C30 profiles to EQ-5D utilities using published algorithms should be performed with reservations.
虽然癌症特异性健康相关生活质量测量通常包含在随机临床试验或其他前瞻性非随机临床研究中,但很少使用偏好测量工具,这使得计算适合估计获得的质量调整生命年的效用权重成为可能。
通过从 QLQ-C30 评分预测 EQ-5D 效用,检验先前发表的映射算法将 EORTC QLQ-C30 问卷反应转换为 EQ-5D 衍生效用的外部有效性。
对来自乳腺癌、多发性骨髓瘤、非霍奇金淋巴瘤和非小细胞肺癌的四项多中心前瞻性临床试验的回顾性比较数据进行分析,分别有 219、172、132 和 172 名患者。对 EQ-5D 和 QLQ-C30 评分的个体对进行回归分析。
虽然先前发表的映射方程的内部预测能力较高,但在其他癌症的一组不相关的外部数据集上进行测试时,其外部有效性证明低估了映射 EQ-5D 效用的平均值和方差。此外,QLQ-C30 评分与 EQ-5D 值之间的关系似乎在不同的数据集之间并不稳定。
应该鼓励在其他外部临床数据集上验证所提出的算法,并应用其他更复杂的映射方法来提高映射的准确性。同时,使用已发表的算法直接从 QLQ-C30 概况映射到 EQ-5D 效用应该持保留态度。