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A Bayesian approach to discrete multiple outcome network meta-analysis

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  • Rebecca Graziani
  • Sergio Venturini

Abstract

In this paper we suggest a new Bayesian approach to network meta-analysis for the case of discrete multiple outcomes. The joint distribution of the discrete outcomes is modeled through a Gaussian copula with binomial marginals. The remaining elements of the hierarchial random effects model are specified in a standard way, with the logit of the success probabilities given by the sum of a baseline log-odds and random effects comparing the log-odds of each treatment against the reference and having a Gaussian distribution centered at the vector of pooled effects. An adaptive Markov Chain Monte Carlo algorithm is devised for running posterior inference. The model is applied to two datasets from Cochrane reviews, already analysed in two papers so to assess and compare its performance. We implemented the model in a freely available R package called netcopula.

Suggested Citation

  • Rebecca Graziani & Sergio Venturini, 2020. "A Bayesian approach to discrete multiple outcome network meta-analysis," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-17, April.
    • Handle: RePEc:plo:pone00:0231876
    DOI: 10.1371/journal.pone.0231876
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    References listed on IDEAS

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    1. Han Chen & Alisa K. Manning & Josée Dupuis, 2012. "A Method of Moments Estimator for Random Effect Multivariate Meta-Analysis," Biometrics, The International Biometric Society, vol. 68(4), pages 1278-1284, December.
    2. Peter J. Danaher & Michael S. Smith, 2011. "Modeling Multivariate Distributions Using Copulas: Applications in Marketing," Marketing Science, INFORMS, vol. 30(1), pages 4-21, 01-02.
    3. Michael Pitt & David Chan & Robert Kohn, 2006. "Efficient Bayesian inference for Gaussian copula regression models," Biometrika, Biometrika Trust, vol. 93(3), pages 537-554, September.
    4. Michael S. Smith & Mohamad A. Khaled, 2012. "Estimation of Copula Models With Discrete Margins via Bayesian Data Augmentation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 290-303, March.
    5. Stacia M. DeSantis & Huirong Zhu, 2014. "A Bayesian Mixed-Treatment Comparison Meta-analysis of Treatments for Alcohol Dependence and Implications for Planning Future Trials," Medical Decision Making, , vol. 34(7), pages 899-910, October.
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