在LGBTQ+健康差异研究中的共变量调整:将方法与假设对齐
Colleen A Reynolds1,2, Jarvis T Chen3, Payal Chakraborty1,4
1Department of Epidemiology, Harvard T.H. Chan School of Public Health.
American journal of epidemiology
|September 8, 2025
概括
研究人员可以通过使用定向环形图 (DAG) 来改进LGBTQ+健康差异研究,以选择合适的分析变量. 这有助于避免因调整调解媒介而不是混而引起的偏差.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 公共卫生 公共卫生
背景情况:
- 女同性恋者,男同性恋者,双性恋者,跨性别者,奇异性恋者以及所有性和性别少数群体 (LGBTQ+) 个人是一个被指定的健康差异人口.
- 方法严谨对于推动LGBTQ+健康研究至关重要.
- 定向非循环图 (DAG) 为因果推理提供了一个框架.
研究的目的:
- 引导研究人员识别适当的调整集用于因果分析在LGBTQ+健康差异研究使用DAG.
- 为了说明常见的方法陷,例如调整介质和碰撞机分层偏差.
- 为了证明DAG在解决该领域复杂的因果关系问题的实用性.
主要方法:
- 模拟了1000个数据集 (N=10,000),结果是流产.
- 利用DAG来识别混者与调解者.
- 从调整调解者 (例如,医疗辅助生殖) 的插图偏差.
- 证明了碰撞器分层偏差和调解器结果混.
- 在DAG分析中纳入未观察到的异性恋.
主要成果:
- 调整医疗辅助生殖等调解者可能会导致LGBTQ+健康因果研究中的偏见.
- 调解者-结果混和碰撞器分层偏差可能来自研究设计和招聘.
- DAG提供了一个视觉工具来识别适当的调整集,减轻偏差.
结论:
- 仔细考虑变量角色 (调解者与混者) 对于在LGBTQ+健康差异研究中有效的因果推断至关重要.
- DAG是研究人员导航复杂因果路径并避免方法学偏见的宝贵工具.
- 准确的因果推断对于解决LGBTQ+人口中的健康不平等至关重要.
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