Editing Meta-analysis (section)

=====Bayesian framework=====
Specifying a Bayesian network meta-analysis model involves writing a directed acyclic graph (DAG) model for general-purpose [[Markov chain Monte Carlo]] (MCMC) software such as WinBUGS.<ref name="Valkenhoef, G. 2012">{{cite journal | vauthors = van Valkenhoef G, Lu G, de Brock B, Hillege H, Ades AE, Welton NJ | title = Automating network meta-analysis | journal = Research Synthesis Methods | volume = 3 | issue = 4 | pages = 285–299 | date = December 2012 | pmid = 26053422 | doi = 10.1002/jrsm.1054 | s2cid = 33613631 }}</ref> In addition, prior distributions have to be specified for a number of the parameters, and the data have to be supplied in a specific format.<ref name="Valkenhoef, G. 2012"/> Together, the DAG, priors, and data form a Bayesian hierarchical model. To complicate matters further, because of the nature of MCMC estimation, overdispersed starting values have to be chosen for a number of independent chains so that convergence can be assessed.<ref>{{cite journal |vauthors=Brooks SP, Gelman A | year = 1998 | title = General methods for monitoring convergence of iterative simulations | url = http://www.stat.columbia.edu/~gelman/research/published/brooksgelman2.pdf| journal = Journal of Computational and Graphical Statistics | volume = 7 | issue = 4| pages = 434–455 | doi=10.1080/10618600.1998.10474787| s2cid = 7300890 }}</ref> Recently, multiple [[R (programming language)|R]] software packages were developed to simplify the model fitting (e.g., ''metaBMA''<ref>{{Cite web | vauthors = Heck DW, Gronau QF, Wagenmakers EJ, Patil I |title=metaBMA: Bayesian model averaging for random and fixed effects meta-analysis |url=https://CRAN.R-project.org/package=metaBMA |access-date=9 May 2022 |website=CRAN|date=17 March 2021 }}</ref> and ''RoBMA''<ref>{{Cite web | vauthors =  Bartoš F, Maier M, Wagenmakers EJ, Goosen J, Denwood M, Plummer M |title=RoBMA: An R Package for Robust Bayesian Meta-Analyses |date=20 April 2022 |url=https://CRAN.R-project.org/package=RoBMA |access-date=9 May 2022}}</ref>) and even implemented in statistical software with graphical user interface ([[Graphical user interface|GUI]]): [[JASP]]. Although the complexity of the Bayesian approach limits usage of this methodology, recent tutorial papers are trying to increase accessibility of the methods.<ref>{{Cite journal | vauthors = Gronau QF, Heck DW, Berkhout SW, Haaf JM, Wagenmakers EJ |date=July 2021 |title=A Primer on Bayesian Model-Averaged Meta-Analysis |journal=Advances in Methods and Practices in Psychological Science |language=en |volume=4 |issue=3 |pages= |doi=10.1177/25152459211031256 |s2cid=237699937 |issn=2515-2459|doi-access=free |hdl=11245.1/ec2c07d1-5ff0-431b-b53a-10f9c5d9541d |hdl-access=free }}</ref><ref>{{Cite journal | vauthors = Bartoš F, Maier M, Quintana D, Wagenmakers EJ |date=2020-10-16 |title=Adjusting for Publication Bias in JASP & R - Selection Models, PET-PEESE, and Robust Bayesian Meta-Analysis | journal = Advances in Methods and Practices in Psychological Science |url=https://osf.io/75bqn |doi=10.31234/osf.io/75bqn |s2cid=236826939 |doi-access=free |hdl=11245.1/5540e87c-0883-45e6-87de-48d2bf4c1e1d |hdl-access=free }}</ref> Methodology for automation of this method has been suggested<ref name="Valkenhoef, G. 2012" /> but requires that arm-level outcome data are available, and this is usually unavailable. Great claims are sometimes made for the inherent ability of the Bayesian framework to handle network meta-analysis and its greater flexibility. However, this choice of implementation of framework for inference, Bayesian or frequentist, may be less important than other choices regarding the modeling of effects<ref name="ReferenceC">{{cite journal | vauthors = Senn S, Gavini F, Magrez D, Scheen A | title = Issues in performing a network meta-analysis | journal = Statistical Methods in Medical Research | volume = 22 | issue = 2 | pages = 169–189 | date = April 2013 | pmid = 22218368 | doi = 10.1177/0962280211432220 | s2cid = 10860031 }}</ref> (see discussion on models above).