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.
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.
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.
The correlation coefficient is a common used statistic index in the management science, sociology, psychology and nursing. The meta-analysis based on data of correlation coefficients has increased nowadays. The meta package and metafor package are the two major packages in R for performing meta-analysis and can implement many types of meta-analysis, including the meta-analysis of correlation coefficients. This article gives a brief introduction of the process to perform meta-analysis of correlation coefficients using these two packages, and compares their statistical results and functions (such as plot drawing).
BUGSnet is a powerful R project package for Bayesian network meta-analysis. The package is based on JAGS and enables high-quality Bayesian network meta-analysis according to recognized reporting guidelines (PRISMA, ISPOR-AMPC-NCA and NICE-DSU). In this paper, we introduced the procedure of the BUGSnet package for Bayesian network meta-analysis through an example of network meta-analysis of steroid adjuvant treatment of pemphigus with continuous or dichotomous data.
R Software is an open, free of use and charge statistical software which has a powerful graphic capability; however, it requires more complex codes and commands to perform network meta-analysis, which causes errors and difficulties in operation. WinBUGS software is based on Bayesian theory, which has a powerful data processing capability, and especially its codes are simple and easy to operate for dealing with network meta-analysis. However, its function of illustrating statistical results is very poor. In order to fully integrate the advantages of R software and WinBUGS software, an R2WinBUGS package based on R software has been developed which builds a “bridge” across two of them, making network meta-analysis process conveniently, quickly and result illustration more beautiful. In this article, we introduced how to use the R2WinBUGS package for performing network meta-analysis using examples.
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.
ObjectiveTo introduce sensitivity and homogeneity tests in network meta-analysis and its implementation in R software.
MethodsUsing an example, we performed sensitivity analysis by comparing the random effect model with the fixed effect model. Homogeneity analysis was performed using metaphor package and combinat package in R software.
ResultsThe results of the two models were similar, and the data was steady. The results of homogeneity analysis showed that the confidential intervals in all loops were crossed over with blank value; and direct and indirect estimates of the effects in network meta-analysis were not significantly different, with good homogeneity.
ConclusionNetwork meta-analysis is a kind of indirect comparison analysis method, and its sensitivity is especially important. The introduction of homogeneity makes network meta-analysis more accurate. Using R software for sensitivity and homogeneity analysis in network meta-analysis is a feasible method.
R software is a free and powerful statistical software, which has the functions of data processing and graphics drawing and is widely used in the meta-analysis. This article introduced detailed operation steps of rmeta package which is suitable for meta-analysis of binary data, and also can finish cumulative meta-analysis, draw standard summary plots, forest plot and funnel plot. In summary, the functions of rmeta package are simple and easy to mastery. There are some limitations of current version which need to be improved in the future.
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.
