Confounding, causality, and confusion: the role of intermediate variables in interpreting observational studies in obstetrics.
Confounding, causality, and confusion: the role of intermediate variables in interpreting observational studies in obstetrics.
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DOI：
10.1016/j.ajog.2017.04.016
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发表时间：
2017-08
影响因子：
9.8
通讯作者：
Schisterman EF
中科院分区：
文献类型：
--
作者：
Ananth CV;Schisterman EF
前瞻性和回顾性队列研究以及病例对照研究都是流行病学中最重要的研究设计，因为在某些假设下，如果做得好，它们可以模仿随机试验。这些假设包括但不限于正确解释两个重要的偏倚来源：混杂和选择偏倚。虽然不调整一个中间变量的因果关系将产生一个无偏的估计，产科医生往往会想调整一个中间变量，以评估中间是否是潜在的驱动程序的关联。这种做法必须根据基本的研究问题加以权衡，并应仔细考虑是否有必要进行这种调整。到目前为止，孕龄是产科最常见的变量，经常被错误地标记为混杂因素，而事实上，它可能是一个中间变量。如果胎龄确实是一个中间变量，但如果错误地标记为混杂变量，并因此在分析中进行调整，则结论可能是意想不到的。这种过度调整中间体的影响，就好像它是一个混杂因素，可以使一个有说服力的研究完全失去意义。这篇评论提供了一个混杂的偏见，碰撞分层和选择偏见，在产科和围产期流行病学的应用博览会。
Both prospective and retrospective cohort, and case-control studies are some of the most important study designs in epidemiology because, under certain assumptions, they can mimic a randomized trial when done well. These assumptions include but not limited to properly accounting for two important sources of bias: confounding and selection bias. While not adjusting the causal association for an intermediate variable will yield an unbiased estimate of the exposure-outcome’s total causal effect, it is often that obstetricians will want to adjust for an intermediate variable to assess if the intermediate is the underlying driver of the association. Such a practice must be weighed in light of the underlying research question, and whether such an adjustment is necessary should be carefully considered. Gestational age is, by far, the most commonly encountered variable in obstetrics that is often mislabeled as a confounder when, in fact, it may be an intermediate. If, indeed, gestational age is an intermediate but if mistakenly labeled as a confounding variable and consequently adjusted in an analysis, the conclusions can be unexpected. The implications of this over adjustment of an intermediate as though it were a confounder can render an otherwise persuasive study downright meaningless. This commentary provides an exposition of confounding bias, collider stratification and selection biases, with applications in obstetrics and perinatal epidemiology.
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DOI：
10.1097/ede.0b013e3181c297af
发表时间：
2010-01
期刊：
Epidemiology (Cambridge, Mass.)
影响因子：
--
作者：
Ananth CV;Basso O
通讯作者：
Basso O
DOI：
10.1097/ede.0000000000000213
发表时间：
2015-01
期刊：
Epidemiology (Cambridge, Mass.)
影响因子：
--
作者：
Mendola P;Mumford SL;Männistö TI;Holston A;Reddy UM;Laughon SK
通讯作者：
Laughon SK
影响因子：
2.8
作者：
BUEKENS, P;WILCOX, AJ;MASUYSTROOBANT, G
通讯作者：
MASUYSTROOBANT, G
影响因子：
5
作者：
Buekens, P;Notzon, F;Wilcox, A
通讯作者：
Wilcox, A
影响因子：
5
作者：
Cole, Stephen R.;Hernan, Miguel A.
通讯作者：
Hernan, Miguel A.