Evidence map›Paper›PMID 39098821›Full record

ArticleAmerican journal of epidemiology2024

Quantile regressions as a tool to evaluate how an exposure shifts and reshapes the outcome distribution: A primer for epidemiologists.

Aayush Khadka, Jillian L Hebert, M Maria Glymour, Fei Jiang, Amanda Irish, Kate A Duchowny, Anusha M Vable

Abstract read
In one paragraph

Article in American journal of epidemiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Trial
  2. Observational
  3. medRxiv : the preprint server for health sciences · 2026
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. 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

7 authors.

Aayush KhadkaDepartment of Family and Community Medicine, University of California San Francisco.ORCID 0009-0009-8736-6331
Jillian L HebertDepartment of Family and Community Medicine, University of California San Francisco.
M Maria GlymourDepartment of Epidemiology, Boston University.
Fei JiangDepartment of Epidemiology and Biostatistics, University of California San Francisco.
Amanda IrishDepartment of Epidemiology and Biostatistics, University of California San Francisco.
Kate A DuchownyInstitute for Social Research, University of Michigan Ann Arbor.
Anusha M VableDepartment of Family and Community Medicine, University of California San Francisco.ORCID 0000-0002-2202-0294

Funding

Network on Life Course Health Dynamics and Disparities in 21st Century America RenewalR24AG045061 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI JENNIFER A AILSHIRE, Sarah A. Burgard · 2014 to 2026
$5.2M
Evaluation of college accessibility and income security interventions as preventative measures for dementia risk and solutions to dementia disparitiesR01AG069092 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI VABLE, ANUSHA MURTHY · 2020 to 2024
$3.1M
Examining the role of the neighborhood environment on muscle function in older adultsR00AG066846 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI DUCHOWNY, CATHERINE · 2023 to 2025
$742k
NIA NIH HHS R00 AG066846NIA NIH HHS R01 AG069092NIA NIH HHS R24 AG045061
6 · The paper itself

Abstract

Quantifying how an exposure affects the entire outcome distribution is often important, e.g., for outcomes such as blood pressure which have non-linear effects on long-term morbidity and mortality. Quantile regressions offer a powerful way of estimating an exposure's relationship with the outcome distribution but remain underused in epidemiology. We introduce quantile regressions with a focus on distinguishing estimators for quantiles of the conditional and unconditional outcome distributions. We also present an empirical example in which we fit mean and quantile regressions to investigate educational attainment's association with later-life systolic blood pressure (SBP). We use data on 8,875 US-born respondents aged 50+ years from the US Health and Retirement Study. More education was negatively associated with mean SBP. Conditional and unconditional quantile regressions both suggested a negative association between education and SBP at all levels of SBP, but the absolute magnitudes of these associations were higher at higher SBP quantiles relative to lower quantiles. In addition to showing that educational attainment shifted the SBP distribution left-wards, quantile regression results revealed that education may have reshaped the SBP distribution through larger protective associations in the right tail, thus benefiting those at highest risk of cardiovascular diseases.

Indexed as

conditional quantile regressiondistributional effectseffect heterogeneityquantile regressionunconditional quantile regression

Identifiers

PMID39098821
PMCPMC12234224

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.