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 evidence-based practice and decision, dose-response meta-analysis has been concerned by many scholars. It can provide unique dose-response relationship between exposure and disease, with a high grade of evidence among observational-study based meta-analysis. Thus, it is important to clearly understand this type of meta-analysis on software implementations. Currently, there are different software for dose-response meta-analysis with various characteristics. In this paper, we will focus on how to conduct dose-response meta-analysis by Stata, R and SAS software, which including a brief introduction, the process of calculation, the graph drawing, the generalization, and some examples of the processes.
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.
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.
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.
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.
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.
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.