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.
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.
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.
Objective To introduce how to use Stata software to implement the randomization of clinical trials. Methods Some examples were taken to describe how to implement the randomization of each kind of clinical trials. Results Stata implemented its required functions, such as simple randomization, stratified randomization, block randomization and the randomization of group treatment. Conclusion Stata can easily implement the randomization of clinical trials.
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.
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.
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.
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.
Dose-response meta-analysis is being increasingly applied in evidence production and clinical decision. The research method, synthesizing certain dose-specific effects across studies with the same target question by a certain types of weighting schedule to get a mean dose-response effect, is to reflect the dose-response relationship between certain exposure and outcome. Currently, the most popular method for dose-response meta-analysis is based on the classical "two-stage approach", with the advantage that it allows fixed- or random-effect model, according to the amount of heterogeneity in the model. There are two types of random-effect model available for dose-response meta-analysis, that is, the generally model and the coefficient-correlation-adjusted model. In this article, we briefly introduce two models and illustrate how they are applied in Stata software, which is expected to provide theoretical foundation for evidence-based practice.
