Book Chapter10.1201/9781315180212-14
Bayesian Methods for Meta-Analysis
Nicky J Welton,Hayley E Jones,Sofia Dias +2 more
- 15 Apr 2020
- pp 273-300
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About: The article was published on 15 Apr 2020.
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Citations
On weakly informative prior distributions for the heterogeneity parameter in Bayesian random-effects meta-analysis.
Christian Röver,Ralf Bender,Sofia Dias,Christopher H. Schmid,Heinz Schmidli,Sibylle Sturtz,Sebastian Weber,Tim Friede +7 more
TL;DR: In this article, a consensus on how to specify a weakly informative heterogeneity prior is provided. But this consensus is limited to only few studies contributing to the meta-analysis, and it requires the sensible specification of prior distributions.
References
The interpretation of random-effects meta-analysis in decision models.
TL;DR: The authors’ analysis suggests that the mean treatment effect from a random-effects meta-analysis will only seldom be an appropriate representation of the efficacy expected in a future implementation, and modelers should consider either the predictive distribution of a future treatment effect or the future implementation itself.
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Comparative diagnostic performance of three radiological procedures for the detection of lumbar disk herniation.
TL;DR: Based on the available, retrospective data, phlebography appears to have the highest performance in visualizing an LDH, followed by myelography and CT, and homogeneity of sensitivity turns out to be much more likely than a common odds ratio.
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Network meta-analysis of multiple outcome measures accounting for borrowing of information across outcomes
Felix A. Achana,Nicola J. Cooper,Sylwia Bujkiewicz,Stephanie J. Hubbard,Denise Kendrick,David R. Jones,Alex J. Sutton +6 more
TL;DR: This work applies multivariate NMA methods to binary outcome data from a systematic review evaluating the effectiveness of nine home safety interventions on uptake of three poisoning prevention practices in households with children, and makes use of the additional assumption that intervention effects are exchangeable between outcomes to predict effect estimates.