Howards Penelope P, Schisterman Enrique F, Heagerty Patrick J
Division of Epidemiology, Statistics, and Prevention Research, National Institute of Child Health and Human Development, Bethesda, Maryland 20892, USA.
Epidemiology. 2007 Sep;18(5):544-51. doi: 10.1097/ede.0b013e31812001e6.
Prior pregnancy outcomes, such as spontaneous abortion and preterm birth, are often predictive of future pregnancy outcomes. Therefore, many researchers adjust for reproductive history. Although this adjustment may be appropriate for a predictive model, it is not necessarily appropriate when the goal is to obtain an unbiased estimate of the effect of exposure on disease. Reproductive history may seem to meet the conventional criteria for confounding because it is unlikely to be on the causal pathway between exposure and current outcome, is often associated with current outcome, and may be associated with exposure as well. However, whether reproductive history is a confounder or not depends on the underlying reason for its associations with exposure and current outcome. Thus, conventional methods for assessing confounding are often inadequate. Directed acyclic graphs (DAGs) can be used to evaluate complex scenarios for confounding when the research question is clearly defined with respect to the exposure, the outcome, and the effect estimate of interest. Special care is required when reproductive history affects future exposure. We use 5 DAGs to illustrate possible relations between reproductive history and current outcome. We assess each DAG for confounding, and identify the appropriate analytic technique. We provide a numeric example using data from the Collaborative Perinatal Project. There is no single answer as to whether reproductive history should be included in the model; the decision depends on the research question and the underlying DAG.
既往妊娠结局，如自然流产和早产，往往能预测未来的妊娠结局。因此，许多研究人员会对生殖史进行调整。虽然这种调整可能适用于预测模型，但当目标是获得暴露对疾病影响的无偏估计时，它不一定合适。生殖史似乎符合混杂的传统标准，因为它不太可能处于暴露与当前结局之间的因果路径上，通常与当前结局相关，也可能与暴露相关。然而，生殖史是否为混杂因素取决于其与暴露和当前结局关联的潜在原因。因此，评估混杂的传统方法往往并不充分。当关于暴露、结局和感兴趣的效应估计的研究问题明确界定后，有向无环图（DAG）可用于评估混杂的复杂情况。当生殖史影响未来暴露时，需要特别小心。我们使用5个有向无环图来说明生殖史与当前结局之间的可能关系。我们评估每个有向无环图的混杂情况，并确定合适的分析技术。我们使用围产期协作项目的数据提供了一个数值示例。关于生殖史是否应纳入模型并没有单一答案；这一决定取决于研究问题和潜在的有向无环图。
