Bayesian methods in meta-analysis and evidence synthesis.
By A. Sutton, K. Abrams. Statistical Methods in Medical Research, 2001
445 citations recorded by the source corpus.
Subfields: Statistics and Probability, Surgery
View Paper: https://www.semanticscholar.org/paper/86eb1845f20723dc15aeb029b7217577e2051a5f
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Statistics and Probability
- A. Sutton
- Daniel W. Heck
- Luke Plonsky
- Quentin F. Gronau
- K. Abrams
- David R. Jones
- N. Hawkins
- Annabel Barrett
In Surgery
Citations in the corpus
- A Bayesian model-averaged meta-analysis of the power pose effect with informed and default priors: the case of felt power (2017, methodology): “Concretely, Bayesian model-averaging allows us to obtain a model-averaged estimate for the meta-analytic effect size (Sutton & Abrams, 2001) and to quantify the overall evidence for an effect that considers both the fixedeffect and random-effect model (Scheibehenne, Gronau, Jamil, & Wagenmakers,…”
- Domain definition and search techniques in meta-analyses of L2 research (Or why 18 meta-analyses of feedback have different results) (2015, background): “(Although outside the scope of the present article, see Ross, 2013, and Sutton and Abrams, 2001, for illustrations of how a Bayesian approach can be applied to future meta-analyses of L2 research.)”
- How vague is vague? A simulation study of the impact of the use of vague prior distributions in MCMC using WinBUGS (2005, background): “A simple two-level hierarchical model can be (cid:1)tted [25].”
- Heat Effects on Mortality in 15 European Cities (2008, methodology): “To produce regional averages of city-specific estimates of the effect parameters, we specified Bayesian hierarchical models.(26) Let ̂1, .”
- Conducting indirect-treatment-comparison and network-meta-analysis studies: report of the ISPOR Task Force on Indirect Treatment Comparisons Good Research Practices: part 2. (2011, background): “(roughly, the probability of the data as a function of the parameters) with a prior probability distribution (which reflects prior belief about possible values of those parameters) to obtain a posterior probability distribution of the parameters [17].”