无监督项目反应理论模型用于评估患者报告结果的样本异质性
Tolulope T Sajobi1, Ridwan A Sanusi2, Nancy E Mayo3
1Department of Community Health Sciences, University of Calgary, 3280 Hospital Drive NW, Calgary, T2N 4Z6, Canada. ttsajobi@ucalgary.ca.
概括
两个无监督的项目反应理论模型在患者报告的结果测量中,在识别与差异性项目功能相关的因素方面显示出不一致的结果. 需要进一步的模拟来评估模型性能.
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:
- 患者报告的结果指标 (PROM) 对于评估患者健康至关重要.
- 差异性项目功能 (DIF) 分析对于确保PROM在不同子组中被一致解释至关重要.
- 无监督项目响应理论 (IRT) 模型提供了一种方法来检测DIF,当底层共变量是未知的.
研究的目的:
- 为了比较两个无监督IRT模型之间的结果一致性:IRTrees (基于递归分区的IRT) 和MixIRT (混合IRT).
- 使用这些模型,在医院焦虑和抑郁量表 (HADS) 抑郁症子量表中识别与差异性反应模式相关的共变量.
主要方法:
- 利用了来自阿尔伯塔省冠心病登记处结局评估项目4478名心脏病患者的数据.
- 应用的多种IRT模型:基于递归分区 (PCTree) 的部分信用模型和混合部分信用模型 (MixPCM).
- 研究人口和临床特征作为DIF的潜在共变量.
主要成果:
- PCTree模型根据吸烟状态,年龄和体重指数确定了四个终端节点.
- 一个三类混合部分信贷模型为数据提供了很好的匹配.
- 混合PCM潜伏类的特点是年龄,疾病征兆,吸烟状况和并发症 (糖尿病,充血性心力衰竭,COPD).
结论:
- 在PCTree和MixPCM模型中,在识别与PROM项目的不同解释相关的共变量方面出现了不一致.
- 未来的研究应该使用计算机模拟来评估这些模型在DIF共变量检测中的I型错误率和统计能力.
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