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<p>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 &gt;800 patients revealed an overall weak PFS1/PFS2 correlation (Kendall’s <i>τ</i> range 0.17-0.35), and an asymptotically unbiased median <i>S</i><sub>PFSratio</sub>(<i>δ</i>=1.3) = 33% by means of the Kernel-based analysis, while other methods considerably deviated in studies with censoring rate&gt;10%. This methodology is implemented in the <i>PROPHETS</i> R package and Shiny app.</p>

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Benchmarking progression-free survival ratio as primary endpoint in precision oncology clinical trials

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月15日

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  • Federico Nichetti,
  • Jennifer Hüllein,
  • Pauline du Rusquec,
  • Erin Pleasance,
  • Li Chen,
  • Andreas Mock,
  • Peter Horak,
  • Paolo Ambrosini,
  • Simone Rota,
  • Monica Niger,
  • Luca Agnelli,
  • Gabriele Tinè,
  • Dominic Edelmann,
  • Christophe Le Tourneau,
  • Janessa Laskin,
  • Giancarlo Pruneri,
  • Chi Wang,
  • Daniel Hübschmann,
  • Filippo de Braud,
  • Stefan Fröhling,
  • Luigi Mariani

摘要

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 >800 patients revealed an overall weak PFS1/PFS2 correlation (Kendall’s τ range 0.17-0.35), and an asymptotically unbiased median SPFSratio(δ=1.3) = 33% by means of the Kernel-based analysis, while other methods considerably deviated in studies with censoring rate>10%. This methodology is implemented in the PROPHETS R package and Shiny app.