Evidence map›Paper›PMID 36208412›Full record

ReviewSports medicine (Auckland, N.Z.)2023

With Great Power Comes Great Responsibility: Common Errors in Meta-Analyses and Meta-Regressions in Strength & Conditioning Research.

Daniel Kadlec, Kristin L Sainani, Sophia Nimphius

Open access · hybridAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Sports medicine (Auckland, N.Z.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers, 47 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
82citing papers in PubMed, 47 pooled it
19.2field-weighted citation impact, top 1% of its field
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

82 citing papers in PubMed, 47 syntheses or guidelines pooled it, 108 citations in OpenAlex.

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22 more citing papers are in PubMed but not listed here.

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

3 authors at 2 institutions in 2 countries.

Daniel KadlecSchool of Medical and Health Sciences, Centre for Human Performance, Edith Cowan University, 270 Joondalup Drive, Joondalup, WA, 6027, Australia. d.kadlec@ecu.edu.au.ORCID 0000-0002-6986-4689
Kristin L SainaniEpidemiology and Population Health, Stanford University, Stanford, CA, USA.ORCID 0000-0003-0614-303X
Sophia NimphiusSchool of Medical and Health Sciences, Centre for Human Performance, Edith Cowan University, 270 Joondalup Drive, Joondalup, WA, 6027, Australia.ORCID 0000-0002-3524-0245
Edith Cowan University · AUStanford University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectiveMeta-analysis and meta-regression are often highly cited and may influence practice. Unfortunately, statistical errors in meta-analyses are widespread and can lead to flawed conclusions. The purpose of this article was to review common statistical errors in meta-analyses and to document their frequency in highly cited meta-analyses from strength and conditioning research.

methodsWe identified five errors in one highly cited meta-regression from strength and conditioning research: implausible outliers; overestimated effect sizes that arise from confusing standard deviation with standard error; failure to account for correlated observations; failure to account for within-study variance; and a focus on within-group rather than between-group results. We then quantified the frequency of these errors in 20 of the most highly cited meta-analyses in the field of strength and conditioning research from the past 20 years.

resultsWe found that 85% of the 20 most highly cited meta-analyses in strength and conditioning research contained statistical errors. Almost half (45%) contained at least one effect size that was mistakenly calculated using standard error rather than standard deviation. In several cases, this resulted in obviously wrong effect sizes, for example, effect sizes of 11 or 14 standard deviations. Additionally, 45% failed to account for correlated observations despite including numerous effect sizes from the same study and often from the same group within the same study.

conclusionsStatistical errors in meta-analysis and meta-regression are common in strength and conditioning research. We highlight five errors that authors, editors, and readers should check for when preparing or critically reviewing meta-analyses.

Identifiers

PMID36208412
PMCPMC9877053
OpenAlexW4303645629

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.