设计特征如何影响受访者参与? 在评估EQ-5D-5L的离散选择实验中评估属性缺席
Peiwen Jiang1,2, Deborah Street3, Richard Norman4
1Faculty of Health, Centre for Health Economics Research and Evaluation, University of Technology Sydney, Broadway, PO Box 123, Sydney, NSW, 2007, Australia. peiwen.jiang1@health.nsw.gov.au.
The patient
|March 15, 2025
概括
在离散选择实验 (DCE) 中的属性重叠提高了受访者参与度,并减少了不出席率. 建议使用Ngene或SAS修改的Fedorov设计,以更好地开发健康状态值集.
科学领域:
- 卫生经济学 卫生经济学
- 心理测量 心理测量 心理测量
- 调查方法 调查方法
背景情况:
- 离散选择实验 (DCE) 被广泛用于开发与健康相关的生活质量 (HRQoL) 价值集.
- 响应者启发式,如属性非出席 (ANA),可以影响这些值集的准确性.
- 在DCE任务中,属性级别的重叠可能会提高受访者参与度,并简化任务完成.
研究的目的:
- 为了比较DCE设计的有效性,有和没有属性级别重叠.
- 评估不同的设计施工方法如何影响受访者参与和ANA.
- 评估 ANA 对衍生健康状态实用值的影响.
主要方法:
- 在澳大利亚普通人口中使用EQ-5D-5L仪器进行了多臂DCE.
- 以不同级别的属性重叠的设计基于受访者参与度进行了比较,通过使用隐性类型模型推断的ANA量化.
- 利用所有受访者来估计实用性下降,而不是仅仅使用所有属性来估计.
主要成果:
- 包括属性重叠在内显著提高了全日制率,从22.3-28.4%增加到28.2-54.2%.
- 与其他设计相比,经过修改的Fedorov设计 (Ngene, SAS) 与重叠显示出更高的全员率.
- 在排除基于ANA分析的受访者之前和之后,属性的重要性有很大差异.
结论:
- 修改后的 Fedorov 设计包含属性重叠,有效地减少了 ANA,并提高了受访者对 DCE 研究的参与度.
- 属性缺勤分析作为一种有价值的质量控制工具,用于在健康评估中选择受访者.
- 这些发现支持开发准确的EQ-5D-5L值集的改进方法.
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