Evidence map›Paper›PMID 36573275›Full record

SynthesisEcology letters2023

A robust and readily implementable method for the meta-analysis of response ratios with and without missing standard deviations.

Shinichi Nakagawa, Daniel W A Noble, Malgorzata Lagisz, Rebecca Spake, Wolfgang Viechtbauer, Alistair M Senior

Abstract readMeta-Analysis
In one paragraph

Synthesis in Ecology letters, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 18 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 18 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

29 citing papers in PubMed, 18 syntheses or guidelines pooled it.

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  11. Elevated COFrontiers in plant science · 2025
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  14. Dung beetles increase plant growth: a meta-analysis.Proceedings. Biological sciences · 2024
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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

6 authors.

Shinichi NakagawaEvolution & Ecology Research Centre and School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0002-7765-5182
Daniel W A NobleDivision of Ecology and Evolution, Research School of Biology, The Australian National University, Canberra, Australian Capital Territory, Australia.ORCID https://orcid.org/0000-0001-9460-8743
Malgorzata LagiszEvolution & Ecology Research Centre and School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0002-3993-6127
Rebecca SpakeSchool of Biological Sciences, University of Reading, Reading, UK.ORCID https://orcid.org/0000-0003-4671-2225
Wolfgang ViechtbauerFaculty of Health, Medicine, and Life Sciences, Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands.ORCID https://orcid.org/0000-0003-3463-4063
Alistair M SeniorCharles Perkins Centre, School of Life and Environmental Sciences and Sydney Centre for Precision Data Science, University of Sydney, New South Wales, Camperdown, Australia.ORCID https://orcid.org/0000-0001-9805-7280

Funding

Australian Research Council DP200100367
6 · The paper itself

Abstract

The log response ratio, lnRR, is the most frequently used effect size statistic for meta-analysis in ecology. However, often missing standard deviations (SDs) prevent estimation of the sampling variance of lnRR. We propose new methods to deal with missing SDs via a weighted average coefficient of variation (CV) estimated from studies in the dataset that do report SDs. Across a suite of simulated conditions, we find that using the average CV to estimate sampling variances for all observations, regardless of missingness, performs with minimal bias. Surprisingly, even with missing SDs, this simple method outperforms the conventional approach (basing each effect size on its individual study-specific CV) with complete data. This is because the conventional method ultimately yields less precise estimates of the sampling variances than using the pooled CV from multiple studies. Our approach is broadly applicable and can be implemented in all meta-analyses of lnRR, regardless of 'missingness'.

Indexed as

Research DesignBiasmeta-regressionmissing datamultiple imputationresearch synthesisrobust variance estimation

Identifiers

PMID36573275
PMCPMC10108319

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.