对于酒精和毒品使用频率的许多平衡顺序模型
Mark S Chambers1, Christopher Drovandi2
1National Drug and Alcohol Research Centre, University of New South Wales, Sydney, Australia.
The American journal of drug and alcohol abuse
|June 26, 2023
概括
顺序模型有效地分析了局限性物质使用计数数据,显示澳大利亚COVID-19流行期间大麻使用日数显著下降. 这种方法比传统方法更好地处理复杂的分布.
科学领域:
- 统计 统计 统计 统计
- 生物统计学 生物统计学
- 流行病学 流行病学
背景情况:
- 药物使用调查经常收集使用日的数据,由于上限,这可能会产生上限效应.
- 每周的物质使用模式可以导致长期总结中的多式联络分布.
- 传统的计数模型可能无法充分捕捉这些复杂的数据特征.
研究的目的:
- 为了证明顺序模型对分析边界和潜在的多模式计数数据的实用性.
- 为了突出使用单数模型的优点,每个不同的调查响应都有一个单独的级别.
- 将顺序模型的性能与其他标准计数数据模型进行比较.
主要方法:
- 贝叶斯比例概率顺序模型应用于443名非法毒品使用者的纵向大麻使用日数据.
- 每个独特的调查响应都被赋予一个顺序级,以推断确切的数值分布.
- 模型匹配与二项式,负二项式,障碍负二项式和β-二项式模型进行了比较.
主要成果:
- 比例概率顺序模型显示,它与大麻使用日数据相匹配,后续预测检查和留出一个信息标准证实了这一点.
- 在澳大利亚COVID-19大流行期间,大麻使用量显著下降.
- 超过任何特定使用大麻频率的几率估计在第4波比第1波低73%.
结论:
- 顺序模型非常适合处理在物质使用研究中常见的复杂,边界和多模式计数数据.
- 研究结果表明,在大流行期间,研究群体内大麻消费量大幅减少.
- 这项研究强调了使用适当的统计模型来准确解释物质使用趋势的重要性.
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