Ameri Hosein, Yousefi Mahmood, Yaseri Mehdi, Nahvijou Azin, Arab Mohammad, Akbari Sari Ali
Department of Health Management and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Iranian Center of Excellence in Health Management, School of Management and Medical Informatics, Health Economics Department, Tabriz University of Medical Sciences, Tabriz, Iran.
J Gastrointest Cancer. 2020 Mar;51(1):196-203. doi: 10.1007/s12029-019-00229-6.
Patient-level utility data are needed for cost-utility analysis; in oncology, however, the data are commonly gathered using disease-specific questionnaires that are often not appropriate. Present study aimed to derive an algorithm which can map the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-C30 (EORTC QLQ-C30) scales and the Colorectal Cancer-Specific Quality Of Life Questionnaire (QLQ-CR29) scales onto the EuroQoL 5-Dimension 5-Level (EQ-5D-5L) values in patients with colorectal cancer (CRC).
Using the Ordinary Least Square (OLS) model, a cross-sectional dataset of 252 patients with CRC were gathered from three academic centers of cancer treatment in Tehran in 2017. The predicted R (Pred R) and adjusted R (Adj R) are used to evaluate model goodness of fit. Additionally, mean absolute error (MAE), root mean square error (RMSE), Spearman's correlation coefficients (ρ), and intraclass correlation (ICC) are applied to assess predictive ability of models. The tenfold cross-validation procedure was applied for validation models.
According to the results of our study, the model C4 from EORTC QLQ-C30 was the best predictive model (Pred R = 66.57%, Adj R = 67.67%, RMSE = 0.10173, MAE = 0.07840). Also, the model R4 from QLQ-CR29 performed the best for EQ-5D-5L (Adj R2 = 48.42%, Pred R = 45.54%, MAE = 0.10051, RMSE = 0.12997).
The mapping algorithm successfully mapped the EORTC QLQ-C30 and QLQ-CR29 scales onto the EQ-5D-5L values; therefore, it enables policymakers to convert cancer-specific questionnaires scores to the preference-based scores.
成本效用分析需要患者层面的效用数据；然而，在肿瘤学领域，这些数据通常是通过疾病特异性问卷收集的，而这些问卷往往并不合适。本研究旨在推导一种算法，该算法可以将欧洲癌症研究与治疗组织生活质量问卷C30（EORTC QLQ-C30）量表和结直肠癌特异性生活质量问卷（QLQ-CR29）量表转换为结直肠癌（CRC）患者的欧洲五维健康量表（EQ-5D-5L）值。
采用普通最小二乘法（OLS）模型，于2017年从德黑兰的三个癌症治疗学术中心收集了252例CRC患者的横断面数据集。预测R（Pred R）和调整R（Adj R）用于评估模型的拟合优度。此外，平均绝对误差（MAE）、均方根误差（RMSE）、Spearman相关系数（ρ）和组内相关系数（ICC）用于评估模型的预测能力。采用十折交叉验证程序对模型进行验证。
根据我们的研究结果，EORTC QLQ-C30的C4模型是最佳预测模型（Pred R = 66.57%，Adj R = 67.67%，RMSE = 0.10173，MAE = 0.07840）。此外，QLQ-CR29的R4模型在EQ-5D-5L方面表现最佳（Adj R2 = 48.42%，Pred R = 45.54%，MAE = 0.10051，RMSE = 0.12997）。
该映射算法成功地将EORTC QLQ-C30和QLQ-CR29量表转换为EQ-5D-5L值；因此，它使政策制定者能够将癌症特异性问卷得分转换为基于偏好的得分。
