Trial Sequential Analysis (TSA) is a method used to improve the control of type I and type II errors in meta-analyses. Traditional meta-analyses often lack sufficient statistical power to detect or refute even large intervention effects, leading to an increased risk of false positive (type I) and false negative (type II) conclusions. TSA adjusts the confidence intervals and significance thresholds based on the diversity-adjusted required information size and the number of trials included in the meta-analysis. This approach provides a frequentistic method to control both types of errors by considering the variability (heterogeneity) between the intervention effect estimates of the included trials. TSA is applied to meta-analyses by including trials in chronological order and treating the analysis as an interim analysis relative to the required number of randomised participants. The required information size is defined as the number of participants and events necessary to detect or reject an a priori assumed intervention effect in a meta-analysis. TSA calculates the required number of participants based on the predefined anticipated intervention effect and adjusts the confidence interval and significance level relative to the fraction of the required information size that has been accrued. In the example of hypothermia versus no hypothermia in comatose patients after cardiac arrest, TSA showed that the four trials did not reach half of the required information size to confirm or reject a 17% relative risk reduction. The conventional confidence interval suggested a reduction in mortality, but TSA-adjusted confidence interval indicated that the effect was uncertain. After the inclusion of the Target Temperature Management (TTM) Trial, the updated TSA showed no statistically significant effect at the conventional level, indicating that a 17% relative risk reduction may be excluded. TSA is recommended for use in meta-analyses to reduce the risk of false conclusions due to insufficient statistical power. It provides a transparent approach to analyzing meta-analytic data and better controls type I and type II errors compared to traditional meta-analyses. TSA is also used in sequential analyses of single randomised clinical trials and cumulative meta-analyses of several trials. The method has been criticized for being too conservative and for not accounting for the true intervention effect in the data already accrued. However, TSA is considered a valuable tool for improving the reliability of meta-analyses by adjusting for the required information size and the variability between trials.Trial Sequential Analysis (TSA) is a method used to improve the control of type I and type II errors in meta-analyses. Traditional meta-analyses often lack sufficient statistical power to detect or refute even large intervention effects, leading to an increased risk of false positive (type I) and false negative (type II) conclusions. TSA adjusts the confidence intervals and significance thresholds based on the diversity-adjusted required information size and the number of trials included in the meta-analysis. This approach provides a frequentistic method to control both types of errors by considering the variability (heterogeneity) between the intervention effect estimates of the included trials. TSA is applied to meta-analyses by including trials in chronological order and treating the analysis as an interim analysis relative to the required number of randomised participants. The required information size is defined as the number of participants and events necessary to detect or reject an a priori assumed intervention effect in a meta-analysis. TSA calculates the required number of participants based on the predefined anticipated intervention effect and adjusts the confidence interval and significance level relative to the fraction of the required information size that has been accrued. In the example of hypothermia versus no hypothermia in comatose patients after cardiac arrest, TSA showed that the four trials did not reach half of the required information size to confirm or reject a 17% relative risk reduction. The conventional confidence interval suggested a reduction in mortality, but TSA-adjusted confidence interval indicated that the effect was uncertain. After the inclusion of the Target Temperature Management (TTM) Trial, the updated TSA showed no statistically significant effect at the conventional level, indicating that a 17% relative risk reduction may be excluded. TSA is recommended for use in meta-analyses to reduce the risk of false conclusions due to insufficient statistical power. It provides a transparent approach to analyzing meta-analytic data and better controls type I and type II errors compared to traditional meta-analyses. TSA is also used in sequential analyses of single randomised clinical trials and cumulative meta-analyses of several trials. The method has been criticized for being too conservative and for not accounting for the true intervention effect in the data already accrued. However, TSA is considered a valuable tool for improving the reliability of meta-analyses by adjusting for the required information size and the variability between trials.