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.
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.
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.
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.
The goal of JAGS (Just Another Gibbs Sampler) software is to remedy the short of BUGS software that unable to running on a system besides Microsoft Windows, such as Unix or Linux. JAGS owns independent computing function and formula of Bayesian theory; it is mischaracterized with simple user interface, good system compatibility, smoother operation, and good interactivity with other programming software. However, due to the limitations of lacking function for results data reading and unscrambling and graph plotting, the popularization and application of JAGS software is restricted. Calling JAGS software from R software through R2jags package, rjags package, or runjags package can overcome these limitations. The operating principle of these three packages is calling JAGS software in the framework of the R software, they have similar functional structure and all have easy maneuverability, concise command, perfect function of data reading and unscrambling and graph drawing; however, there are some differences among them in practice. This article introduces how to performing network meta-analysis by calling JAGS software from R through these three packages.
The pcnetmeta package in R is a special package for performing network meta-analysis based on Bayesian theory, which combines the strength computing function of JAGS software and the special data integration and powerful graph drawing function of R software. This package conducts calculation by calling JAGS, provides 3 different models for users, and each model can yield results of 3 effect-sizes (RR, OR and RD). At the same time, this package can draw many kinds of plots, which greatly meets actual needs of users to deal with complicated network meta-analysis. In this article, we introduce how to use pcnetmeta package to perform network meta-analysis based on an example.
The paper presents two statistical methods to compare summary estimates of different subgroups in meta-analysis. It also shows how to use Z test and meta-regression model with dichotomous data and continuous data in R software to explain the similarities and differences between the two statistical methods by examples.
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 method of network meta-analysis of diagnostic test accuracy is in the exploratory stage. We had explored and introduced several methods of network meta-analysis of diagnostic test accuracy before. Based on example, we introduce ANOVA model for performing network meta-analysis of diagnostic test accuracy step-by-step.