Literature DB >> 20572123

Joint modeling of progression-free survival and death in advanced cancer clinical trials.

David Dejardin1, Emmanuel Lesaffre, Geert Verbeke.   

Abstract

Progression-related endpoints (such as time to progression or progression-free survival) and time to death are common endpoints in cancer clinical trials. It is of interest to study the link between progression-related endpoints and time to death (e.g. to evaluate the degree of surrogacy). However, current methods ignore some aspects of the definitions of progression-related endpoints. We review those definitions and investigate their impact on modeling the joint distribution. Further, we propose a multi-state model in which the association between the endpoints is modeled through a frailty term. We also argue that interval-censoring needs to be taken into account to more closely match the latent disease evolution. The joint distribution and an expression for Kendall's tau are derived. The model is applied to data from a clinical trial in advanced metastatic ovarian cancer. Copyright (c) 2010 John Wiley & Sons, Ltd.

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Mesh:

Year:  2010        PMID: 20572123     DOI: 10.1002/sim.3918

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  6 in total

1.  Integrating Quality of Life and Survival Outcomes in Cardiovascular Clinical Trials.

Authors:  Jacob V Spertus; Laura A Hatfield; David J Cohen; Suzanne V Arnold; Martin Ho; Philip G Jones; Martin Leon; Bram Zuckerman; John A Spertus
Journal:  Circ Cardiovasc Qual Outcomes       Date:  2019-06-13

2.  A Multi-state Model for Designing Clinical Trials for Testing Overall Survival Allowing for Crossover after Progression.

Authors:  Fang Xia; Stephen L George; Xiaofei Wang
Journal:  Stat Biopharm Res       Date:  2016-03-22       Impact factor: 1.452

3.  Semiparametric time-to-event modeling in the presence of a latent progression event.

Authors:  John D Rice; Alex Tsodikov
Journal:  Biometrics       Date:  2016-08-24       Impact factor: 2.571

4.  Bias in progression-free survival analysis due to intermittent assessment of progression.

Authors:  Leilei Zeng; Richard J Cook; Lan Wen; Audrey Boruvka
Journal:  Stat Med       Date:  2015-05-24       Impact factor: 2.373

5.  Quantifying the association between progression-free survival and overall survival in oncology trials using Kendall's τ.

Authors:  Enya M Weber; Andrew C Titman
Journal:  Stat Med       Date:  2018-10-12       Impact factor: 2.373

6.  Review of meta-analyses evaluating surrogate endpoints for overall survival in oncology.

Authors:  Beth Sherrill; James A Kaye; Rickard Sandin; Joseph C Cappelleri; Connie Chen
Journal:  Onco Targets Ther       Date:  2012-10-23       Impact factor: 4.147

  6 in total

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