The curse of the perinatal epidemiologist: inferring causation amidst selection.
The curse of the perinatal epidemiologist: inferring causation amidst selection.
复制标题
围产期流行病学家的诅咒：在选择中推断因果关系。
DOI：
10.1007/s40471-018-0172-x
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发表时间：
2018
影响因子：
3.3
通讯作者：
Basso,Olga
中科院分区：
文献类型：
--
作者：
Snowden,JonathanM;Bovbjerg,MaritL;Dissanayake,Mekhala;Basso,Olga
人类生殖是一个常见的过程，在相对较短的时间内展开，但怀孕和分娩过程是具有挑战性的研究。选择发生在这个过程的每一步（例如，不孕症、早期妊娠丢失和死胎），增加了估计的妊娠结局相关性的实质性偏倚。在这里，我们专注于选择在围产期流行病学，特别是，它如何影响研究问题的制定，可行的研究设计，和结果的解释。最近的FindingsApproaches最近已被提出来解决围产期流行病学的选择问题。其中一种方法是对婴儿结局进行妊娠分层分析的持续妊娠分母。类似地，由左截断引起的偏差最近被称为“活产偏差”，并且提出的解决方案是控制选择变量的常见原因（例如，生育力、胎儿丢失）和出生结果。然而，这些方法都有理论上的缺陷，冲突的基本流行病学概念的人群中的风险，为一个给定的outcome.SummaryWe从事流行病学理论和思想实验来证明的问题，包括单位不“风险”的结果。结果定义和分析的基本（和常识）问题（例如，确保所有研究参与者都面临结果的风险）应优先考虑问题和分析方法，选择利益相关者关心的问题也是如此。人类生殖过程中的选择和由此产生的偏差使无偏因果关系的估计复杂化，但我们不应该仅仅（甚至主要）关注最小化这些偏差。
Purpose of ReviewHuman reproduction is a common process and one that unfolds over a relatively short time, but pregnancy and birth processes are challenging to study. Selection occurs at every step of this process (e.g., infertility, early pregnancy loss, and stillbirth), adding substantial bias to estimated exposure-outcome associations. Here, we focus on selection in perinatal epidemiology, specifically, how it affects research question formulation, feasible study designs, and interpretation of results.Recent FindingsApproaches have recently been proposed to address selection issues in perinatal epidemiology. One such approach is the ongoing pregnancies denominator for gestation-stratified analyses of infant outcomes. Similarly, bias resulting from left truncation has recently been termed “live birth bias,” and a proposed solution is to control for common causes of selection variables (e.g., fecundity, fetal loss) and birth outcomes. However, these approaches have theoretical shortcomings, conflicting with the foundational epidemiologic concept of populations at risk for a given outcome.SummaryWe engage with epidemiologic theory and employ thought experiments to demonstrate the problems of using denominators that include units not “at risk” of the outcome. Fundamental (and commonsense) concerns of outcome definition and analysis (e.g., ensuring that all study participants are at risk for the outcome) should take precedence in formulating questions and analysis approaches, as should choosing questions that stakeholders care about. Selection and resulting biases in human reproductive processes complicate estimation of unbiased causal exposure-outcome associations, but we should not focus solely (or even mostly) on minimizing such biases.