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.
ObjectiveTo explore the application of enhanced funnel plots (EFP) and trial sequential analysis (TSA) in robustness assessment of meta-analysis results.MethodsData were extracted from published meta-analysis. The EFP was used to evaluate the robustness of the significance and heterogeneity of the current meta-analysis. The TSA was used to judge the sufficiency of the cumulative sample size of the current meta-analysis and to assess the robustness of conclusions based on current evidence.ResultsThe EFP showed that the meta-analysis results of low-density lipoprotein (LDL) was robust, and the meta-analysis results of triglyceride (TG), total cholesterol (TC) and high-density lipoprotein (HDL) were not stable. The TSA showed that the cumulative sample size of LDL had reached the required information size (RIS), and the current conclusion was stable. The cumulative Z value of TG, TC and HDL neither reached the RIS nor passed through the TSA monitoring boundary or futility boundary, indicating that current conclusions were not robust.ConclusionsThe combination of EFP and TSA can make a comprehensive judgment on the robustness of current meta-analysis results, and provide methodological support in the robustness assessment of results for future systematic reviews and meta-analyses.
The robustness of results of statistical analysis would be altered on the condition of repeated update of traditional meta-analysis and cumulative meta-analysis. In addition, the cumulative meta-analysis lacks estimation of the sample size. While trail sequential analysis (TSA), which introduces group sequential analysis in meta-analysis, can adjust the random error and ultimately estimate the required sample size of the systematic review or meta-analysis. TSA is performed in TSA software. In the present study, we aimed to introduce how to use the TSA software for performing meta-analysis.
The sample size of a meta-analysis should not be less than a single randomized controlled trial. Trial sequential analysis (TSA) can provide required information size and monitoring boundary to justify the conclusion of meta-analysis. However, the TSA software is only suitable for binary and continuous data, and it cannot analyze the time-to-event data. This paper aimed to introduce how to analyze the time-to-event data using TSA approach.
The assumption of fixed-effects model is based on that the true effect of the each trial is same. However, the assumption of random-effects model is based on that the true effect of included trials is normal distributed. The total variance is equal to the sum of within-trial variance and between-trial variance under the random-effects model. There are many estimators of the between-trial variance. The aim of this paper is to give a brief introduction of the estimators of between-trial variance in trial sequential analysis for random-effects model.
Cumulative meta-analysis could help researchers to justify the effectiveness of the intervention and whether the obtained evidence is sufficient. However, the process of the meta-analysis does not adjust the repeated testing of the null hypothesis and neither quantifies the statistical power. The sequential meta-analysis has solved the aforementioned problems and has been widely used in the clinical practice and decision-making. Currently several methods of sequential meta-analysis have been proposed and these methods differ from each other. Of which, the methodology of trial sequential (TSA) is well developed and corresponding performance is relatively easy; the methodology of double-triangular test of Whitehead is lagged than TSA and its performance is relatively difficult; the approach of semi-Bayes refers to the theory of Bayes and it's very difficult to generalize. Our paper aimed to give a brief introduction of the methodology of the sequential meta-analysis.
Objective To detect the false-negative results of cumulative meta-analyses of Cochrane Urology Group with the trial sequential analysis (TSA). Methods The Urology Group of The Cochrane Library (Issue 6, 2016) was searched to collect meta-analyses with negative results. Two researchers independently screened literature and extracted data of included meta-analyses. Then, TSA was performed using TSA software version 0.9 beta. Results A total of 11 papers involving 12 meta-analyses were included. The results of TSA showed that, four (33%) out of 12 meta-analyses were potentially false-negative results for failing to surpass the trial sequential monitoring boundary and to reach the required information size. Conclusion Some of the negative results of systematic reviews from Cochrane Urology Group was false-negative. TSA can help researchers to identify the false-negative results of meta-analyses.
Trial Sequential Analysis (TSA), one kind of cumulative meta-analysis, is a method which introduces sequential analysis into traditional meta-analysis to avoid random errors (false positive or false negative outcomes) that occurred during repeated updates when traditional meta-analysis is performing. It is also applied to calculate required information size (RIS) of a firm conclusion. This study aims to summarize the proposal, fundamental theory, application software, and current limitation of TSA, and to clarify the advantages of TSA on the basis of detailed examples, in order to attract more attention of researchers and promote the methodological development of meta-analysis in China.
ObjectivesTo systematically review the association between the SIX6 gene rs10483727 mutation and primary open angle glaucoma (POAG).MethodsPubMed, Web of Science, The Cochrane Library, CNKI, WanFang Data and VIP databases were searched to collect case-control studies on the SIX6 gene rs10483727 polymorphism and primary open angle glaucoma from inception to December 28th, 2018. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies. Meta-analysis was performed by Stata 12.0 software.ResultsSeventeen case-control studies in 16 papers were included, involving 9 886 patients and 19 663 controls. The results of meta-analysis showed that rs10483727 polymorphism in SIX6 gene was associated with the risk of POAG in the Asians and Caucasians. However, no association was found in the Africans.ConclusionsThe current evidence shows that rs10483727 polymorphism in SIX6 gene is associated with the risk of POAG in the Asians and Caucasians.
Trial sequential analysis (TSA) can identify inclusive results of apparently conclusive of meta-analyses by providing require information size and monitoring boundary. Certain methods of calculating information size are existed. Our objective was to give a brief introduction of four methods to help readers to better perform TSA in making meta-analyses.
