{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Shao T"],"funding":["National Natural Science Foundation of China"],"pagination":["1042-1056"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12657653"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["16(6)"],"pubmed_abstract":["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 tool"],"journal":["Research synthesis methods"],"pubmed_title":["NMAsurv: An R Shiny application for network meta-analysis based on survival data."],"pmcid":["PMC12657653"],"funding_grant_id":["72174207"],"pubmed_authors":["Zhao M","Shi F","Rui M","Tang W","Shao T"],"additional_accession":[]},"is_claimable":false,"name":"NMAsurv: An R Shiny application for network meta-analysis based on survival data.","description":"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 tool","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Nov","modification":"2026-06-25T03:14:35.792Z","creation":"2026-06-25T03:07:51.553Z"},"accession":"S-EPMC12657653","cross_references":{"pubmed":["41626982"],"doi":["10.1017/rsm.2025.10020"]}}