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 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.
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 mada package is a type of package that is especially used for implementing meta-analysis of diagnostic accuracy tests. This package is developed on basis of classical statistical theories and it can be used to calculate all relevant effect size of diagnostic accuracy tests; however, it does not provide pooled values of sensitivity and specificity. This article uses an example to introduce the whole functions of mada package in implementing meta-analysis of diagnostic accuracy tests, including data preparation, calculation implementation, result summary, and plots drawing.
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 "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.
Network plots can clearly present the relationships among the direct comparisons of various interventions in a network meta-analysis. Currently, there are some methods of drawing network plots. However, the information provided by a network plot and the interface-friendly degree to a user differ in the kinds of software. This article briefly introduces how to draw network plots using the network package and gemtc package that base on R Software, Stata software, and ADDIS software, and it also compares the similarities and differences among them.
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.