Evidence map›Paper›PMID 30575793›Full record

ArticleScientific reports2018

An adaptive microbiome α-diversity-based association analysis method.

Hyunwook Koh

Abstract read
In one paragraph

Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 2 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

26 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  3. Trial
  4. Article
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  17. Antidiabetic potential of polysaccharides fromCurrent research in food science · 2022
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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

1 author.

Hyunwook KohDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, 21205, United States. hkoh7@jhu.edu.ORCID http://orcid.org/0000-0001-6893-7164

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To relate microbial diversity with various host traits of interest (e.g., phenotypes, clinical interventions, environmental factors) is a critical step for generic assessments about the disparity in human microbiota among different populations. The performance of the current item-by-item α-diversity-based association tests is sensitive to the choice of α-diversity metric and unpredictable due to the unknown nature of the true association. The approach of cherry-picking a test for the smallest p-value or the largest effect size among multiple item-by-item analyses is not even statistically valid due to the inherent multiplicity issue. Investigators have recently introduced microbial community-level association tests while blustering statistical power increase of their proposed methods. However, they are purely a test for significance which does not provide any estimation facilities on the effect direction and size of a microbial community; hence, they are not in practical use. Here, I introduce a novel microbial diversity association test, namely, adaptive microbiome α-diversity-based association analysis (aMiAD). aMiAD simultaneously tests the significance and estimates the effect score of the microbial diversity on a host trait, while robustly maintaining high statistical power and accurate estimation with no issues in validity.

Indexed as

BiodiversityComputer SimulationAdaptation, BiologicalAnimalsHost SpecificityHumansMicrobiotaModels, TheoreticalPhenotypePhylogenyResearch Design

Identifiers

PMID30575793
PMCPMC6303306

What OpenQuestion holds

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LicenceCC BY
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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.