GeMTC R package: model generation for network meta-analysis

GPU enabled Many-Task Computing (GeMTC)

Web interface for network meta-analysis based on the gemtc R package

R package that provides models and functions to fit non-standard NMAs: piecewise exponential models and fractional polynomial models for grouped survival data (derived from digitized Kaplan-Meier curves).

Framework supporting the execution of Many-Task Computing on GPUs

Network meta-analysis (NMA) has been increasingly adopted in evidence-based medicine to compare multiple interventions and address the questions such as ‘which intervention is the ‘best’ overall?’. Currently, NMA is primarily conducted in statistical packages such as WinBUGs, R and STATA, and the software coding can be difficult for non- statisticians and hinders the progress of carrying out systematic reviews containing NMA. MetaInsight is a tool that conducts NMA via the web requiring no specialist software for the user to install but leveraging established analysis routines (specifically the netmeta and gemtc package in R). The tool is interactive and uses an intuitive ‘point and click’ interface and presents results in visually intuitive and appealing ways. It is hoped that this tool will assist those in conducting NMA who are not statistical experts, and, in turn, increase the relevance of published meta-analyses, and in the long term contribute to improved healthcare decision making as a result. 

:exclamation: This is a read-only mirror of the CRAN R package repository. gemtc — Network Meta-Analysis Using Bayesian Methods. Homepage: https://github.com/gertvv/gemtc

GemTcl is a cross platform TclShell written in Gtk

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Config files for my GitHub profile.

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User interface for network meta-analysis based on the gemtc R package and Patavi

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:exclamation: This is a read-only mirror of the CRAN R package repository. gemtc.jar — GeMTC Java binary

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Framework similar to GeMTC using OpeMP for Xeon Phi

Bayesian NMA of advanced NSCLC treatments across 10 studies using gemtc and JAGS — fixed/random effects models, DIC model selection, SUCRA rankings, full league table, node-splitting consistency check, and heterogeneity estimation for NICE STA submissions.