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.
Stata is statistical software that combines programming and un-programming, which is easy to operate, of high efficiency and good expansibility. In performing meta-analysis, Stata software also presents powerful function. The mvmeta package of Stata software is based on a multiple regression model to conduct network meta-analysis, and it also processes "multiple outcomes-multivariate" data. Currently, the disadvantages of mvmeta package include relatively cumbersome process, poor interest-risk sorting, and lack of drawing function in the process of conducting network meta-analysis. In this article, we introduce how to implement network meta-analysis using this package based on cases.
ITC (Indirect Treatment Comparison) software and indirect procedure of Stata software are especially used for indirect comparison nowadays, both of which possess the characteristics of friendly concise interface and support for menu operation. ITC software needs the application of other software to yield effect estimation and its confidence interval of direct comparison firstly; while Stata-indirect procedure can complete direct comparison internally and also operate using commands, which simplifies complicated process of indirect comparison. However, both of them only perform "single-pathway" of data transferring and pooling, which is a common deficiency. From the results, their results are of high-degree similarity.
Trial sequential analysis (TSA) could be performed in both TSA software and Stata software. The implementation process of TSA in Stata needs the command of "metacumbounds" of Stata combines with the packages of "foreign" and "ldbounds" of R software. This paper briefly introduces how to implement TSA using Stata software.
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.
This article introduces two methods used to calculate effect indicators and their standard errors with non-comparative binary data. Then we give an example, the effect indicators and standard errors are calculated using both methods, and meta-analysis with the outcomes is conducted using RevMan software. At last the calculated results are compared with the results of meta-analysis conducted using Stata software with original data based on cases. The results of meta-analysis performed in RevMan software and Stata software are consistent in calculating non-comparative binary data.
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.
Most statistical data in observational studies is expressed as the effect value and its 95% confidence interval (95% CI), which do not correspond to the data format used for traditional meta-analyses, so special data conversion is to be needed when Review Manager software is applied to do a meta-analysis for this type of data, which will make the operation complicated and cumbersome. In addition, Stata software is such a powerful statistical software that can be used directly to conduct a meta-analysis with the effect value and its 95% CI. Therefore, it is an indispensable statistical tool for meta-analysis in observational studies. And this study will give a brief introduction how to use Stata software to conduct a meta-analysis with effect value and its 95% CI based on the published meta-analysis data.
Network meta-analysis may be performed by fitting multivariate meta-analysis models with Stata software mvmeta command; however, there are various challenges such as preprocessing the data, parameterising the model, and making good graphical displays of results. A suite of Stata programs, network, may meet these challenges. In this article, we introduce how to use the network commands to implement network meta-analysis by the example of continuous data.
Network meta-regression model can be used to account for important effect modifiers that might have impact on the treatment effects, and it can be performed within a frequentist or Bayesian framework. This study introduces how to use the mvmeta command in Stata software to implement network meta-regression within frequentist framework and briefly introduces the application of network meta-regression.