Meta-analysis of binary outcomes via generalized linear mixed models: a simulation study
Abstract Background Systematic 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...
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doaj-727a588fe5254ebf99ef57522d9eb2ec2020-11-25T02:22:47ZengBMCBMC Medical Research Methodology1471-22882018-07-0118111810.1186/s12874-018-0531-9Meta-analysis of binary outcomes via generalized linear mixed models: a simulation studyIlyas Bakbergenuly0Elena Kulinskaya1School of Computing Sciences, University of East AngliaSchool of Computing Sciences, University of East AngliaAbstract Background Systematic 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. Methods The 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. Results In 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. Conclusions It 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.http://link.springer.com/article/10.1186/s12874-018-0531-9Generalized linear mixed-effects modelsRandom effectsHypergeometric-normal likelihoodTransformation biasMeta-analysis |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ilyas Bakbergenuly Elena Kulinskaya |
spellingShingle |
Ilyas Bakbergenuly Elena Kulinskaya Meta-analysis of binary outcomes via generalized linear mixed models: a simulation study BMC Medical Research Methodology Generalized linear mixed-effects models Random effects Hypergeometric-normal likelihood Transformation bias Meta-analysis |
author_facet |
Ilyas Bakbergenuly Elena Kulinskaya |
author_sort |
Ilyas Bakbergenuly |
title |
Meta-analysis of binary outcomes via generalized linear mixed models: a simulation study |
title_short |
Meta-analysis of binary outcomes via generalized linear mixed models: a simulation study |
title_full |
Meta-analysis of binary outcomes via generalized linear mixed models: a simulation study |
title_fullStr |
Meta-analysis of binary outcomes via generalized linear mixed models: a simulation study |
title_full_unstemmed |
Meta-analysis of binary outcomes via generalized linear mixed models: a simulation study |
title_sort |
meta-analysis of binary outcomes via generalized linear mixed models: a simulation study |
publisher |
BMC |
series |
BMC Medical Research Methodology |
issn |
1471-2288 |
publishDate |
2018-07-01 |
description |
Abstract Background Systematic 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. Methods The 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. Results In 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. Conclusions It 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. |
topic |
Generalized linear mixed-effects models Random effects Hypergeometric-normal likelihood Transformation bias Meta-analysis |
url |
http://link.springer.com/article/10.1186/s12874-018-0531-9 |
work_keys_str_mv |
AT ilyasbakbergenuly metaanalysisofbinaryoutcomesviageneralizedlinearmixedmodelsasimulationstudy AT elenakulinskaya metaanalysisofbinaryoutcomesviageneralizedlinearmixedmodelsasimulationstudy |
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