[根据队列研究,选择用于估计暴露因素与罕见结果之间的关联的统计方法]
1Center for Clinical Epidemiology and Evidence-Based Medicine, Beijing Children's Hospital, Capital Medical University, National Center for Children Health, Beijing 100045, China.
概括
对于队列研究中的罕见结果,相对风险 (RR) 比赔率 (OR) 更容易解释. 逻辑-二项式或波桑回归可以估计RR,但逻辑回归可能更稳定.
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:
- 几率比率 (OR) 和相对风险 (RR) 是风险与结果关联的关键指标.
- 虽然OR在队列研究中的罕见结果中接近RR,但RR提供了更大的解释性.
研究的目的:
- 为了比较来自不同多变量分析的OR和RR估计,用于队列研究中的罕见结果.
- 为了指导选择适当的回归方法和报告指标,为罕见的结果.
主要方法:
- 利用来自中国出生队列研究的数据.
- 怀孕方式和先天性残疾分别是暴露和结果.
- 使用后勤学,日志-二项式和波松回归模型来估计OR和RR,并对母体年龄和家族史等共变量进行调整.
主要成果:
- 或从逻辑回归近似的RR从日志-二项式或波桑回归.
- 来自日志双项或波桑回归的RR估计较接近1.00,较窄的95%置信区间 (CI).
- 逻辑双项和波桑回归模型可能会遇到趋同或过度分散的问题.
结论:
- 相对风险 (RR) 建议在罕见结果的队列研究中报告,如果可行,使用日志-二项式或Poisson回归.
- 考虑与日志-二项式和Poisson模型的潜在收和过度分散问题.
- 后勤回归提供了一个稳定的替代方案,在罕见的结果中,OR接近RR.
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