<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Nichetti F</submitter><funding>NCI NIH HHS</funding><pagination>38</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC12820104</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>10(1)</volume><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</pubmed_abstract><journal>NPJ precision oncology</journal><pubmed_title>Benchmarking progression-free survival ratio as primary endpoint in precision oncology clinical trials.</pubmed_title><pmcid>PMC12820104</pmcid><funding_grant_id>R21 CA284179</funding_grant_id><funding_grant_id>R21CA284179-01A1</funding_grant_id><pubmed_authors>Tine G</pubmed_authors><pubmed_authors>de Braud F</pubmed_authors><pubmed_authors>Rota S</pubmed_authors><pubmed_authors>Mock A</pubmed_authors><pubmed_authors>Hubschmann D</pubmed_authors><pubmed_authors>Wang C</pubmed_authors><pubmed_authors>Chen L</pubmed_authors><pubmed_authors>Nichetti F</pubmed_authors><pubmed_authors>Niger M</pubmed_authors><pubmed_authors>Ambrosini P</pubmed_authors><pubmed_authors>Frohling S</pubmed_authors><pubmed_authors>Hullein J</pubmed_authors><pubmed_authors>Pruneri G</pubmed_authors><pubmed_authors>Le Tourneau C</pubmed_authors><pubmed_authors>Laskin J</pubmed_authors><pubmed_authors>Edelmann D</pubmed_authors><pubmed_authors>Mariani L</pubmed_authors><pubmed_authors>Pleasance E</pubmed_authors><pubmed_authors>Horak P</pubmed_authors><pubmed_authors>Agnelli L</pubmed_authors><pubmed_authors>du Rusquec P</pubmed_authors></additional><is_claimable>false</is_claimable><name>Benchmarking progression-free survival ratio as primary endpoint in precision oncology clinical trials.</name><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</description><dates><release>2025-01-01T00:00:00Z</release><publication>2025 Dec</publication><modification>2026-07-15T09:10:24.016Z</modification><creation>2026-07-02T03:08:52.579Z</creation></dates><accession>S-EPMC12820104</accession><cross_references><pubmed>41398053</pubmed><doi>10.1038/s41698-025-01231-x</doi></cross_references></HashMap>