PDF(Pubmed)
Abstract：
There 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.
A 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.
The 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.
In 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.
A 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.
The 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.
In 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.
摘要：
■在中国甲状腺癌患者人群中，将临床工具映射到基于偏好的通用工具的证据有限。当前的研究旨在将FACT-H＆N（癌症治疗-头颈部癌症的功能评估）映射到SF-6D（简短的六维），这将为未来与甲状腺癌治疗相关的成本-效用分析提供信息。
■共有1050名完成FACT-H&N和SF-6D问卷的参与者被纳入分析。估计了直接和间接映射的四种方法：OLS回归，Tobit回归，有序概率回归，和β混合回归。我们根据均方根误差(RMSE)评估了预测性能，平均绝对误差(MAE)，一致性相关系数(CCC)，Akaike信息准则（AIC）和贝叶斯信息准则（BIC）以及观察到的和预测的SF-6D得分之间的相关性。
■SF-6D的平均值为0.690(SD=0.128)。本研究中多重模型的5倍交叉验证以及30%随机样本验证的RMSE值为0.0833-0.0909，MAE值为0.0676-0.0782，CCC值为0.6940-0.7161。SF-6D效用分数最好通过由FACT-H&N各维度总分组成的回归模型来预测，每个维度总分的平方，和协变量包括年龄和性别。我们建议使用直接映射（OLS回归）和间接映射（有序概率回归）来建立FACT-H＆N到SF-6D的映射模型。从推荐的映射算法模拟的平均SF-6D和累积分布函数通常与观察到的匹配。
■在缺乏基于偏好的生活质量工具的情况下，从直接映射的OLS回归和间接映射的有序probit回归中获得甲状腺癌患者的健康状况效用是一种有效的替代方法。
■共有1050名完成FACT-H&N和SF-6D问卷的参与者被纳入分析。估计了直接和间接映射的四种方法：OLS回归，Tobit回归，有序概率回归，和β混合回归。我们根据均方根误差(RMSE)评估了预测性能，平均绝对误差(MAE)，一致性相关系数(CCC)，Akaike信息准则（AIC）和贝叶斯信息准则（BIC）以及观察到的和预测的SF-6D得分之间的相关性。
■SF-6D的平均值为0.690(SD=0.128)。本研究中多重模型的5倍交叉验证以及30%随机样本验证的RMSE值为0.0833-0.0909，MAE值为0.0676-0.0782，CCC值为0.6940-0.7161。SF-6D效用分数最好通过由FACT-H&N各维度总分组成的回归模型来预测，每个维度总分的平方，和协变量包括年龄和性别。我们建议使用直接映射（OLS回归）和间接映射（有序概率回归）来建立FACT-H＆N到SF-6D的映射模型。从推荐的映射算法模拟的平均SF-6D和累积分布函数通常与观察到的匹配。
■在缺乏基于偏好的生活质量工具的情况下，从直接映射的OLS回归和间接映射的有序probit回归中获得甲状腺癌患者的健康状况效用是一种有效的替代方法。
