Comment on ‘A vine copula mixed effect model for trivariate meta-analysis of diagnostic test accuracy studies accounting for disease prevalence’

@article{Nikoloulopoulos2016CommentO,
  title={Comment on ‘A vine copula mixed effect model for trivariate meta-analysis of diagnostic test accuracy studies accounting for disease prevalence’},
  author={Aristidis K. Nikoloulopoulos},
  journal={Statistical Methods in Medical Research},
  year={2016},
  volume={25},
  pages={988 - 991}
}
A bivariate copula mixed model has been recently proposed to synthesize diagnostic test accuracy studies and it has been shown that it is superior to the standard generalized linear mixed model in this context. Here, we call trivariate vine copulas to extend the bivariate meta-analysis of diagnostic test accuracy studies by accounting for disease prevalence. Our vine copula mixed model includes the trivariate generalized linear mixed model as a special case and can also operate on the original… 
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References

SHOWING 1-10 OF 49 REFERENCES
A vine copula mixed effect model for trivariate meta-analysis of diagnostic test accuracy studies accounting for disease prevalence
TLDR
This study suggests that there can be an improvement on trivariate generalized linear mixed model in fit to data and makes the argument for moving to vine copula random effects models especially because of their richness, including reflection asymmetric tail dependence, and computational feasibility despite their three dimensionality.
Comment on ‘A vine copula mixed effect model for trivariate meta-analysis of diagnostic test accuracy studies accounting for disease prevalence’ by Aristidis K Nikoloulopoulos
  • A. Hoyer, O. Kuss
  • Computer Science
    Statistical methods in medical research
  • 2016
TLDR
The HK model is proposed for the meta-analysis of diagnostic accuracy studies accounting for disease prevalence with modelling random effects by vine copulas and results from the HK model are frequently better than those from the standard trivariate generalized linear mixed model (GLMM).
Meta-analysis of diagnostic tests accounting for disease prevalence: a new model using trivariate copulas.
TLDR
A new model is proposed using trivariate copulas and beta-binomial marginal distributions for sensitivity, specificity, and prevalence as an expansion of the bivariate model.
A mixed effect model for bivariate meta-analysis of diagnostic test accuracy studies using a copula representation of the random effects distribution.
TLDR
This study suggests that there can be an improvement on GLMM in fit to data and makes the argument for moving to copula random effects models.
Meta-analysis for diagnostic accuracy studies: a new statistical model using beta-binomial distributions and bivariate copulas.
TLDR
A new statistical model is proposed for the meta-analysis for diagnostic accuracy studies that uses beta-binomial distributions for the marginal numbers of true positives and true negatives and links these margins by a bivariate copula distribution.
Meta-analysis of diagnostic accuracy studies accounting for disease prevalence: alternative parameterizations and model selection.
TLDR
The proposed trivariate random effects models are novel and can be very useful in practice for meta-analysis of diagnostic accuracy studies and allow investigators to study the complex relationship among the disease prevalence, sensitivities and specificities.
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TLDR
A bivariate Beta-binomial model derived from Sarmanov family of bivariate distributions and a bivariate generalised linear mixed effects model for binary clustered data to make valid inferences are discussed.
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TLDR
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TLDR
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