Evidence map›Paper›PMID 29315733›Full record

ArticleStatistics in medicine2018

A comparison of seven random-effects models for meta-analyses that estimate the summary odds ratio.

Dan Jackson, Martin Law, Theo Stijnen, Wolfgang Viechtbauer, Ian R White

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Statistics in medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers, 31 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
79citing papers in PubMed, 31 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

79 citing papers in PubMed, 31 syntheses or guidelines pooled it.

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  5. Audit and feedback: effects on professional practice.The Cochrane database of systematic reviews · 2025
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  18. Fixed- and Random-Effects Models.Methods in molecular biology (Clifton, N.J.) · 2022
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19 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

5 authors.

Dan JacksonMRC Biostatistics Unit, Institute of Public Health, University of Cambridge, Cambridge, UK.ORCID 0000-0002-4963-8123
Martin LawMRC Biostatistics Unit, Institute of Public Health, University of Cambridge, Cambridge, UK.
Theo StijnenMedical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, Netherlands.ORCID 0000-0001-9733-969X
Wolfgang ViechtbauerPsychiatry and Psychology, Maastricht University, Maastricht, Netherlands.ORCID 0000-0003-3463-4063
Ian R WhiteMRC Clinical Trials Unit Institute of Clinical Trials and Methodology, University College London, London, UK.

Funding

Medical Research Council MC_U105260558Medical Research Council MC_UU_00002/3Medical Research Council MC_UU_12023/21
6 · The paper itself

Abstract

Comparative trials that report binary outcome data are commonly pooled in systematic reviews and meta-analyses. This type of data can be presented as a series of 2-by-2 tables. The pooled odds ratio is often presented as the outcome of primary interest in the resulting meta-analysis. We examine the use of 7 models for random-effects meta-analyses that have been proposed for this purpose. The first of these models is the conventional one that uses normal within-study approximations and a 2-stage approach. The other models are generalised linear mixed models that perform the analysis in 1 stage and have the potential to provide more accurate inference. We explore the implications of using these 7 models in the context of a Cochrane Review, and we also perform a simulation study. We conclude that generalised linear mixed models can result in better statistical inference than the conventional 2-stage approach but also that this type of model presents issues and difficulties. These challenges include more demanding numerical methods and determining the best way to model study specific baseline risks. One possible approach for analysts is to specify a primary model prior to performing the systematic review but also to present the results using other models in a sensitivity analysis. Only one of the models that we investigate is found to perform poorly so that any of the other models could be considered for either the primary or the sensitivity analysis.

Indexed as

Linear ModelsLogistic ModelsMeta-Analysis as TopicOdds RatioBiasComputer SimulationHumansbinomial distributionexact within-study distributionsrandom-effects modelsstatistical computing

Identifiers

PMID29315733
PMCPMC5841569

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.