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Journal of Biopharmaceutical Statistics Volume 23, 2013 - Issue 5
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Original Articles

A Review of Statistical Issues with Progression-Free Survival as an Interval-Censored Time-to-Event Endpoint

Xing Sun Merck & Co., Merck Research Labs, North Wales, Pennsylvania, USACorrespondence[email protected]
,
Xiaoyun Li Merck & Co., Merck Research Labs, North Wales, Pennsylvania, USA
,
Cong Chen Merck & Co., Merck Research Labs, North Wales, Pennsylvania, USA
&
Yang Song Merck & Co., Merck Research Labs, North Wales, Pennsylvania, USA
Pages 986-1003 | Received 22 Feb 2011, Accepted 08 Jun 2012, Published online: 19 Aug 2013

Citations (8)

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Read on this site (2)

Zihang Zhong, Min Yang, Senmiao Ni, Lixin Cai, Jingwei Wu, Jianling Bai & Hao Yu. (2022) The heterogeneity effect of surveillance intervals on progression free survival. Journal of Applied Statistics 0:0, pages 1-18.
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Marc Delord & Emmanuelle Génin. (2016) Multiple imputation for competing risks regression with interval-censored data. Journal of Statistical Computation and Simulation 86:11, pages 2217-2228.
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Articles from other publishers (6)

Evgueni Jacob, Angélique Perrillat-Mercerot, Jean-Louis Palgen, Adèle L’Hostis, Nicoletta Ceres, Jean-Pierre Boissel, Jim Bosley, Claudio Monteiro & Riad Kahoul. (2023) Empirical methods for the validation of time-to-event mathematical models taking into account uncertainty and variability: application to EGFR + lung adenocarcinoma. BMC Bioinformatics 24:1.
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Jaime Lynn Speiser, Walter T Ambrosius & Nicholas M Pajewski. (2023) Performance of Cox regression models for composite time-to-event endpoints with component-wise censoring in randomized trials. Clinical Trials, pages 174077452311770.
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Jian Zhu & Rui (Sammi) Tang. (2022) A proper statistical inference framework to compare clinical trial and real‐world progression‐free survival data. Statistics in Medicine 41:29, pages 5738-5752.
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Michal Abrahamowicz, Marie‐Eve Beauchamp, Cristiano Soares Moura, Sasha Bernatsky, Steve Ferreira Guerra & Coraline Danieli. (2022) Adapting SIMEX to correct for bias due to interval‐censored outcomes in survival analysis with time‐varying exposure. Biometrical Journal 64:8, pages 1467-1485.
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Robert Miltenberger, Heiko Götte, Armin Schüler & Antje Jahn‐Eimermacher. (2021) Progression‐free survival in oncological clinical studies: Assessment time bias and methods for its correction. Pharmaceutical Statistics 20:4, pages 864-878.
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Audrey Emmanuelle Dugué, Marina Pulido, Sylvie Chabaud, Lisa Belin & Jocelyn Gal. (2016) How to Deal with Interval-Censored Data Practically while Assessing the Progression-Free Survival: A Step-by-Step Guide Using SAS and R Software. Clinical Cancer Research 22:23, pages 5629-5635.
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