在患者报告的结果测量中使用共变量识别DIF来源:一个模拟研究,比较基于Rasch家族模型的两种方法
Yseulys Dubuy1, Jean-Benoit Hardouin1,2,3, Myriam Blanchin1
1UMR INSERM 1246, MethodS in Patients-centered outcomes and HEalth ResEarch (SPHERE), Nantes Université, Nantes, France.
Frontiers in psychology
|August 28, 2023
概括
这项研究引入了两种新的算法,ROSALI-DIF FORWARD和BACKWARD,用于检测患者报告的结果 (PRO) 数据中的差异性项目功能 (DIF),具有多个共变量. 这些方法通过同时分析共变效应,减少假阳性和提高DIF检测精度来改进现有方法.
科学领域:
- 心理测量 心理测量 心理测量
- 健康 结果 研究 研究 结果
- 统计建模 统计建模
背景情况:
- 分析患者报告结果 (PRO) 数据通常涉及多个共同变量,需要准确检测差异性物品功能 (DIF).
- 当前的DIF检测方法经常独立分析共变量,这可能导致假阳性,特别是当共变量相关时.
- 同时分析多个共变量对于全面了解PRO指标中的DIF至关重要.
研究的目的:
- 开发和评估基于拉什家族模型的两种新的代,逐项DIF检测算法 (ROSALI-DIF FORWARD和BACKWARD).
- 为了允许在存在两个二进制共变量时调整DIF的组比较.
- 将新算法的性能与惩罚性概率方法进行比较.
主要方法:
- 开发了两种代算法,ROSALI-DIF FORWARD和ROSALI-DIF BACKWARD,用于使用Rasch模型进行DIF检测.
- 在各种健康研究代表性的条件下进行了模拟研究.
- 绩效评估的重点是错误/正确的DIF检测率,DIF大小/形式恢复,以及潜在变量估计偏差.
主要成果:
- 两种ROSALI-DIF算法都显示出较低的错误检测率,接近5%.
- 正确的DIF检测率随着DIF大小的增加而增加,并受到DIF形式的影响.
- 罗萨利-DIF算法表现优于惩罚性概率方法,特别是在识别项目值的同质差异方面.
结论:
- 开发的ROSALI-DIF算法提供了一个强大的方法来检测多个二进制共变量的PRO数据中的DIF.
- 在DIF检测中同时进行共变量分析,可以更好地评估和理解潜在的偏差.
- 这些发现为在健康研究环境中使用DIF检测方法的研究人员提供了宝贵的见解.
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