Evidence map›Paper›PMID 29973146›Full record

SynthesisBMC medical research methodology2018

Meta-analysis of binary outcomes via generalized linear mixed models: a simulation study.

Ilyas Bakbergenuly, Elena Kulinskaya

Abstract readMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in BMC medical research methodology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 21 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed, 21 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

32 citing papers in PubMed, 21 syntheses or guidelines pooled it.

  1. Pooled it
  2. Handedness and creativity: Facts and fictions.Psychonomic bulletin & review · 2025
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  11. The safety and effectiveness of the Contour Neurovascular System for the treatment of wide-necked aneurysms: A systematic review and meta-analysis of early experience.Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences · 2024
    Pooled it
  12. Endovascular outcomes for anterior choroidal artery aneurysms: systematic review and meta-analysis.Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association · 2024
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  19. Bayesian Methods for Meta-Analyses of Binary Outcomes: Implementations, Examples, and Impact of Priors.International journal of environmental research and public health · 2021
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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

2 authors.

Ilyas BakbergenulySchool of Computing Sciences, University of East Anglia, Norwich, NR4 7TJ, UK. i.bakbergenuly@uea.ac.uk.
Elena KulinskayaSchool of Computing Sciences, University of East Anglia, Norwich, NR4 7TJ, UK.ORCID 0000-0002-9843-1663

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSystematic reviews and meta-analyses of binary outcomes are widespread in all areas of application. The odds ratio, in particular, is by far the most popular effect measure. However, the standard meta-analysis of odds ratios using a random-effects model has a number of potential problems. An attractive alternative approach for the meta-analysis of binary outcomes uses a class of generalized linear mixed models (GLMMs). GLMMs are believed to overcome the problems of the standard random-effects model because they use a correct binomial-normal likelihood. However, this belief is based on theoretical considerations, and no sufficient simulations have assessed the performance of GLMMs in meta-analysis. This gap may be due to the computational complexity of these models and the resulting considerable time requirements.

methodsThe present study is the first to provide extensive simulations on the performance of four GLMM methods (models with fixed and random study effects and two conditional methods) for meta-analysis of odds ratios in comparison to the standard random effects model.

resultsIn our simulations, the hypergeometric-normal model provided less biased estimation of the heterogeneity variance than the standard random-effects meta-analysis using the restricted maximum likelihood (REML) estimation when the data were sparse, but the REML method performed similarly for the point estimation of the odds ratio, and better for the interval estimation.

conclusionsIt is difficult to recommend the use of GLMMs in the practice of meta-analysis. The problem of finding uniformly good methods of the meta-analysis for binary outcomes is still open.

Indexed as

AlgorithmsLikelihood FunctionsLinear ModelsModels, StatisticalBinomial DistributionComputer SimulationHumansOdds RatioGeneralized linear mixed-effects modelsHypergeometric-normal likelihoodMeta-analysisRandom effectsTransformation bias

Identifiers

PMID29973146
PMCPMC6032567

What OpenQuestion holds

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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.