Evidence map›Paper›PMID 27160789›Full record

ArticlePaediatric and perinatal epidemiology2016

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

Gretchen Bandoli, Kristin Palmsten, Katrina F Flores, Christina D Chambers

Abstract read
In one paragraph

Article in Paediatric and perinatal epidemiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

22 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  13. Sleep Apnea and Poor COVID-19 Outcomes: Beware of Causal Intermediates and Colliders.American journal of respiratory and critical care medicine · 2021
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Gretchen BandoliDepartment of Pediatrics, University of California, San Diego, La Jolla, CA.
Kristin PalmstenDepartment of Pediatrics, University of California, San Diego, La Jolla, CA.
Katrina F FloresDepartment of Pediatrics, University of California, San Diego, La Jolla, CA.
Christina D ChambersDepartment of Pediatrics, University of California, San Diego, La Jolla, CA.

Funding

Oral Corticosteroid Use During Pregnancy and Risk for Preterm BirthK99HD082412 · NICHD · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI PALMSTEN, KRISTIN · 2015 to 2016
$274k
NICHD NIH HHS K99 HD082412
6 · The paper itself

Abstract

backgroundCovariate 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.

methodsAfter 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.

resultsBy 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.

conclusionIn 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.

Indexed as

Models, StatisticalPregnancy OutcomeAbnormalities, Drug-InducedAbortion, SpontaneousAntidepressive AgentsFemaleGrowthHumansPregnancyPremature BirthAntidepressive Agentscausal inferencedirected acyclic graphsperinatal epidemiology

Identifiers

PMID27160789
PMCPMC4970924

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.