Evidence map›Paper›PMID 24287468›Full record

ArticleAmerican journal of epidemiology2014

Analyzing risks of adverse pregnancy outcomes.

Michael S Kramer, Xun Zhang, Robert W Platt

Abstract read
PubMed Publisher
In one paragraph

Article in American journal of epidemiology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed
–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

36 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Michael S Kramer
Xun Zhang
Robert W Platt

Funding

Canadian Institutes of Health Research
6 · The paper itself

Abstract

Approaches for analyzing the risks of adverse pregnancy outcomes have been the source of much debate and many publications. Much of the problem, in our view, is the conflation of time at risk with gestational age at birth (or birth weight, a proxy for gestational age). We consider the causal questions underlying such analyses with the help of a generic directed acyclic graph. We discuss competing risks and populations at risk in the context of appropriate numerators and denominators, respectively. We summarize 3 different approaches to quantifying risks with respect to gestational age, each of which addresses a distinct etiological or prognostic question (i.e., cumulative risk, prospective risk, or instantaneous risk (hazard)) and suggest the appropriate denominators for each. We show how the gestational age-specific risk of perinatal death (PND) can be decomposed as the product of the gestational age-specific risk of birth and the risk of PND conditional on birth at a given gestational age. Finally, we demonstrate how failure to consider the first of these 2 risks leads to selection bias. This selection bias creates the well-known crossover paradox, thus obviating the need to posit common causes of early birth and PND other than the study exposure.

Indexed as

Data Interpretation, StatisticalEpidemiologic Research DesignGestational AgePerinatal MortalityStillbirthBirth WeightCausalityFemaleHumansInfant, NewbornPregnancyPremature BirthRisk AssessmentSelection Biasbirth weightbirth weight paradoxearly neonatal deathgestational ageperinatal mortalitypreterm birthselection biasstillbirth

Identifiers

PMID24287468

What OpenQuestion holds

Textmetadata
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.