基于树的项目响应理论模型,用于评估患者报告结果的差异性项目功能:基于网络的R Shiny实现
Olayinka I Arimoro1, Lisa M Lix2, Mark A Ferro3
1Department of Community Health Sciences & O'Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada.
概括
本研究引入了一个网络应用程序,用于评估基于树的项目响应理论 (IRT) 模型的患者报告结果 (PROM) 的差异性项目功能 (DIF). 该工具有助于识别样本异质性,提高医疗保健研究中PROM得分推断的有效性.
科学领域:
- 医疗信息学
- 心理测量
- 统计模型
背景情况:
- 患者报告的结果测量 (PROM) 对于评估健康状况至关重要,但其有效性可能会受到差异性功能 (DIF) 的影响.
- DIF源于同一基本健康状况的个体对单项反应的系统差异,可能导致不准确的结论和临床决定.
- 当未知共变量时,传统的DIF检测方法可能会失败,因此需要强大的替代方法.
研究的目的:
- 引入一个用户友好的网络应用程序,以实现基于树的项目响应理论 (IRT) 模型,以检测患者报告的结果测量 (PROM) 数据中的差异性项目功能 (DIF).
- 提供一个能够容纳潜在异质群体和未知的DIF相关共变量的工具.
- 通过识别样本异质性的来源,促进PROM得分的准确解释.
主要方法:
- 开发一个基于树的IRT建模的R Shiny网络应用程序.
- 该应用程序支持灵活的模型规范,交互式数据可视化以及各种数据类型的可定制设置.
- 包括一个数据准备,模型选择和结果解释的教程.
主要成果:
- 该网络应用程序允许交互式数据上传 (.CSV, .XLSX) 和对二元和多元项目的DIF测试.
- 提供基于模拟研究的参数选择建议.
- 输出系数,项目参数和图表以确定潜在的DIF来源.
结论:
- 开发的网络应用程序对于研究人员和临床医生来说是一个可访问和有价值的工具.
- 它增强了对DIF引起的PROM数据样本异质性的理解.
- 在临床和研究环境中从PROM得分得出更可靠的推断.
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