Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2016 Sep;30(5):521-8.
doi: 10.1111/ppe.12302. Epub 2016 May 10.

Constructing Causal Diagrams for Common Perinatal Outcomes: Benefits, Limitations and Motivating Examples with Maternal Antidepressant Use in Pregnancy

Gretchen Bandoli  1 , Kristin Palmsten  1 , Katrina F Flores  1   2 , Christina D Chambers  1   2
Affiliations

Constructing Causal Diagrams for Common Perinatal Outcomes: Benefits, Limitations and Motivating Examples with Maternal Antidepressant Use in Pregnancy

Gretchen Bandoli et al. Paediatr Perinat Epidemiol. 2016 Sep.

Abstract

Background: Covariate selection to reduce bias in observational data analysis has primarily relied upon statistical criteria to guide researchers. This approach may lead researchers to condition on variables that ultimately increase bias in the effect estimates. The use of directed acyclic graphs (DAGs) aids researchers in constructing thoughtful models based on hypothesised biologic mechanisms to produce the least biased effect estimates possible.

Methods: After providing an overview of different relations in DAGs and the prevailing mechanisms by which conditioning on variables increases or reduces bias in a model, we illustrate examples of DAGs for maternal antidepressants in pregnancy and four separate perinatal outcomes.

Results: By comparing and contrasting the diagrams for maternal antidepressant use in pregnancy and spontaneous abortion, major malformations, preterm birth, and postnatal growth, we illustrate the different conditioning sets required for each model. Moreover, we illustrate why it is not appropriate to condition on the same set of covariates for the same exposure and different perinatal outcomes. We further discuss potential selection biases, overadjustment of mediators on the causal path, and sufficient sets of conditioning variables.

Conclusion: In our efforts to construct parsimonious models that minimise confounding and selection biases, we must rely upon our scientific knowledge of the causal mechanism. By structuring data collection and analysis around hypothesised DAGs, we ultimately aim to validly estimate the causal effect of interest.

Keywords: causal inference; directed acyclic graphs; perinatal epidemiology.

PubMed Disclaimer

Figures

Figure 01
Figure 01
Directed acyclic graph (DAG) of the effects of X (exposure) on Y (outcome).
Figure 02
Figure 02
Directed acyclic graphs (DAGs) for (2a) the effects of maternal antidepressants on spontaneous abortion; and (2b) the effects of maternal antidepressants on major structural malformations.
Figure 03
Figure 03
Directed acyclic graphs (DAGs) for (3a) the effects of maternal antidepressants on preterm birth; and (3b) the effects of maternal antidepressants on postnatal growth.
See this image and copyright information in PMC

References

    1. Greenland S, Robins JM. Identifiability, exchangeability and confounding revisited. Epidemiol Perspect Innov. 2009;6:4. doi:10.1186/1742-5573-6-4. - PMC - PubMed
    1. Robins JM. Data, Design, and Background Knowledge in Etiologic Inference. Epidemiology. 2015;12(3):313–320. - PubMed
    1. Hernán M a., Hernández-Diaz S, Werler MM, Mitchell A a. Causal knowledge as a prerequisite for confounding evaluation: An application to birth defects epidemiology. Am J Epidemiol. 2002;155(2):176–184. doi:10.1093/aje/155.2.176. - PubMed
    1. Evans D, Chaix B, Lobbedez T, Verger C, Flahault A. Combining directed acyclic graphs and the change-in-estimate procedure as a novel approach to adjustment-variable selection in epidemiology. BMC Med Res Methodol. 2012;12(1):156. doi:10.1186/1471-2288-12-156. - PMC - PubMed
    1. Halsey LG, Curran-Everett D, Vowler SL, Drummond GB. The fickle P value generates irreproducible results. Nat Methods. 2015;12(3):179–185. doi:10.1038/nmeth.3288. - PubMed

Publication types

Substances