Evidence map›Paper›PMID 23175567›Full record

ArticleThe American statistician

Optimal Nonbipartite Matching and Its Statistical Applications.

Bo Lu, Robert Greevy, Xinyi Xu, Cole Beck

Abstract read
In one paragraph

Article in The American statistician. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.

0numbers the graph read from it
0cells of the map it votes in
44citing 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

44 citing papers in PubMed.

  1. Trial
  2. Trial
  3. Trial
  4. Trial
  5. Article
  6. A Study of the Microdynamics of Early-Childhood Learning.The journal of political economy · 2025
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. The Association of Hospital MagnetPolicy, politics & nursing practice · 2021
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Observational
  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

4 authors.

Bo LuDivision of Biostatistics, College of Public Health, The Ohio State University, B110 Starling-Loving Hall, 320 West 10th Avenue, Columbus, OH 43210.
Robert Greevy
Xinyi Xu
Cole Beck

Funding

Institute for Population ResearchP2CHD058484 · NICHD · OHIO STATE UNIVERSITY · PI HAYFORD, SARAH R · 2014 to 2023
$5.0M
Statistics and Survey Methods CoreR24HD058484 · NICHD · OHIO STATE UNIVERSITY · PI QIAN, ZHENCHAO · 2009 to 2013
$2.1M
Initiative in Population ResearchR21HD047943 · NICHD · OHIO STATE UNIVERSITY · PI OLSEN, RANDALL J. · 2004 to 2008
$1.1M
NICHD NIH HHS P2C HD058484NICHD NIH HHS R21 HD047943NICHD NIH HHS R24 HD058484
6 · The paper itself

Abstract

Matching is a powerful statistical tool in design and analysis. Conventional two-group, or bipartite, matching has been widely used in practice. However, its utility is limited to simpler designs. In contrast, nonbipartite matching is not limited to the two-group case, handling multiparty matching situations. It can be used to find the set of matches that minimize the sum of distances based on a given distance matrix. It brings greater flexibility to the matching design, such as multigroup comparisons. Thanks to improvements in computing power and freely available algorithms to solve nonbipartite problems, the cost in terms of computation time and complexity is low. This article reviews the optimal nonbipartite matching algorithm and its statistical applications, including observational studies with complex designs and an exact distribution-free test comparing two multivariate distributions. We also introduce an R package that performs optimal nonbipartite matching. We present an easily accessible web application to make nonbipartite matching freely available to general researchers.

Identifiers

PMID23175567
PMCPMC3501247

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.