对计数结果的统计模型性能进行模拟研究,其中过多的零值
Zhengyang Zhou1, Dateng Li2, David Huh3
1Department of Population and Community Health, University of North Texas Health Science Center, Fort Worth, Texas, USA.
Statistics in medicine
|August 28, 2024
概括
与其他模型相比,边缘化的零膨胀波桑 (MZIP) 模型在健康行为研究中为零膨胀计数数据提供了优越的统计能力和I型错误控制.
科学领域:
- 健康行为 研究 研究 研究 研究
- 生物统计学 生物统计学
- 统计建模 统计建模
背景情况:
- 在健康行为研究中,诸如追踪酒精消费等,过度使用零数的计数变量很普遍.
- 现有的统计模型很难用零膨胀数据准确评估干预的有效性.
- 需要对模型进行实证比较,包括新的边缘化计数回归方法.
研究的目的:
- 为了比较各种模型的统计能力和I型错误率,用于零膨胀计数结果.
- 在模拟健康行为数据中评估传统和新型回归方法的性能.
- 确定最有效的统计模型来分析干预对数量数据的干预效应,其中有许多零.
主要方法:
- 一项模拟研究比较了五种统计模型:线性 (原始和日志转换),Poisson,负二项式和零膨胀的Poisson (ZIP).
- 边缘化零膨胀波桑 (MZIP) 模型被纳入作为估计总人口平均效应的新替代方案.
- 模拟的样本大小,零率 (0.2-0.8) 和干预效果大小各不相同,其动机是酒精滥用预防试验.
主要成果:
- 在零通货膨胀下,Poisson模型控制不充分的I型错误率,导致膨胀的假阳性结果.
- 当干预效应在零和计数组件之间对齐时,MZIP模型表现出最高的统计能力.
- 具有日志转换结果的线性模型表现不佳;MZIP,原始线性,负二项式和ZIP模型表现不同.
结论:
- MZIP模型在检测干预效应和控制零膨胀计数数据中的错误阳性方面表现出卓越的统计特性.
- MZIP模型是一个有前途的分析方法,用于评估在研究中具有高比例的零计数的整体干预效应.
- 研究人员应该考虑MZIP模型来分析在健康行为研究中过多的零数的计数结果.
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