Evidence map›Paper›PMID 35869146›Full record

ReviewMucosal immunology2022

The rise to power of the microbiome: power and sample size calculation for microbiome studies.

Tahsin Ferdous, Lai Jiang, Irina Dinu, Julie Groizeleau, Anita L Kozyrskyj, Celia M T Greenwood, Marie-Claire Arrieta

Abstract readReview
PubMed Publisher
In one paragraph

Review in Mucosal immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
55citing papers in PubMed, 3 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

55 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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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

7 authors.

Tahsin FerdousIMPACTT: Integrated Microbiome Platforms for Advancing Causation Testing & Translation, Calgary, Canada.
Lai JiangIMPACTT: Integrated Microbiome Platforms for Advancing Causation Testing & Translation, Calgary, Canada.
Irina DinuIMPACTT: Integrated Microbiome Platforms for Advancing Causation Testing & Translation, Calgary, Canada.
Julie GroizeleauIMPACTT: Integrated Microbiome Platforms for Advancing Causation Testing & Translation, Calgary, Canada.
Anita L Kozyrskyj *IMPACTT: Integrated Microbiome Platforms for Advancing Causation Testing & Translation, Calgary, Canada.
Celia M T Greenwood *IMPACTT: Integrated Microbiome Platforms for Advancing Causation Testing & Translation, Calgary, Canada.
Marie-Claire Arrieta *IMPACTT: Integrated Microbiome Platforms for Advancing Causation Testing & Translation, Calgary, Canada. marie.arrieta@ucalgary.ca.ORCID 0000-0002-4490-3370

Funding

CIHR DC0190GP
6 · The paper itself

Abstract

A priori power and sample size calculations are crucial to appropriately test null hypotheses and obtain valid conclusions from all clinical studies. Statistical tests to evaluate hypotheses in microbiome studies need to consider intrinsic features of microbiome datasets that do not apply to classic sample size calculation. In this review, we summarize statistical approaches to calculate sample sizes for typical microbiome study scenarios, including those that hypothesize microbiome features to be the outcome, the exposure or the mediator, and provide relevant R scripts to conduct some of these calculations. This review is intended to be a resource to facilitate the conduct of sample size calculations that are based on testable hypotheses across several dimensions of the microbiome. Implementation of these methods will improve the quality of human or animal microbiome studies, enabling reliable conclusions that will generalize beyond the study sample.

Indexed as

MicrobiotaAnimalsHumansSample Size

Identifiers

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.