Abstract
BackgroundSeveral reviews have noted shortcomings regarding the quality and reporting of network meta-analyses (NMAs). We suspect that this issue may be partially attributable to limitations in current NMA software which do not readily produce all of the output needed to satisfy current guidelines.ResultsTo better facilitate the conduct and reporting of NMAs, we have created an R package called “BUGSnet” (Bayesian inference Using Gibbs Sampling to conduct a Network meta-analysis). This R package relies upon Just Another Gibbs Sampler (JAGS) to conduct Bayesian NMA using a generalized linear model. BUGSnet contains a suite of functions that can be used to describe the evidence network, estimate a model and assess the model fit and convergence, assess the presence of heterogeneity and inconsistency, and output the results in a variety of formats including league tables and surface under the cumulative rank curve (SUCRA) plots. We provide a demonstration of the functions contained within BUGSnet by recreating a Bayesian NMA found in the second technical support document composed by the National Institute for Health and Care Excellence Decision Support Unit (NICE-DSU). We have also mapped these functions to checklist items within current reporting and best practice guidelines.ConclusionBUGSnet is a new R package that can be used to conduct a Bayesian NMA and produce all of the necessary output needed to satisfy current scientific and regulatory standards. We hope that this software will help to improve the conduct and reporting of NMAs.
Highlights
Several reviews have noted shortcomings regarding the quality and reporting of network meta-analyses (NMAs)
The net.tab() function produces descriptive tables that are based on the tables produced by NetMetaXL – an excel-based software for conducting Bayesian NMAs [16]
We have recreated an analysis of a dichotomous outcome where studies had variable follow-up times described in the NICE-DSU technical support document 2 [17]
Summary
Several reviews have noted shortcomings regarding the quality and reporting of network meta-analyses (NMAs). Unlike traditional pairwise meta-analysis, ITC/NMA can incorporate indirect evidence that arises when a group of studies evaluating different treatments share a common comparator. The incorporation of such evidence within an NMA has several advantages over pairwise meta-analysis [1, 2]. The number of publications using NMA has increased dramatically within the past decade [3] Despite this increase, several reviews have noted shortcomings with respect to the quality of the conduct and reporting of NMAs [4,5,6,7,8,9].
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