Table_2_Obtaining SF-6D utilities from FACT-H&N in thyroid carcinoma patients: development and results from a mapping study.xls

ObjectiveThere is limited evidence for mapping clinical tools to preference-based generic tools in the Chinese thyroid cancer patient population. The current study aims to map the FACT-H&N (Functional Assessment of Cancer Therapy-Head and Neck Cancer) to the SF-6D (Short Form Six-Dimension), which will inform future cost-utility analyses related to thyroid cancer treatment.
MethodsA total of 1050 participants who completed the FACT-H&N and SF-6D questionnaires were included in the analysis. Four methods of direct and indirect mapping were estimated: OLS regression, Tobit regression, ordered probit regression, and beta mixture regression. We evaluated the predictive performance in terms of root mean square error (RMSE), mean absolute error (MAE), concordance correlation coefficient (CCC), Akaike information criterion (AIC) and Bayesian information criterion (BIC) and the correlation between the observed and predicted SF-6D scores.
ResultsThe mean value of SF-6D was 0.690 (SD = 0.128). The RMSE values for the fivefold cross-validation as well as the 30% random sample validation for multiple models in this study were 0.0833-0.0909, MAE values were 0.0676-0.0782, and CCC values were 0.6940-0.7161. SF-6D utility scores were best predicted by a regression model consisting of the total score of each dimension of the FACT-H&N, the square of the total score of each dimension, and covariates including age and gender. We proposed to use direct mapping (OLS regression) and indirect mapping (ordered probit regression) to establish a mapping model of FACT-H&N to SF-6D. The mean SF-6D and cumulative distribution functions simulated from the recommended mapping algorithm generally matched the observed ones.
ConclusionsIn the absence of preference-based quality of life tools, obtaining the health status utility of thyroid cancer patients from directly mapped OLS regression and indirectly mapped ordered probit regression is an effective alternative.

研究目的：目前针对中国甲状腺癌患者群体，将临床专用量表映射至偏好效用型普适性健康量表的相关研究证据较为匮乏。本研究旨在将头颈部癌症治疗功能评价量表（Functional Assessment of Cancer Therapy-Head and Neck Cancer, FACT-H&N）映射至健康调查简表六维度量表（Short Form Six-Dimension, SF-6D），以期为后续甲状腺癌治疗相关的成本效用分析提供参考依据。
研究方法：本研究共纳入1050名完成FACT-H&N与SF-6D问卷的受试者进行分析。共构建四种直接映射与间接映射模型：普通最小二乘（OLS）回归、Tobit回归、有序Probit回归以及β混合回归。本研究以均方根误差（RMSE）、平均绝对误差（MAE）、一致性相关系数（CCC）、赤池信息准则（AIC）与贝叶斯信息准则（BIC）作为模型预测性能的评价指标，并同时分析观测SF-6D得分与预测SF-6D得分之间的相关性。
研究结果：本研究中SF-6D效用得分的平均值为0.690（标准差SD=0.128）。五种交叉验证及30%随机抽样验证下，多模型的均方根误差区间为0.0833~0.0909，平均绝对误差区间为0.0676~0.0782，一致性相关系数区间为0.6940~0.7161。以FACT-H&N各维度总分、各维度总分的平方项，以及年龄、性别作为协变量构建的回归模型，对SF-6D效用得分的预测效果最优。本研究推荐采用直接映射法（OLS回归）与间接映射法（有序Probit回归）构建FACT-H&N至SF-6D的映射模型。基于推荐映射算法模拟得到的平均SF-6D得分及累积分布函数，与观测值整体匹配度良好。
结论：在缺乏偏好效用型生活质量量表的情况下，通过直接映射的OLS回归与间接映射的有序Probit回归获取甲状腺癌患者的健康状态效用值，是一种切实有效的替代方案。