在简短的酒精干预试验中报告计数结果和干预效应的统计模型的选择:一篇综述和建议
Lin Tan1, Justin M Luningham1, David Huh2
1School of Public Health, The University of North Texas Health Science Center at Fort Worth, Fort Worth, Texas, USA.
Alcohol, clinical & experimental research
|December 6, 2023
概括
短期酒精干预 (BAI) 研究通常使用计数数据. 本次审查强调了需要适当的统计模型来分析过度分散和零膨胀的酒精消费数据,以准确估计干预效应.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 行为科学 行为科学
背景情况:
- 短期酒精干预 (BAI) 对公共卫生至关重要,但其有效性估计可能会受到计数结果的统计分析的影响.
- 在BAI试验中的计数数据,就像饮料消费一样,经常表现出过度分散和零通胀,违反了标准模型的假设.
- 不准确的统计方法导致对BAI疗效的偏见估计和不可靠的结论.
研究的目的:
- 批判性地审查青少年和年轻成人BAI试验中计数结果的统计模型和报告实践.
- 为选择和报告该领域计数数据的适当统计分析提供实用指南和决策指南.
- 促进在酒精流行病学方面进行更准确,透明和可重复的研究.
主要方法:
- 对64篇报道BAI试验 (2013-2018) 青少年和年轻成年人计数结果的文章进行系统审查.
- 分析使用的统计模型,重点关注它们对过度分散和零膨胀计数数据的适当性.
- 先进计数模型的概述,包括波桑回归,负二项式,零膨胀和边缘化零膨胀波桑回归.
主要成果:
- 大多数分析 (61.3%) 错误地使用线性模型,尽管违反了正常性假设.
- 过度分散是普遍存在的,75.6%的结果变量表现出这种特征.
- 当前的报告实践往往未能解决计数结果的分布性质.
结论:
- 标准的统计方法经常被错误地应用在BAI研究中计算结果,从而损害了结果.
- 采用适当的计数模型 (例如负二项式,零膨胀模型) 和透明的报告是必不可少的.
- 实施推的统计实践将提高BAI疗效研究的准确性和可重复性.
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