biostudies-literatureShao TNational Natural Science Foundation of China1042-1056https://www.ebi.ac.uk/biostudies/studies/S-EPMC12657653biostudies-literatureUnknown16(6)Network meta-analysis (NMA) is becoming increasingly important, especially in the field of medicine, as it allows for comparisons across multiple trials with different interventions. For time-to-event data, that is, survival data, traditional NMA based on the proportional hazards (PH) assumption simply synthesizes reported hazard ratios (HRs). Novel methods for NMA based on the non-PH assumption have been proposed and implemented using R software. However, these methods often involve complex methodologies and require advanced programming skills, creating a barrier for many researchers. Therefore, we developed an R Shiny tool, NMAsurv (https://psurvivala.shinyapps.io/NMAsurv/). NMAsurv allows users with little or zero background in R to conduct survival-data-based NMA effortlessly. The toolResearch synthesis methodsNMAsurv: An R Shiny application for network meta-analysis based on survival data.PMC1265765372174207Zhao MShi FRui MTang WShao TfalseNMAsurv: An R Shiny application for network meta-analysis based on survival data.Network meta-analysis (NMA) is becoming increasingly important, especially in the field of medicine, as it allows for comparisons across multiple trials with different interventions. For time-to-event data, that is, survival data, traditional NMA based on the proportional hazards (PH) assumption simply synthesizes reported hazard ratios (HRs). Novel methods for NMA based on the non-PH assumption have been proposed and implemented using R software. However, these methods often involve complex methodologies and require advanced programming skills, creating a barrier for many researchers. Therefore, we developed an R Shiny tool, NMAsurv (https://psurvivala.shinyapps.io/NMAsurv/). NMAsurv allows users with little or zero background in R to conduct survival-data-based NMA effortlessly. The tool2025-01-01T00:00:00Z2025 Nov2026-06-25T03:14:35.792Z2026-06-25T03:07:51.553ZS-EPMC126576534162698210.1017/rsm.2025.10020