Deriving an Algorithm to Convert the Eight Mean SF-36 Dimension Scores into a Mean EQ-5D Preference-Based Score from Published Studies (Where Patient Level Data Are Not Available)
Deriving an Algorithm to Convert the Eight Mean SF-36 Dimension Scores into a Mean EQ-5D Preference-Based Score from Published Studies (Where Patient Level Data Are Not Available)
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DOI：
10.1111/j.1524-4733.2008.00352.x
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发表时间：
2008-12-01
期刊：
影响因子：
4.5
通讯作者：
Brazier, John
中科院分区：
文献类型：
--
作者：
Ara, Roberta;Brazier, John
目的：这项研究的目的是推导出一种方法来预测平均队列EQ-5D偏好为基础的指数得分使用公布的平均统计的8个维度的分数描述SF-36 health profile.Methods：普通最小二乘回归模型，来自使用患者水平的数据（n = 6350）收集在12个临床研究。使用标准技术比较模型的拟合优度，例如解释的方差、预测值的误差幅度和EQ-5D最小重要差异内的值比例。预测能力也进行了比较，从两个样本内的亚组和已发表的study.Results：获得的模型解释了超过56%的EQ-5D评分的方差的汇总统计。所有模型的平均预测EQ-5D评分均正确至小数点后两位，个体预测值的绝对误差约为0.13。使用汇总统计量预测样本内亚组平均EQ-5D评分，平均误差（平均绝对误差）范围为0.021 - 0.077（0.045-0.083）。样本外已发表数据集的统计量范围为0.048 - 0.099（0.064-0.010）。结论：该模型为研究人员提供了一种从已发表的平均维度得分估计EQ-5D效用数据的机制。这项研究的独特之处在于它使用了已发表研究的平均统计数据来验证结果。虽然需要进一步的研究来验证其他健康状况下的结果，但这些算法可用于推导其他基于偏好的措施，以用于经济分析。
Objective: The objective of the study was to derive a method to predict a mean cohort EQ-5D preference-based index score using published mean statistics of the eight dimension scores describing the SF-36 health profile.Methods: Ordinary least square regressions models are derived using patient level data (n = 6350) collected during 12 clinical studies. The models were compared for goodness of fit using standard techniques such as variance explained, the magnitude of errors in predicted values, and the proportion of values within the minimal important difference of the EQ-5D. Predictive abilities were also compared using summary statistics from both within-sample subgroups and published studies.Results: The models obtained explained more than 56% of the variance in the EQ-5D scores. The mean predicted EQ-5D score was correct to within two decimal places for all models and the absolute error for the individual predicted values was approximately 0.13. Using summary statistics to predict within-sample subgroup mean EQ-5D scores, the mean errors (mean absolute errors) ranged from 0.021 to 0.077 (0.045-0.083). These statistics for the out-of-sample published data sets ranged from 0.048 to 0.099 (0.064-0.010).Conclusions: The models provided researchers with a mechanism to estimate EQ-5D utility data from published mean dimension scores. This research is unique in that it uses mean statistics from published studies to validate the results. While further research is required to validate the results in additional health conditions, the algorithms can be used to derive additional preference-based measures for use in economic analyses.