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

Ilyas Bakbergenuly · Elena Kulinskaya

69 citations0/0 files storedBMC Medical Research Methodology

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Meta-analysis and systematic reviews Statistical Methods and Bayesian Inference Statistical Methods in Epidemiology

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.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 69 citations exist upstream.

References

1986Meta-analysis in clinical trials 2010Conducting Meta-Analyses in R with the metafor Package 2008A Re-Evaluation of Random-Effects Meta-Analysis 2005Bias and Efficiency of Meta-Analytic Variance Estimators in the Random-Effects Model 2010Random effects meta-analysis of event outcome in the framework of the generalized linear mixed model with applications in sparse data 2002Issues in the selection of a summary statistic for meta‐analysis of clinical trials with binary outcomes 1996A LIKELIHOOD APPROACH TO META-ANALYSIS WITH RANDOM EFFECTS 2006Confidence intervals for the amount of heterogeneity in meta‐analysis

Cited by

2020Methods for estimating between‐study variance and overall effect in meta‐analysis of odds ratios 2021Performing Meta-analyses with Very Few Studies 2021Meta-analysis with zero-event studies: a comparative study with application to COVID-19 data 2020Comparison of random‐effects meta‐analysis models for the relative risk in the case of rare events: A simulation study 2021Bayesian Methods for Meta-Analyses of Binary Outcomes: Implementations, Examples, and Impact of Priors

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.