Evidence map›Paper›PMID 35047696›Full record

ArticleBMJ open science2021

Comparison of commonly used methods in random effects meta-analysis: application to preclinical data in drug discovery research.

Ezgi Tanriver-Ayder, Christel Faes, Tom van de Casteele, Sarah K McCann, Malcolm R Macleod

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMJ open science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 37 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
60citing papers in PubMed, 37 pooled it
2.9field-weighted citation impact, top 3% 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

60 citing papers in PubMed, 37 syntheses or guidelines pooled it, 82 citations in OpenAlex.

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  2. Influence of Concave Versus Convex Emergence Profiles on Midfacial Mucosal Stability-A Systematic Review With Meta-Analysis.Journal of esthetic and restorative dentistry : official publication of the American Academy of Esthetic Dentistry ... [et al.] · 2026
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  3. Retinal and choroidal optical coherence tomography findings in gestational diabetes mellitus: a systematic review and meta-analysis.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026
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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

5 authors at 4 institutions in 3 countries.

Ezgi Tanriver-AyderCentre for Clinical Brain Sciences, Edinburgh Medical School, The University of Edinburgh, Edinburgh, Scotland, UK.ORCID 0000-0002-9653-2540
Christel FaesData Science Institute (DSI), Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Hasselt University, Hasselt, Limburg, Belgium.
Tom van de CasteeleTranslational Medicine and Early Development Statistics, Janssen Pharmaceutica, Beerse, Antwerpen, Belgium.
Sarah K McCannQUEST Center for Transforming Biomedical Research, Berlin Institute of Health (BIH), Charité-Universitätsmedizin Berlin, Berlin, Germany.ORCID 0000-0003-4737-2349
Malcolm R MacleodCentre for Clinical Brain Sciences, Edinburgh Medical School, The University of Edinburgh, Edinburgh, Scotland, UK.ORCID 0000-0001-9187-9839
Janssen (Belgium) · BEBerlin Institute of Health at Charité - Universitätsmedizin Berlin · DEHasselt University · BEUniversity of Edinburgh · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMeta-analysis of preclinical data is used to evaluate the consistency of findings and to inform the design and conduct of future studies. Unlike clinical meta-analysis, preclinical data often involve many heterogeneous studies reporting outcomes from a small number of animals. Here, we review the methodological challenges in preclinical meta-analysis in estimating and explaining heterogeneity in treatment effects.

methodsAssuming aggregate-level data, we focus on two topics: (1) estimation of heterogeneity using commonly used methods in preclinical meta-analysis: method of moments (DerSimonian and Laird; DL), maximum likelihood (restricted maximum likelihood; REML) and Bayesian approach; (2) comparison of univariate versus multivariable meta-regression for adjusting estimated treatment effects for heterogeneity. Using data from a systematic review on the efficacy of interleukin-1 receptor antagonist in animals with stroke, we compare these methods, and explore the impact of multiple covariates on the treatment effects.

resultsWe observed that the three methods for estimating heterogeneity yielded similar estimates for the overall effect, but different estimates for between-study variability. The proportion of heterogeneity explained by a covariate is estimated larger using REML and the Bayesian method as compared with DL. Multivariable meta-regression explains more heterogeneity than univariate meta-regression.

conclusionsOur findings highlight the importance of careful selection of the estimation method and the use of multivariable meta-regression to explain heterogeneity. There was no difference between REML and the Bayesian method and both methods are recommended over DL. Multiple meta-regression is worthwhile to explain heterogeneity by more than one variable, reducing more variability than any univariate models and increasing the explained proportion of heterogeneity.

Indexed as

aggregate data meta-analysisBayesian analysisbetween-study heterogeneitymeta-regressionpreclinical animal studies

Identifiers

PMID35047696
PMCPMC8647574
OpenAlexW3109210909

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.