Estimation of effect heterogeneity in rare events meta-analysis
Estimation of effect heterogeneity in rare events meta-analysis
The paper outlines several approaches for dealing with meta-analyses of count outcome data. These counts are the accumulation of occurred events, and these events might be rare, so a special feature of the meta-analysis is dealing with low counts including zero-count studies. Emphasis is put on approaches which are state of the art for count data modelling including mixed log-linear (Poisson) and mixed logistic (binomial) regression as well as nonparametric mixture models for count data of Poisson and binomial type. A simulation study investigates the performance and capability of discrete mixture models in estimating effect heterogeneity. The approaches are exemplified on a meta-analytic case study investigating the acceptance of bibliotherapy.
count data analysis, generalised linear mixed models, heterogeneity variance, meta-analysis, nonparametric mixture models, rare events
1081-1102
Holling, Heinz
88d46f56-77ca-4d0e-b035-a51aff735435
Jansen, Katrin
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Böhning, Walailuck
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Böhning, Dankmar
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Martin, Susan
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Sangnawakij, Patarawan
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1 September 2022
Holling, Heinz
88d46f56-77ca-4d0e-b035-a51aff735435
Jansen, Katrin
82129bd2-7903-41de-b9fe-fe341a176d72
Böhning, Walailuck
c0e89cb8-3d21-433c-8ca1-c1ee7c5bdfa6
Böhning, Dankmar
1df635d4-e3dc-44d0-b61d-5fd11f6434e1
Martin, Susan
57c869c0-9a02-473b-ad80-f20d5e6dd363
Sangnawakij, Patarawan
e821a2a7-a89f-4172-9006-8a6c2db9add6
Holling, Heinz, Jansen, Katrin, Böhning, Walailuck, Böhning, Dankmar, Martin, Susan and Sangnawakij, Patarawan
(2022)
Estimation of effect heterogeneity in rare events meta-analysis.
Psychometrika, 87 (3), .
(doi:10.1007/s11336-021-09835-5).
Abstract
The paper outlines several approaches for dealing with meta-analyses of count outcome data. These counts are the accumulation of occurred events, and these events might be rare, so a special feature of the meta-analysis is dealing with low counts including zero-count studies. Emphasis is put on approaches which are state of the art for count data modelling including mixed log-linear (Poisson) and mixed logistic (binomial) regression as well as nonparametric mixture models for count data of Poisson and binomial type. A simulation study investigates the performance and capability of discrete mixture models in estimating effect heterogeneity. The approaches are exemplified on a meta-analytic case study investigating the acceptance of bibliotherapy.
Text
NPMLE_Psychometrika_revised_version_22_10_2021
- Accepted Manuscript
Text
Holling2022_Article_EstimationOfEffectHeterogeneit
- Version of Record
More information
Submitted date: 22 October 2020
Accepted/In Press date: 14 November 2021
e-pub ahead of print date: 8 February 2022
Published date: 1 September 2022
Additional Information:
Funding Information:
This work was supported by Grant HO 1286/16-1 of the German Research Foundation (DFG) to Heinz Holling.
Copyright © 2022, The Author(s)
Keywords:
count data analysis, generalised linear mixed models, heterogeneity variance, meta-analysis, nonparametric mixture models, rare events
Identifiers
Local EPrints ID: 454378
URI: http://eprints.soton.ac.uk/id/eprint/454378
ISSN: 0033-3123
PURE UUID: 4066ef36-1242-4532-be46-306e6661c93e
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Date deposited: 08 Feb 2022 17:42
Last modified: 18 Mar 2024 03:19
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Contributors
Author:
Heinz Holling
Author:
Katrin Jansen
Author:
Walailuck Böhning
Author:
Susan Martin
Author:
Patarawan Sangnawakij
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