Evidence map›Paper›PMID 34109972›Full record

ReviewEpidemiologic reviews2022

Matching Methods for Confounder Adjustment: An Addition to the Epidemiologist's Toolbox.

Noah Greifer, Elizabeth A Stuart

Abstract readReview
In one paragraph

Review in Epidemiologic reviews, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed, 1 pooled it
–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

44 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
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  7. Observational
  8. Mitigating Suicide Risk During the Military-to-Civilian Transition: The VA Veteran Sponsorship Initiative.International journal of environmental research and public health · 2026
    Article
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  11. Incidence and Risk Factors for New-onset Depression After Total Shoulder Arthroplasty.Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews · 2026
    Article
  12. Article
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  14. Observational
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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

2 authors.

Noah Greifer
Elizabeth A Stuart

Funding

Using an innovative quality improvement process to increase delivery of evidenced-based CVD risk factor care in community mental health organizationsP50MH115842 · NIMH · JOHNS HOPKINS UNIVERSITY · PI Christina T. Yuan · 2018 to 2026
$16.2M
NIMH NIH HHS P50 MH115842
6 · The paper itself

Abstract

Propensity score weighting and outcome regression are popular ways to adjust for observed confounders in epidemiologic research. Here, we provide an introduction to matching methods, which serve the same purpose but can offer advantages in robustness and performance. A key difference between matching and weighting methods is that matching methods do not directly rely on the propensity score and so are less sensitive to its misspecification or to the presence of extreme values. Matching methods offer many options for customization, which allow a researcher to incorporate substantive knowledge and carefully manage bias/variance trade-offs in estimating the effects of nonrandomized exposures. We review these options and their implications, provide guidance for their use, and compare matching methods with weighting methods. Because of their potential advantages over other methods, matching methods should have their place in an epidemiologist's methodological toolbox.

Indexed as

EpidemiologistsBiasHumansPropensity Scoreepidemiologic methodspropensity score

Identifiers

PMID34109972
PMCPMC9005055

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.