Evidence map›Paper›PMID 24683503›Full record

ArticleEpidemiology (Sunnyvale, Calif.)2011

Matching on Race and Ethnicity in Case-Control Studies as a Means of Control for Population Stratification.

Anand P Chokkalingam, Melinda C Aldrich, Karen Bartley, Ling-I Hsu, Catherine Metayer, Lisa F Barcellos, Joseph L Wiemels, John K Wiencke, Patricia A Buffler, Steve Selvin

Abstract read
In one paragraph

Article in Epidemiology (Sunnyvale, Calif.), 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
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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

10 authors.

Anand P ChokkalingamSchool of Public Health, University of California Berkeley, Berkeley.
Melinda C AldrichVanderbilt University Medical Center, Vanderbilt University, Nashville, Tennessee.
Karen BartleySchool of Public Health, University of California Berkeley, Berkeley.
Ling-I HsuSchool of Public Health, University of California Berkeley, Berkeley.
Catherine MetayerSchool of Public Health, University of California Berkeley, Berkeley.
Lisa F BarcellosSchool of Public Health, University of California Berkeley, Berkeley.
Joseph L WiemelsDepartment of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California.
John K WienckeDepartment of Neurological Surgery, University of California, San Francisco San Francisco, California.
Patricia A BufflerSchool of Public Health, University of California Berkeley, Berkeley.
Steve SelvinSchool of Public Health, University of California Berkeley, Berkeley.

Funding

Training CoreP42ES004705 · NIEHS · UNIVERSITY OF CALIFORNIA BERKELEY · PI MORELLO-FROSCH, RACHEL ADELE · 1987 to 2025
$74.6M
Environmental and Molecular Epidemiology of Childhood LeukemiaR01ES009137 · NIEHS · UNIVERSITY OF CALIFORNIA BERKELEY · PI METAYER, CATHERINE · 1999 to 2013
$21.1M
NIEHS NIH HHS P42 ES004705NIEHS NIH HHS R01 ES009137
6 · The paper itself

Abstract

Some investigators argue that controlling for self-reported race or ethnicity, either in statistical analysis or in study design, is sufficient to mitigate unwanted influence from population stratification. In this report, we evaluated the effectiveness of a study design involving matching on self-reported ethnicity and race in minimizing bias due to population stratification within an ethnically admixed population in California. We estimated individual genetic ancestry using structured association methods and a panel of ancestry informative markers, and observed no statistically significant difference in distribution of genetic ancestry between cases and controls (P=0.46). Stratification by Hispanic ethnicity showed similar results. We evaluated potential confounding by genetic ancestry after adjustment for race and ethnicity for 1260 candidate gene SNPs, and found no major impact (>10%) on risk estimates. In conclusion, we found no evidence of confounding of genetic risk estimates by population substructure using this matched design. Our study provides strong evidence supporting the race- and ethnicity-matched case-control study design as an effective approach to minimizing systematic bias due to differences in genetic ancestry between cases and controls.

Indexed as

Case-controlGenetic susceptibilityMatchingPopulation stratification

Identifiers

PMID24683503
PMCPMC3966291

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Registered trials

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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.