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对PHQ-9和GAD-7的映射功能产生EQ-5D-3L用于经济评估

Clara Mukuria¹, Matthew Franklin², Sebastian Hinde³

¹Division of Population Health, Sheffield Centre of Health and Related Research, University of Sheffield, Sheffield, S1 4DA, UK. c.mukuria@sheffield.ac.uk.

The European journal of health economics : HEPAC : health economics in prevention and care

|April 25, 2024

概括

映射函数现在可以从患者健康问卷9 (PHQ-9) 和通用焦虑障碍-7 (GAD-7) 中估计EQ-5D-3L值,用于经济评估. 这允许以质量调整的寿命年 (QALY) 估计,即使在临床试验中缺少EQ-5D-3L数据.

科学领域:

卫生经济学卫生经济学
心理测量测量心理测量测量
临床试验分析

背景情况:

经质量调整的寿命年 (QALYs) 对经济评估至关重要,但在临床试验中,EQ-5D-3L数据往往无法获得.
经常收集患者报告的结果,如患者健康问卷9 (PHQ-9) 和通用焦虑障碍-7 (GAD-7).
需要预测模型来弥合通常收集的数据和基本的经济评估指标之间的差距.

研究的目的:

开发和验证用于估计EQ-5D-3L实用值的映射函数.
为了从仅收集PHQ-9和GAD-7数据的临床试验中进行QALY估计.
促进健康技术评估中的强有力的经济评估.

主要方法:

利用了四项抑郁症治疗试验的数据,包括PHQ-9,GAD-7和EQ-5D-3L措施.
使用调整的有限依赖变量混合模型 (ALDVMM),普通最小平方 (OLS) 和托比特模型.
根据错误指标 (ME,MAE,RMSE) 和良性合适性评估,选择了最佳模型.

主要成果:

与OLS和Tobit模型相比,ALDVMM模型显示出更高的预测准确度.
ALDVMM模型实现了低平均误差 (ME ≤0.0018) 和MAE (0.1890.192).
推使用PHQ-9和GAD-7分数来估计EQ-5D-3L公用事业的四组件ALDVMM模型.

结论:

经过验证的映射函数可以从PHQ-9和GAD-7分数可靠地估计EQ-5D-3L公用事业.
这些功能支持经济评估,通过使用现有试验数据进行QALY计算.
在没有直接EQ-5D-3L数据的情况下,为整合健康实用措施提供了实际解决方案.

关键词:

EQ-5D-3L EQ-5D-3L EQ-5D-3L EQ-5D-3L EQ-5D-3L EQ-5D-3L EQ-5D-3 经济评估经济评价在GAD-7中,GAD-7是GAD-7的第一个类型.绘制地图是为了绘制地图.在 PHQ-9 中.公用事业公用事业

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