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.
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 published methodological studies about network meta-analysis mostly focused on the binary variables, but study focused on the continuous variables was few. This study introduces how to use R, GeMTC and Stata softwares jointly to produce various graphics of continuous variable network meta-analysis. It also introduces how to perform the convergence diagnostics, trace and density plot, forest, rank probabilities and rankogram, internal relationship summary chart, network plot, contribution plot and publication bias test.
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.
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.
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.
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.
The association between single nucleotide polymorphism and disease is a typical representation of genetic association studies. Compared with the traditional dichotomous data, single nucleotide polymorphism data has its own characteristics, and 5 genetic models are commonly performed in meta-analysis. In this paper, we show how to use the " meta” package in R software to conduct meta-analysis of single nucleotide polymorphism research through examples.
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.
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.