由于研究中断而缺少数据对使用结果依赖抽样的纵向分析推断的影响.
Melissa P Wilson1,2, Kristine M Erlandson1, Camille M Moore2,3
1School of Medicine, Division of Infectious Disease, University of Colorado, Aurora, CO, United States.
International journal of epidemiology
|September 10, 2025
概括
结果依赖抽样 (ODS) 方法可以在纵向研究中引入偏差,特别是在缺少数据的情况下. 在ODS分析中包括不完全跟踪的参与者,可以提高对MNAR数据的稳定性.
科学领域:
- 生物统计学 生物统计学
- 纵向研究设计 纵向研究设计
- 流行病学 流行病学
背景情况:
- 纵向队列研究为回顾性分析提供了有价值的数据.
- 取决于结果的抽样 (ODS) 是一种有效的替代品,用于样本测试的随机抽样.
- ODS方法通常是从重复的二进制结果的病例控制设计中调整出来的.
研究的目的:
- 评估缺失数据机制 (MCAR, MAR, MNAR) 对纵向研究中的ODS方法的影响.
- 在从完整的病例与所有个体抽样时比较ODS性能.
- 评估ODS分析的稳定性,并进行不完整的后续.
主要方法:
- 模拟研究使用来自全球艾滋病毒感染,衰老和免疫功能长期观察性研究队列的先进临床治疗学的数据.
- 在完全随机缺失 (MCAR),随机缺失 (MAR) 和不随机缺失 (MNAR) 假设下检查缺失.
- 对完整病例的比较分析与所有个人进行比较,包括那些学的人.
主要成果:
- ODS方法显示偏差缺失非随机 (MNAR) 数据,类似于随机抽样.
- 当ODS将参与者排除在不完全跟进的情况下时,偏见会增加.
- 包括所有个体在内的ODS分析对MCAR强大,并且对MAR缺失的偏见较小.
结论:
- 参与者学是纵向研究中经常出现的挑战.
- 使用ODS的研究人员必须仔细考虑掉队对采样和分析的影响.
- 包括不完全跟进的个体在内,提高了ODS在纵向研究中的可靠性.
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