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.
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.
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.
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.
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.
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.
The WinBUGS software can be called from either R (provided R2WinBUGS as an R package) or Stata software for network meta-analysis. Unlike R, Stata software needs to create relevant ADO scripts at first which simplify operation process greatly. Similar with R, Stata software also needs to load another package when drawing network plots. This article briefly introduces how to implement network meta-analysis using Stata software by calling WinBUGS software.
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.
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.