A Bayesian approach to discrete multiple outcome network meta-analysis.
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 speci...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2020-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0231876&type=printable |
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| author | Rebecca Graziani Sergio Venturini |
| author_facet | Rebecca Graziani Sergio Venturini |
| author_sort | Rebecca Graziani |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9525c86605f241d696562f3ab8e889ec |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-9525c86605f241d696562f3ab8e889ec2025-01-24T05:31:13ZengPublic Library of Science (PLoS)PLoS ONE1932-62032020-01-01154e023187610.1371/journal.pone.0231876A Bayesian approach to discrete multiple outcome network meta-analysis.Rebecca GrazianiSergio VenturiniIn 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.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0231876&type=printable |
| spellingShingle | Rebecca Graziani Sergio Venturini A Bayesian approach to discrete multiple outcome network meta-analysis. PLoS ONE |
| title | A Bayesian approach to discrete multiple outcome network meta-analysis. |
| title_full | A Bayesian approach to discrete multiple outcome network meta-analysis. |
| title_fullStr | A Bayesian approach to discrete multiple outcome network meta-analysis. |
| title_full_unstemmed | A Bayesian approach to discrete multiple outcome network meta-analysis. |
| title_short | A Bayesian approach to discrete multiple outcome network meta-analysis. |
| title_sort | bayesian approach to discrete multiple outcome network meta analysis |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0231876&type=printable |
| work_keys_str_mv | AT rebeccagraziani abayesianapproachtodiscretemultipleoutcomenetworkmetaanalysis AT sergioventurini abayesianapproachtodiscretemultipleoutcomenetworkmetaanalysis AT rebeccagraziani bayesianapproachtodiscretemultipleoutcomenetworkmetaanalysis AT sergioventurini bayesianapproachtodiscretemultipleoutcomenetworkmetaanalysis |