Projects per year
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.
- Generalized linear mixed-effects models
- Random effects
- Hypergeometric-normal likelihood
- Transformation bias
- School of Computing Sciences - Professor in Statistics (AVIVA)
- Business and Local Government Data Research Centre - Member
- Data Science and Statistics - Member
Person: Research Group Member, Research Centre Member, Academic, Teaching & Research
- 1 Finished
Hancock, R., Sena, V., Coakley, J., Cornford, J., De La Iglesia, B., Fasli, M., Fearne, A., Forder, J., Harwood, A., Hviid, M., Jones, A., Kulinskaya, E., Laurie, H., Lovett, A., Schofield, G., Appleton, K., Morciano, M. & Sunnenberg, G.
31/01/14 → 31/10/20