There is limited evidence of mapping clinical instruments to a generic preference-based instrument in Asian patient populations. The current study aims to map the eight-item Patient Health Questionnaire depression scale (PHQ-8) onto the EuroQol Five-Dimension (EQ-5D), the Health Utilities Index Mark 3 (HUI3) and the Short Form Six-Dimension (SF-6D) which helps to inform future cost-utility analyses of treatments for depression. A total of 249 participants who had completed PHQ-8, EQ-5D, SF-6D and HUI3 questionnaires were included in the analyses. A beta regression mixture model was used to map the utility scores as a function of PHQ-8 total scores, PHQ-squared, age and gender. The predictive accuracy of the models was examined using mean absolute error and root mean square error. The results were compared against two common regression methods including Ordinary Least Square (OLS) and Tobit regression models. The mean age of the sample was 36.2 years (SD = 11.1). The mean EQ-5D-3L, EQ-5D-5L, HUI3 and SF-6D utility scores were 0.615, 0.709, 0.461 and 0.607, respectively. The EQ-5D-3L, EQ-5D-5L and SF-6D utility scores were best predicted by the beta mixture regression model consisting of PHQ-8 total sores, PHQ-squared, and covariates including age and gender. The HUI3 was best predicted by the OLS regression model. The current study provides important evidence to clinicians and researchers about the mapping algorithms that can be used in economic evaluation among patients with depression.

中文翻译：

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将 PHQ-8 映射到抑郁症患者的 EQ-5D、HUI3 和 SF6D

将临床仪器映射到亚洲患者人群中基于通用偏好的仪器的证据有限。目前的研究旨在将八项患者健康问卷抑郁量表 (PHQ-8) 映射到 EuroQol 五维 (EQ-5D)、健康公用事业指数标记 3 (HUI3) 和简表六维 (SF -6D)，这有助于为未来抑郁症治疗的成本效用分析提供信息。共有 249 名已完成 PHQ-8、EQ-5D、SF-6D 和 HUI3 问卷的参与者被纳入分析。使用 beta 回归混合模型将效用分数映射为 PHQ-8 总分、PHQ 平方、年龄和性别的函数。使用平均绝对误差和均方根误差检查模型的预测准确性。结果与两种常见的回归方法进行了比较，包括普通最小二乘法 (OLS) 和 Tobit 回归模型。样本的平均年龄为 36.2 岁 (SD = 11.1)。平均 EQ-5D-3L、EQ-5D-5L、HUI3 和 SF-6D 效用分数分别为 0.615、0.709、0.461 和 0.607。EQ-5D-3L、EQ-5D-5L 和 SF-6D 效用分数最好通过由 PHQ-8 总疮口、PHQ 平方和包括年龄和性别在内的协变量组成的 beta 混合回归模型进行预测。OLS 回归模型最能预测 HUI3。当前的研究为临床医生和研究人员提供了关于可用于抑郁症患者经济评估的映射算法的重要证据。EQ-5D-5L、HUI3 和 SF-6D 效用分数分别为 0.615、0.709、0.461 和 0.607。EQ-5D-3L、EQ-5D-5L 和 SF-6D 效用分数最好通过由 PHQ-8 总疮口、PHQ 平方和包括年龄和性别在内的协变量组成的 beta 混合回归模型进行预测。OLS 回归模型最能预测 HUI3。当前的研究为临床医生和研究人员提供了关于可用于抑郁症患者经济评估的映射算法的重要证据。EQ-5D-5L、HUI3 和 SF-6D 效用分数分别为 0.615、0.709、0.461 和 0.607。EQ-5D-3L、EQ-5D-5L 和 SF-6D 效用分数最好通过由 PHQ-8 总疮口、PHQ 平方和包括年龄和性别在内的协变量组成的 beta 混合回归模型进行预测。OLS 回归模型最能预测 HUI3。当前的研究为临床医生和研究人员提供了关于可用于抑郁症患者经济评估的映射算法的重要证据。