This paper introduces graphical tools for network meta-analysis (NMA) in STATA, aiming to make the methodology accessible to non-statisticians. The authors present a series of STATA routines that facilitate the visualization of the evidence base, evaluation of model assumptions, and interpretation of NMA results. The tools include network plots, contribution plots, inconsistency plots, comparison-adjusted funnel plots, predictive intervals plots, and ranking plots. These graphical methods help in understanding the characteristics of the evidence, identifying potential inconsistencies, and presenting the results in a clear and understandable manner. The paper also discusses the importance of considering the assumptions underlying NMA, such as consistency, and the challenges in interpreting NMA results. The authors emphasize the need for practical tools that can be easily applied by researchers with varying levels of statistical expertise. The STATA routines provided allow for the analysis of multiple treatments in a network, incorporating both direct and indirect evidence, and offer a flexible framework for conducting NMA in a frequentist setting. The paper highlights the benefits of using graphical tools in NMA, including improved interpretation of results and better decision-making in healthcare. The authors conclude that the development of user-friendly graphical tools is essential for the widespread adoption and effective application of NMA in comparative effectiveness research.This paper introduces graphical tools for network meta-analysis (NMA) in STATA, aiming to make the methodology accessible to non-statisticians. The authors present a series of STATA routines that facilitate the visualization of the evidence base, evaluation of model assumptions, and interpretation of NMA results. The tools include network plots, contribution plots, inconsistency plots, comparison-adjusted funnel plots, predictive intervals plots, and ranking plots. These graphical methods help in understanding the characteristics of the evidence, identifying potential inconsistencies, and presenting the results in a clear and understandable manner. The paper also discusses the importance of considering the assumptions underlying NMA, such as consistency, and the challenges in interpreting NMA results. The authors emphasize the need for practical tools that can be easily applied by researchers with varying levels of statistical expertise. The STATA routines provided allow for the analysis of multiple treatments in a network, incorporating both direct and indirect evidence, and offer a flexible framework for conducting NMA in a frequentist setting. The paper highlights the benefits of using graphical tools in NMA, including improved interpretation of results and better decision-making in healthcare. The authors conclude that the development of user-friendly graphical tools is essential for the widespread adoption and effective application of NMA in comparative effectiveness research.