Evidence map›Paper›PMID 22436842›Full record

ArticleObesity (Silver Spring, Md.)2012

Misuse of odds ratios in obesity literature: an empirical analysis of published studies.

Gabriel S Tajeu, Bisakha Sen, David B Allison, Nir Menachemi

Open access · bronzeAbstract read
In one paragraph

Article in Obesity (Silver Spring, Md.), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 2 pooled it
3.2field-weighted citation impact, top 8% of its field
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

21 citing papers in PubMed, 2 syntheses or guidelines pooled it, 50 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Article
  6. Article
  7. Requiem for odds ratios.Health services research · 2024
    Article
  8. Comparisons of various estimates of theStatistical methods in medical research · 2024
    Article
  9. Article
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  11. Article
  12. Review
  13. Article
  14. Article
  15. Article
  16. Article
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  18. Article
  19. Log Odds and the Interpretation of Logit Models.Health services research · 2018
    Article
  20. A Most Odd Ratio:: Interpreting and Describing Odds Ratios.American journal of preventive medicine · 2017
    Article
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 at 2 institutions in 1 country.

Gabriel S TajeuDepartment of Health Care Organization and Policy, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA. gtajeu@uab.edu
Bisakha Sen
David B Allison
Nir Menachemi
University of Alabama at Birmingham · USAlabama Department of Public Health · US

Funding

Why is the prevalence of obesity so high in U.S. Southern States? Regional predictors of BMI and obesity treatment response.P30DK056336 · NIDDK · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI James O Hill · 2000 to 2026
$31.9M
NIDDK NIH HHS P30 DK056336NIDDK NIH HHS P30DK056336
6 · The paper itself

Abstract

Odds ratios (ORs) are widely used in scientific research to demonstrate the associations between outcome variables and covariates (risk factors) of interest, and are often described in language suitable for risks or probabilities, but odds and probabilities are related, not equivalent. In situations where the outcome is not rare (e.g., obesity), ORs no longer approximate the relative risk ratio (RR) and may be misinterpreted. Our study examines the extent of misinterpretation of ORs in Obesity and International Journal of Obesity. We reviewed all 2010 issues of these journals to identify all articles that presented ORs. Included articles were then primarily reviewed for correct presentation and interpretation of ORs; and secondarily reviewed for article characteristics that may have been associated with how ORs are presented and interpreted. Of the 855 articles examined, 62 (7.3%) presented ORs. ORs were presented incorrectly in 23.2% of these articles. Clinical articles were more likely to present ORs correctly than social science or basic science articles. Studies with outcome variables that had higher relative prevalence were less likely to present ORs correctly. Overall, almost one-quarter of the studies presenting ORs in two leading journals on obesity misinterpreted them. Furthermore, even when researchers present ORs correctly, the lay media may misinterpret them as relative RRs. Therefore, we suggest that when the magnitude of associations is of interest, researchers should carefully and accurately present interpretable measures of association--including RRs and risk differences--to minimize confusion and misrepresentation of research results.

Indexed as

Data Interpretation, StatisticalObesityPeriodicals as TopicPublishingRiskHumansOdds RatioResearch Design

Identifiers

PMID22436842
PMCPMC3399983
OpenAlexW2053675042

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.