Dataset Information

'Arm-based' parameterization for network meta-analysis.

ABSTRACT: We present an alternative to the contrast-based parameterization used in a number of publications for network meta-analysis. This alternative "arm-based" parameterization offers a number of advantages: it allows for a "long" normalized data structure that remains constant regardless of the number of comparators; it can be used to directly incorporate individual patient data into the analysis; the incorporation of multi-arm trials is straightforward and avoids the need to generate a multivariate distribution describing treatment effects; there is a direct mapping between the parameterization and the analysis script in languages such as WinBUGS and finally, the arm-based parameterization allows simple extension to treatment-specific random treatment effect variances. We validated the parameterization using a published smoking cessation dataset. Network meta-analysis using arm- and contrast-based parameterizations produced comparable results (with means and standard deviations being within +/- 0.01) for both fixed and random effects models. We recommend that analysts consider using arm-based parameterization when carrying out network meta-analyses.?© 2015 The Authors Research Synthesis Methods Published by John Wiley & Sons Ltd.

SUBMITTER: Hawkins N

PROVIDER: S-EPMC5063191 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

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'Arm-based' parameterization for network meta-analysis.

Hawkins Neil N Scott David A DA Woods Beth B

Research synthesis methods 20151127 3

We present an alternative to the contrast-based parameterization used in a number of publications for network meta-analysis. This alternative "arm-based" parameterization offers a number of advantages: it allows for a "long" normalized data structure that remains constant regardless of the number of comparators; it can be used to directly incorporate individual patient data into the analysis; the incorporation of multi-arm trials is straightforward and avoids the need to generate a multivariate ...[more]

PMID: 26610409

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Project description:BackgroundThe salience network (SN) is a transitory mediator between active and passive states of mind. Multiple cortical areas, including the opercular, insular, and cingulate cortices have been linked in this processing, though knowledge of network connectivity has been devoid of structural specificity.ObjectiveThe current study sought to create an anatomically specific connectivity model of the neural substrates involved in the salience network.MethodsA literature search of PubMed and BrainMap Sleuth was conducted for resting-state and task-based fMRI studies relevant to the salience network according to PRISMA guidelines. Publicly available meta-analytic software was utilized to extract relevant fMRI data for the creation of an activation likelihood estimation (ALE) map and relevant parcellations from the human connectome project overlapping with the ALE data were identified for inclusion in our SN model. DSI-based fiber tractography was then performed on publicaly available data from healthy subjects to determine the structural connections between cortical parcellations comprising the network.ResultsNine cortical regions were found to comprise the salience network: areas AVI (anterior ventral insula), MI (middle insula), FOP4 (frontal operculum 4), FOP5 (frontal operculum 5), a24pr (anterior 24 prime), a32pr (anterior 32 prime), p32pr (posterior 32 prime), and SCEF (supplementary and cingulate eye field), and 46. The frontal aslant tract was found to connect the opercular-insular cluster to the middle cingulate clusters of the network, while mostly short U-fibers connected adjacent nodes of the network.ConclusionHere we provide an anatomically specific connectivity model of the neural substrates involved in the salience network. These results may serve as an empiric basis for clinical translation in this region and for future study which seeks to expand our understanding of how specific neural substrates are involved in salience processing and guide subsequent human behavior.

Dataset Information

'Arm-based' parameterization for network meta-analysis.

Publications

'Arm-based' parameterization for network meta-analysis.

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