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Abstract
Network metaanalysis (NMA) extends pairwise metaanalysis to synthesise evidence on multiple treatments of interest from a connected network of studies. Standard pairwise and network metaanalysis methods combine aggregate data from multiple studies, assuming that any factors that interact with treatment effects (effect modifiers) are balanced across populations. Population adjustment methods aim to relax this assumption by adjusting for differences in effect modifiers. The "gold standard" approach is to analyse individual patient data (IPD) from every study in a metaregression model; however, such levels of data availability are rare. Multilevel network metaregression (MLNMR) is a recent method that generalises NMA to synthesise evidence from a mixture of IPD and aggregate data studies, whilst avoiding aggregation bias and noncollapsibility bias, and can produce estimates relevant to a decision target population.
We introduce a new R package, multinma: a suite of tools for performing MLNMR and NMA with IPD, aggregate data, or mixtures of both, for a range of outcome types. The package includes functions that streamline the setup of NMA and MLNMR models; perform model fitting and facilitate diagnostics; produce posterior summaries of relative effects, rankings, and absolute predictions; and create flexible graphical outputs that leverage ggplot and ggdist. Models are estimated in a Bayesian framework using the stateofthe art Stan sampler.
We introduce a new R package, multinma: a suite of tools for performing MLNMR and NMA with IPD, aggregate data, or mixtures of both, for a range of outcome types. The package includes functions that streamline the setup of NMA and MLNMR models; perform model fitting and facilitate diagnostics; produce posterior summaries of relative effects, rankings, and absolute predictions; and create flexible graphical outputs that leverage ggplot and ggdist. Models are estimated in a Bayesian framework using the stateofthe art Stan sampler.
Original language  English 

Publication status  Unpublished  21 Jan 2021 
Event  Evidence Synthesis and MetaAnalysis in R Conference 2021  Online Duration: 21 Jan 2021 → 22 Jan 2021 https://www.eshackathon.org/events/202101ESMAR.html 
Conference
Conference  Evidence Synthesis and MetaAnalysis in R Conference 2021 

Abbreviated title  ESMARConf 2021 
Period  21/01/21 → 22/01/21 
Internet address 
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Dive into the research topics of 'multinma: An R package for Bayesian network metaanalysis of individual and aggregate data'. Together they form a unique fingerprint.Activities
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Evidence Synthesis and MetaAnalysis in R Conference 2021
David M Phillippo (Speaker)
21 Jan 2021 → 22 Jan 2021Activity: Participating in or organising an event types › Participation in conference