The article discusses the challenges of comparing multiple treatments in clinical research, emphasizing the need to combine direct and indirect evidence. Standard meta-analysis often focuses on pairwise comparisons, which can limit the ability to determine the best treatment. When direct comparisons are unavailable, indirect comparisons are used, though they may be less precise. The authors propose a method that allows for the simultaneous comparison of multiple treatments, using statistical techniques that respect randomization. This approach can provide more accurate estimates of treatment effects by integrating both direct and indirect evidence. The article uses the example of treatments for acute myocardial infarction to illustrate the method. It highlights the limitations of traditional pairwise meta-analysis, which can lead to biased conclusions when comparisons are made between different treatments. The authors argue that a comprehensive analysis of all available evidence is necessary to determine the most effective treatment. They also discuss the importance of considering the generalizability of findings and the potential for bias in indirect comparisons. The authors emphasize that while multiple treatment comparisons can provide more accurate results, they require careful interpretation. Assumptions about the consistency of treatment effects across studies must be considered, and the results should be validated against direct evidence. The article concludes that integrating multiple treatment comparisons into clinical decision-making is essential for providing evidence-based recommendations, especially when multiple treatments are available. The methods described can be applied to various clinical scenarios and are supported by existing statistical techniques. The authors also acknowledge the need for further research to refine these methods and improve their application in clinical practice.The article discusses the challenges of comparing multiple treatments in clinical research, emphasizing the need to combine direct and indirect evidence. Standard meta-analysis often focuses on pairwise comparisons, which can limit the ability to determine the best treatment. When direct comparisons are unavailable, indirect comparisons are used, though they may be less precise. The authors propose a method that allows for the simultaneous comparison of multiple treatments, using statistical techniques that respect randomization. This approach can provide more accurate estimates of treatment effects by integrating both direct and indirect evidence. The article uses the example of treatments for acute myocardial infarction to illustrate the method. It highlights the limitations of traditional pairwise meta-analysis, which can lead to biased conclusions when comparisons are made between different treatments. The authors argue that a comprehensive analysis of all available evidence is necessary to determine the most effective treatment. They also discuss the importance of considering the generalizability of findings and the potential for bias in indirect comparisons. The authors emphasize that while multiple treatment comparisons can provide more accurate results, they require careful interpretation. Assumptions about the consistency of treatment effects across studies must be considered, and the results should be validated against direct evidence. The article concludes that integrating multiple treatment comparisons into clinical decision-making is essential for providing evidence-based recommendations, especially when multiple treatments are available. The methods described can be applied to various clinical scenarios and are supported by existing statistical techniques. The authors also acknowledge the need for further research to refine these methods and improve their application in clinical practice.