When conducting a meta-analysis, it is important to consider the quality of the studies included. One aspect of study quality is statistical power, which refers to the ability of a study to detect a true effect if one exists. GRADE (Grading of Recommendations Assessment, Development and Evaluation) is a widely used tool to assess the quality of evidence in systematic reviews and meta-analyses, and one of the GRADE domains is imprecision, which is closely related to statistical power.

It would be ideal to have information on the statistical power of each study included in your meta-analysis, as this could help you assess the quality of evidence and interpret the results. However, it is not always possible to obtain this information from the published studies.

If one of your papers has already calculated G*power, you could assume that the sample size and effect size for that study are known, and that the power estimate is accurate. However, it would not be appropriate to assume that the power is the same for all studies, as this would depend on factors such as the study design, sample size, effect size, and outcome measures.

Therefore, if you cannot obtain power estimates for all studies included in your meta-analysis, you could consider using a sensitivity analysis to assess the impact of different assumptions about power on the overall results. This could involve varying the assumed effect size or sample size, or using different statistical methods to combine the studies. Alternatively, you could limit your conclusions to the studies with the highest quality and power, or report the power estimates for the studies where available and acknowledge the limitations of the analysis.

Thank you so much for the answer! I downloaded G*Power to attempt to calculate power myself by T tests, but I am yet to understand the inputs.

Yes, G power is good. But you will still have to account for differences in sample sizes and methodology when you compare and collate studies :)