基于树的潜在变量模型,用于评估患者报告的结果指标中的差异性项目功能:一个模拟研究
Olayinka I Arimoro1, Lisa M Lix2, Scott B Patten1
1Department of Community Health Sciences & O'Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada.
概括
基于递归分区 (PCTree) 的部分信用模型有效地控制了患者报告结果测量 (PROM) 中的差异项目功能 (DIF). 这种统计方法表现出强的性能,特别是在邦费罗尼校正下,确保在异质群体中获得可靠的结果.
科学领域:
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
- 健康 结果 研究 研究 结果
背景情况:
- 患者报告的结果指标 (PROM) 对于评估健康状况至关重要.
- 患者反应的异质性可能导致差异性项目功能 (DIF),威胁到PROM的有效性.
- 现有的基于树的潜在变量模型,如PCTree,需要对DIF检测进行进一步的性能评估.
研究的目的:
- 评估基于递归分区 (PCTree) 的部分信贷模型的统计特性,用于检测多种PROM中的DIF.
- 在各种数据分析条件下评估PCTree的性能.
主要方法:
- 使用计算机模拟来评估PCTree的性能,包括Bonferroni调整和没有Bonferroni调整.
- 使用I型错误率和统计功率率来评估模型性能.
- 使用布拉德利的自由标准来对家族I型错误率控制进行了评估.
主要成果:
- 使用邦费罗尼校正的PCTree在所有模拟条件下有效控制了家族的I型错误率.
- 在500个或更大的样本大小 (N) 中,统计能力至少达到80%.
- 随着非DIF相关解释变量的增加,功率下降.
结论:
- 作为一种在不同患者群体中评估DIF的方法,PCTree显示出前景.
- 为使用PCTree提供了最佳数据分析条件的建议.
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