The article discusses the limitations of traditional meta-analyses in systematic reviews, particularly their insufficient statistical power to detect or refute large intervention effects. It introduces Trial Sequential Analysis (TSA) as a methodology to address these issues by adjusting thresholds for statistical significance when the required sample size has not been reached. TSA uses Lan-DeMets trial sequential monitoring boundaries to provide adjusted confidence intervals and restricted thresholds, reducing both type I and type II errors. The article highlights the importance of considering heterogeneity in meta-analyses and the need for transparent assumptions in TSA. Examples from traditional meta-analyses using unadjusted confidence intervals and 5% significance thresholds are provided to illustrate how TSA can reduce spurious conclusions. Empirical studies have shown that TSA provides better control of type I and type II errors compared to traditional meta-analyses. The article concludes that TSA offers a more robust approach to meta-analytic data analysis, enhancing the reliability and validity of systematic reviews.The article discusses the limitations of traditional meta-analyses in systematic reviews, particularly their insufficient statistical power to detect or refute large intervention effects. It introduces Trial Sequential Analysis (TSA) as a methodology to address these issues by adjusting thresholds for statistical significance when the required sample size has not been reached. TSA uses Lan-DeMets trial sequential monitoring boundaries to provide adjusted confidence intervals and restricted thresholds, reducing both type I and type II errors. The article highlights the importance of considering heterogeneity in meta-analyses and the need for transparent assumptions in TSA. Examples from traditional meta-analyses using unadjusted confidence intervals and 5% significance thresholds are provided to illustrate how TSA can reduce spurious conclusions. Empirical studies have shown that TSA provides better control of type I and type II errors compared to traditional meta-analyses. The article concludes that TSA offers a more robust approach to meta-analytic data analysis, enhancing the reliability and validity of systematic reviews.