The theoretical foundation of relevant packages of R software for network meta-analysis is mainly based on Bayesian statistical model and a few of them use generalized linear model. Network meta-analysis is performed using GeMTC R package through calling the corresponding rjags package, BRugs package, or R2WinBUGS package (namely, JAGS, OpenBUGS, and WinBUGS software, respectively). Meanwhile, GeMTC R package can generate data storage files for GeMTC software. Techonically, network meta-analysis is performed through calling the software based on Markov Chain Monte Carlo method. In this article, we briefly introduce how to use GeMTC R package to perform network meta-analysis through calling the OpenBUGS software.
R software is a free and powerful statistical tool, including Metafor, Meta as well as Rmeta packages, all of which could conduct meta-analysis. Metafor package provides functions for meta-analyses which include analysis of continuous and categorical data, meta-regression, cumulative meta-analysis as well as test for funnel plot asymmetry. The package can also draw various plots, such as forest plot, funnel plot, radial plot and so forth. Mixed-effects models (involving single or multiple categorical and/or continuous moderates) can only be fitted with Metafor packages. Advanced methods for testing model coefficients and confidence intervals are also implemented only in this package. This article introduces detailed operation steps of Metafor package for meta-analysis using cases.
ObjectiveTo compare the characteristics and functions of the network meta-analysis software and for providing references for users.
MethodsPubMed, CNKI, official website of Stata and R, and Google were searched to collect the software and packages that can perform network meta-analysis up to July 2014. After downloading the software, packages, and their user guides, we used the software and packages to calculate a typical example. The characteristics, functions, and computed results were compared and analyzed.
ResultsFinally, 11 types of software were included, including programming and non-programming software. They were developed mainly based on Bayesian or Frequentist. Most types of software have the characteristics of easy to operate, easy to master, exactitude calculation, or good graphing; however, there is no software that has the exactitude calculation and good graphing at the same time, which needs two or more kinds of software combined to achieve.
ConclusionWe suggest the user to choose the software at least according to personal programming basis and custom; and the user can consider to choose two or more kinds of software combined to finish the objective network meta-analysis. We also suggest to develop a kind of software which is characterized of fully function, easy operation, and free.
R software is a free, powerful statistical and graphing software, including metafor, meta as well as metaplus packages. They can be used to conduct meta-analysis. This article introduces detailed operations of the metaplus package for meta-analysis using cases.
In systematic reviews and meta-analyses, time-to-event outcomes were mostly analysed using hazard ratios (HR). It was neglected transformation of the data so that some wrong outcomes were gained. This study introduces how to use Stata and R software to calculate the HR correctly if the report presents HR and confidence intervals were gained.
In evidence-based practice and decision, dose-response meta-analysis has been concerned by many scholars. It can provide unique dose-response relationship between exposure and disease, with a high grade of evidence among observational-study based meta-analysis. Thus, it is important to clearly understand this type of meta-analysis on software implementations. Currently, there are different software for dose-response meta-analysis with various characteristics. In this paper, we will focus on how to conduct dose-response meta-analysis by Stata, R and SAS software, which including a brief introduction, the process of calculation, the graph drawing, the generalization, and some examples of the processes.
ObjectiveTo introduce the method of meta-analysis for effect combination of regression coefficient conducted with the metafor package in R software.
MethodsBy using the data of a published meta-analysis as an example, the detailed process of meta-analysis for regression coefficient was presented with metafor package in R.
ResultsThe results of meta-analysis conducted with metaphor package in R were the same as the published literature.
ConclusionAs a completely free open source software for statistical analysis, R can conduct meta-analysis for effect combination of regression coefficient flexibly and precisely, and should be expanded in the future meta-analysis.
The "bnma" package is a Bayesian network meta-analysis software package developed based on the R programming language. The network meta-analysis was performed utilizing JAGS software, which yielded relevant results and visual graphs. Moreover, this software package provides support for various data structures and types, while also providing the advantages of flexible utilization, user-friendly operation, and deliver of rich and accurate outcomes. In this paper, using a network meta-analysis example of different therapies for androgenetic alopecia, the operational process of conducting network meta-analysis using the "bnma" package is briefly introduced.
Meta-analysis of survival data is becoming more and more popular. The data could be extracted from the original literature, such as hazard ratio (HR) and its 95% confidence interval, the difference of actual frequency and theoretical frequency (O - E) and its standard deviation. The data can be used to calculate the combined HR using Review Manager (RevMan), Stata and R softwares. RevMan software is easy to learn, but there are some limitations. Stata and R software are powerful and flexible, and they are able to draw a variety of graphics, however, they need to be programmed to achieve.
The metacor, which is developed based on the classical frequentist theory, is a specified package for performing meta-analysis of correlation coefficients in R software. This package was officially launched in 2011. Based on the DerSimonian-Laird method and Olkin-Pratt method, correlation coefficients can be directly pooled by using this package. The metacor package also can be used to draw the forest plot and is easy to use; however, it still needs to be improved. This paper briefly introduced how to perform a meta-analysis of correlation coefficients using the metacor package in R software through an example.
