Evidence map›Paper›PMID 34371103›Full record

ArticleJournal of clinical epidemiology2022

Tutorial on directed acyclic graphs.

Jean C Digitale, Jeffrey N Martin, Medellena Maria Glymour

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of clinical epidemiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 191 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
191citing 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

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

  1. Pooled it
  2. Trial
  3. Review
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Dietary and social predictors of PFAS exposure in Hispanic children from Northern Virginia.Journal of exposure science & environmental epidemiology · 2026
    Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. LTBI Prevalence and Risk Factors in Field Practice Areas of a Tertiary Care Hospital, Jodhpur, Rajasthan.Indian journal of community medicine : official publication of Indian Association of Preventive & Social Medicine · 2026
    Article
  17. Article
  18. Article
  19. Article
  20. Review

131 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Jean C DigitaleDepartment of Epidemiology and Biostatistics, University of California, 550 16th St, 2nd Floor, San Francisco, CA 94158.
Jeffrey N MartinDepartment of Epidemiology and Biostatistics, University of California, 550 16th St, 2nd Floor, San Francisco, CA 94158.
Medellena Maria GlymourDepartment of Epidemiology and Biostatistics, University of California, 550 16th St, 2nd Floor, San Francisco, CA 94158. Electronic address: Maria.glymour@ucsf.edu.

Funding

Virology CoreP30AI027763 · NIAID · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI STEVEN Grant DEEKS · 1988 to 2026
$93.6M
Predicting earliest safe extubation time in pediatric patientsF31HL156498 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI DIGITALE, JEAN · 2021 to 2023
$118k
NHLBI NIH HHS F31 HL156498NIAID NIH HHS P30 AI027763
6 · The paper itself

Abstract

Directed acyclic graphs (DAGs) are an intuitive yet rigorous tool to communicate about causal questions in clinical and epidemiologic research and inform study design and statistical analysis. DAGs are constructed to depict prior knowledge about biological and behavioral systems related to specific causal research questions. DAG components portray who receives treatment or experiences exposures; mechanisms by which treatments and exposures operate; and other factors that influence the outcome of interest or which persons are included in an analysis. Once assembled, DAGs - via a few simple rules - guide the researcher in identifying whether the causal effect of interest can be identified without bias and, if so, what must be done either in study design or data analysis to achieve this. Specifically, DAGs can identify variables that, if controlled for in the design or analysis phase, are sufficient to eliminate confounding and some forms of selection bias. DAGs also help recognize variables that, if controlled for, bias the analysis (e.g., mediators or factors influenced by both exposure and outcome). Finally, DAGs help researchers recognize insidious sources of bias introduced by selection of individuals into studies or failure to completely observe all individuals until study outcomes are reached. DAGs, however, are not infallible, largely owing to limitations in prior knowledge about the system in question. In such instances, several alternative DAGs are plausible, and researchers should assess whether results differ meaningfully across analyses guided by different DAGs and be forthright about uncertainty. DAGs are powerful tools to guide the conduct of clinical research.

Indexed as

Confounding Factors, EpidemiologicBiasCausalityData Interpretation, StatisticalHumansSelection Bias

Identifiers

PMID34371103
PMCPMC8821727

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.