title: Constructing Causal Diagrams for Common Perinatal Outcomes: Benefits, Limitations and Motivating Examples with Maternal Antidepressant Use in Pregnancy.
abstract: 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.
  
  
  
  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.
  
  
  
  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.
  
  
  
  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.
authors.affiliation: Department of Pediatrics, University of California, San Diego, La Jolla, CA.
authors.forename: Gretchen
authors.initials: G
authors.lastname: Bandoli
authors.affiliation: Department of Pediatrics, University of California, San Diego, La Jolla, CA.
authors.forename: Kristin
authors.initials: K
authors.lastname: Palmsten
authors.affiliation: Department of Pediatrics, University of California, San Diego, La Jolla, CA.|Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, CA.
authors.forename: Katrina F
authors.initials: KF
authors.lastname: Flores
authors.affiliation: Department of Pediatrics, University of California, San Diego, La Jolla, CA.|Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, CA.
authors.forename: Christina D
authors.initials: CD
authors.lastname: Chambers
mesh_terms: D000014:Abnormalities, Drug-Induced / Q000209:etiology; D000022:Abortion, Spontaneous / Q000139:chemically induced; D000928:Antidepressive Agents / Q000009:adverse effects*; D005260:Female; D006128:Growth / Q000187:drug effects; D006801:Humans; D015233:Models, Statistical; D011247:Pregnancy / Q000187:drug effects*; D011256:Pregnancy Outcome; D047928:Premature Birth / Q000139:chemically induced
chemical_list: D000928:Antidepressive Agents
keywords: causal inference; directed acyclic graphs; perinatal epidemiology