当前状态偏差对最小重要的变化估计的影响:一个模拟研究
Berend Terluin1,2, Piper Fromy3, Andrew Trigg4
1Department of General Practice, Amsterdam UMC, Vrije Universiteit Amsterdam, de Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands. b.terluin@amsterdamumc.nl.
概括
当前状态偏差 (PSB) 可以影响最小重要的变化 (MIC) 估计. 不受约束的纵向项目响应理论 (LIRT) 和纵向确认因子分析 (LCFA) 方法准确地估计了MIC,无论PSB如何.
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) 对于评估治疗效果至关重要.
- 最小重要的变化 (MIC) 量化了PROM中被认为是有益的最小变化.
- 过渡评级中的现状偏差 (PSB) 可以扭曲MIC估计.
研究的目的:
- 研究当前状态偏差 (PSB) 对估计最小重要变化 (MIC) 的各种方法的影响.
- 确定MIC估计的可靠方法,不受PSB的影响.
主要方法:
- 模拟了3240个样本,具有不同程度的真实MIC和PSB.
- 使用平均变化 (MC),接收器运行特征 (ROC) 分析,预测建模 (PM),调整预测建模 (APM),纵向项目响应理论 (LIRT) 和纵向确认因子分析 (LCFA) 估计的MIC.
- 评估了LIRT和LCFA的性能,并没有对参数的限制.
主要成果:
- MC,ROC和PM方法容易产生与PSB无关的偏差.
- 当PSB是实质性的时,PSB在APM中引入了不准确性,并限制了LIRT/LCFA估计.
- 不受约束的LIRT和LCFA方法提供了无偏见和精确的MIC估计,无论PSB.
结论:
- 推使用不受约束的LIRT和LCFA来估计基于的MIC,因为它们对PSB的稳定性很强.
- 当PSB最小时,APM可以作为一个可行的替代方案.
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