后勤回归:与二进制健康结果关联度量的估计的局限性
Lara Pinheiro-Guedes1, Clarisse Martinho2, Maria Rosário O Martins3
1Institute of Hygiene and Tropical Medicine. Universidade NOVA de Lisboa. Lisbon; Public Health Unit. Unidade Local de Saúde do Tâmega e Sousa. Marco de Canaveses. Portugal.
Acta medica portuguesa
|October 4, 2024
概括
后勤回归可能会高估常见结果的流行率. 强大的波桑回归为在横截面研究中估计关联提供了可行的替代方案,比传统的物流模型提供了更可靠的结果.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
背景情况:
- 后勤回归通常用于二进制结果,但当结果频繁 (> 10%) 时,可以产生对相对风险和流行率的偏差估计.
- 尽管有可用的替代方案,但对于这些场景的最佳统计模型存在缺乏共识.
研究的目的:
- 为了比较逻辑,逻辑二项式和强大的波桑回归模型的估计准确性和合适性.
- 评估这些模型在频繁二进制结果的横截面研究的背景下.
主要方法:
- 分析了两项横截面研究:一项是关于空气污染和心理健康,另一项是关于移民获得医疗保健.
- 来自逻辑回归的几率比率 (OR) 和来自日志双项和强大的波桑回归的流行率 (PR) 被计算出来.
- 为了进行比较,使用了信心区间 (CI),标准错误 (SE) 和Akaike信息标准 (AIC).
主要成果:
- 几率比率 (OR) 倾向于高估患病率 (PR),与PR估计相比,显示出更广泛的95%CI和更高的SEs.
- 在一项研究中,逻辑-二项式回归未能趋同,而在两项研究中,强大的波桑回归提供了估计值.
- 在一项研究中,物流回归显示出更好的匹配 (较低的AIC),但OR仍然高估了PRs.
结论:
- 来自物流回归的几率比率可能会导致由于高估导致误解,特别是在普遍的结果.
- 强大的波桑回归模型是对日志双项模型的实用替代方案,在频繁结果的横截面研究中提供稳定的估计.
- 选择合适的统计模型需要考虑不仅仅是适合性之外的多个标准.
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