Evidence map›Paper›PMID 33932323›Full record

ArticleResearch synthesis methods2021

Using information-theoretic approaches for model selection in meta-analysis.

Ozan Cinar, James Umbanhowar, Jason D Hoeksema, Wolfgang Viechtbauer

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In one paragraph

Article in Research synthesis methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 9 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 9 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

22 citing papers in PubMed, 9 syntheses or guidelines pooled it.

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  8. Location-scale models for meta-analysis.Research synthesis methods · 2022
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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

4 authors.

Ozan CinarDepartment of Psychiatry and Neuropsychology, Maastricht University, Maastricht, The Netherlands.ORCID https://orcid.org/0000-0003-0329-1977
James UmbanhowarDepartment of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.ORCID https://orcid.org/0000-0002-8251-9388
Jason D HoeksemaDepartment of Biology, University of Mississippi, University, Mississippi, USA.ORCID https://orcid.org/0000-0003-2338-8442
Wolfgang ViechtbauerDepartment of Psychiatry and Neuropsychology, Maastricht University, Maastricht, The Netherlands.ORCID https://orcid.org/0000-0003-3463-4063

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Meta-regression can be used to examine the association between effect size estimates and the characteristics of the studies included in a meta-analysis using regression-type methods. By searching for those characteristics (i.e., moderators) that are related to the effect sizes, we seek to identify a model that represents the best approximation to the underlying data generating mechanism. Model selection via testing, either through a series of univariate models or a model including all moderators, is the most commonly used approach for this purpose. Here, we describe alternative model selection methods based on information criteria, multimodel inference, and relative variable importance. We demonstrate their application using an illustrative example and present results from a simulation study to compare the performance of the various model selection methods for identifying the true model across a wide variety of conditions. Whether information-theoretic approaches can also be used not only in combination with maximum likelihood (ML) but also restricted maximum likelihood (REML) estimation was also examined. The results indicate that the conventional methods for model selection may be outperformed by information-theoretic approaches. The latter are more often among the set of best methods across all of the conditions simulated and can have higher probabilities for identifying the true model under particular scenarios. Moreover, their performance based on REML estimation was either very similar to that from ML estimation or at times even better depending on how exactly the REML likelihood was computed. These results suggest that alternative model selection methods should be more widely applied in meta-regression.

Indexed as

Computer SimulationMeta-Analysis as Topicinformation criteriameta-analysismeta-regressionmodel selectionmultimodel inference

Identifiers

PMID33932323
PMCPMC8359854

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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.