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find Keyword "Robustness" 2 results
  • Robustness assessment of meta-analysis results based on enhanced funnel plots and trial sequential analysis

    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.

    Release date:2020-07-02 09:18 Export PDF Favorites Scan
  • Robustness assessment of pediatrics Meta-analysis using fragility index

    ObjectiveTo evaluate the robustness of pediatrics Clinical evidence-based evidence using fragility index and to explore the factors influencing fragility index. MethodsWe searched the PubMed, Embase, and Scopus databases to collect relevant literature on systematic reviews and meta-analyses in the field of pediatrics, and calculated the fragility index. The rank sum test was used to compare differences between groups with different outcome types, different levels of statistical significance, and different sample sizes. Spearman correlation analysis was used to explore the association between the fragility index and sample size, as well as the year of publication. ResultsA total of 152 systematic reviews, including 573 meta-analyses, were included, with a median fragility index of 6 (3, 10). Most meta-analyses chose the risk ratio (RR) as the effect measure (387/573, 67.5%), the Mantel-Haenszel method (412/573, 71.9%) as the synthesis method, and the fixed-effect model (300/573, 57.4%) as the assumed model. The Mann-Whitney test showed no statistically significant difference in the fragility index between meta-analyses with safety outcomes and those with efficacy outcomes (P=0.397), and no statistically significant difference between meta-analyses with significant results and those with non-significant results (P=0.520). The Kruskal-Wallis test found a statistically significant difference in sample size among groups with different fragility indices (P<0.001). Spearman correlation analysis found a positive correlation between the fragility index and sample size (ρ=0.39, P<0.001), but no statistically significant correlation with the year of publication (P=0.235). ConclusionThe fragility index of clinical evidence-based evidence published in pediatrics journals is generally low, and the robustness of the results is not high, so it is necessary to be cautious when making evidence-based decisions. Furthermore, the larger the sample size included in the meta-analysis, the higher the fragility index, and incorporating more trials and populations can facilitate the increase in the robustness of the meta-analysis results.

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