Evidence map›Paper›PMID 38912229›Full record

ArticleCurrent epidemiology reports2024

Selection Bias in Health Research: Quantifying, Eliminating, or Exacerbating Health Disparities?

L Paloma Rojas-Saunero, M Maria Glymour, Elizabeth Rose Mayeda

Abstract read
In one paragraph

Article in Current epidemiology reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing 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

25 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Health-related quality of life changes in patients with Q-fever fatigue syndrome: a four-year follow-up study, 10 years post-infection.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2026
    Observational
  6. Article
  7. Article
  8. Causal Inference in Health Disparities Research.Annual review of public health · 2026
    Review
  9. Article
  10. Observational
  11. Article
  12. Review
  13. Article
  14. Article
  15. Article
  16. Article
  17. Review
  18. Observational
  19. Article
  20. 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

3 authors.

L Paloma Rojas-SauneroDepartment of Epidemiology, University of California, Los Angeles Fielding School of Public Health, Los Angeles, California, USA.ORCID 0000-0002-0875-5737
M Maria GlymourDepartment of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, USA.ORCID 0000-0001-9644-3081
Elizabeth Rose MayedaDepartment of Epidemiology, University of California, Los Angeles Fielding School of Public Health, Los Angeles, California, USA.ORCID 0000-0002-6326-0163

Funding

Lifecourse health, cerebral pathology and ethnic disparities in dementia (KHANDLE Study)R01AG052132 · NIA · UNIVERSITY OF CALIFORNIA AT DAVIS · PI GILSANZ, PAOLA, GLYMOUR, MEDELLENA MARIA · 2021 to 2025
$18.0M
Closing the gap between observational research and randomized trials for prevention of Alzheimer's Disease and dementiaR01AG057869 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GRAFF, REBECCA ELIZABETH, POWER, MELINDA C · 2018 to 2022
$3.9M
Historical social and environmental determinants of memory decline and dementia among U.S. older adults.R01AG074359 · NIA · UNIVERSITY OF WASHINGTON · PI CASEY, JOAN A, MAYEDA, ELIZABETH ROSE · 2021 to 2025
$3.0M
Alzheimer’s disease and related dementias in a diverse cohort of Asian AmericansR01AG063969 · NIA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI MAYEDA, ELIZABETH ROSE · 2019 to 2022
$2.0M
Optimizing generalizability of biomarker studies for Alzheimer’s disease and related dementias with epidemiologic toolsR56AG069126 · NIA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI MAYEDA, ELIZABETH ROSE · 2021 to 2021
$583k
NIA NIH HHS R01 AG052132NIA NIH HHS R01 AG057869NIA NIH HHS R01 AG063969NIA NIH HHS R01 AG074359NIA NIH HHS R56 AG069126
6 · The paper itself

Abstract

Purpose of review: To summarize recent literature on selection bias in disparities research addressing either descriptive or causal questions, with examples from dementia research. Recent findings: Defining a clear estimand, including the target population, is essential to assess whether generalizability bias or collider-stratification bias are threats to inferences. Selection bias in disparities research can result from sampling strategies, differential inclusion pipelines, loss to follow-up, and competing events. If competing events occur, several potentially relevant estimands can be estimated under different assumptions, with different interpretations. The apparent magnitude of a disparity can differ substantially based on the chosen estimand. Both randomized and observational studies may misrepresent health disparities or heterogeneity in treatment effects if they are not based on a known sampling scheme. Conclusion: Researchers have recently made substantial progress in conceptualization and methods related to selection bias. This progress will improve the relevance of both descriptive and causal health disparities research.

Indexed as

collider-stratification biascompeting eventsestimandsgeneralizabilityhealth disparitiesselection bias

Identifiers

PMID38912229
PMCPMC11192540

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.