用小样本大小评估共变平衡的方法
George Hripcsak1,2,3, Linying Zhang2,4, Yong Chen2,5,6,7
1Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA.
概括
倾向性得分调整诊断可以错误地标记由于偶然而导致的不平衡,特别是在元分析中. 一种新的诊断方法通过测试统计学上显著的不平衡来提高准确性,提高研究有效性.
科学领域:
- 生物统计学 生物统计学
- 流行病学 流行病学
- 健康研究方法 健康研究方法
背景情况:
- 倾向性得分调整方法 (匹配,分层,加权) 用于控制观察性研究中的混.
- 标准诊断,像标准化平均差异 (SMD) 值一样,评估共变量平衡,但在小到中等样本大小中可能不可靠.
- 机会失衡可能导致错误地拒绝研究的有效性,特别是在使用SMD的固定值时.
研究的目的:
- 为解决因机会失衡而导致的倾向性得分诊断中I型错误率膨胀的问题.
- 提出和评估一种用于调整倾向得分的新型诊断方法,以增加元分析的精度.
- 在大规模研究和元分析中提高协变量平衡评估的可靠性和严格性.
主要方法:
- 提出了一种替代诊断方法,通过统计测试是否标准化平均差异 (SMD) 显著超过预定义的值.
- 通过使用模拟和现实世界的数据,对标准名义值测试对拟议诊断的性能进行了评估.
- 研究了诊断在元分析中的行为,强调了对诊断的元分析以及效果估计的需要.
主要成果:
- 机会失衡在现实环境中是一个重要的问题,即使样本大小高达2000年.
- 提出的诊断表明,在各种样本大小 (250-4000) 和共变量数量 (20-100,000) 中,I型错误率和统计能力之间的优异权衡.
- 分析需要伴随着诊断的分析,以防止系统的混从压倒性的效果估计.
结论:
- 拟议的统计学显著值诊断为评估倾向性得分调整中的共变量平衡提供了更强大的方法.
- 这种方法对于确保元分析结果的有效性至关重要,特别是在网络研究中,混可能很大.
- 该程序促进了对众多共变量的审查,从而导致更严格和可靠的研究诊断.
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