{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Nichetti F"],"funding":["NCI NIH HHS"],"pagination":["38"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12820104"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["10(1)"],"pubmed_abstract":["Progression Free Survival Ratio (PFSratio), as defined as the ratio between PFS on investigational treatment (PFS2) and PFS on the last prior therapy (PFS1), is a popular endpoint in precision oncology (PO) studies. In this work, five methodologies for PFSratio-based trial analysis (count-based, Kaplan Meier, Kernel-based Kaplan Meier, parametric and midrank) and two for trial design (GBVE and Weibull) are benchmarked. The Kernel-based Kaplan Meier analysis is most recommended, as it handles informative censoring and does not require PFS1/PFS2 distribution assumptions. Sample size and power calculation methods perform best when applied to settings with expected high PFS1/PFS2 correlation and median ratio. Analysis of five clinical trials (MOSCATO 01, WINTHER, MASTER, SHIVA and POG570) from"],"journal":["NPJ precision oncology"],"pubmed_title":["Benchmarking progression-free survival ratio as primary endpoint in precision oncology clinical trials."],"pmcid":["PMC12820104"],"funding_grant_id":["R21 CA284179","R21CA284179-01A1"],"pubmed_authors":["Tine G","de Braud F","Rota S","Mock A","Hubschmann D","Wang C","Chen L","Nichetti F","Niger M","Ambrosini P","Frohling S","Hullein J","Pruneri G","Le Tourneau C","Laskin J","Edelmann D","Mariani L","Pleasance E","Horak P","Agnelli L","du Rusquec P"],"additional_accession":[]},"is_claimable":false,"name":"Benchmarking progression-free survival ratio as primary endpoint in precision oncology clinical trials.","description":"Progression Free Survival Ratio (PFSratio), as defined as the ratio between PFS on investigational treatment (PFS2) and PFS on the last prior therapy (PFS1), is a popular endpoint in precision oncology (PO) studies. In this work, five methodologies for PFSratio-based trial analysis (count-based, Kaplan Meier, Kernel-based Kaplan Meier, parametric and midrank) and two for trial design (GBVE and Weibull) are benchmarked. The Kernel-based Kaplan Meier analysis is most recommended, as it handles informative censoring and does not require PFS1/PFS2 distribution assumptions. Sample size and power calculation methods perform best when applied to settings with expected high PFS1/PFS2 correlation and median ratio. Analysis of five clinical trials (MOSCATO 01, WINTHER, MASTER, SHIVA and POG570) from","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Dec","modification":"2026-07-15T09:10:24.016Z","creation":"2026-07-02T03:08:52.579Z"},"accession":"S-EPMC12820104","cross_references":{"pubmed":["41398053"],"doi":["10.1038/s41698-025-01231-x"]}}