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

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Quantifying the association between progression-free survival and overall survival in oncology trials using Kendall's tau. / Weber, Enya; Titman, Andrew Charles.
In: Statistics in Medicine, Vol. 38, No. 5, 28.02.2019, p. 703-719.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Weber, E & Titman, AC 2019, 'Quantifying the association between progression-free survival and overall survival in oncology trials using Kendall's tau.', Statistics in Medicine, vol. 38, no. 5, pp. 703-719. https://doi.org/10.1002/sim.8001

APA

Weber, E., & Titman, A. C. (2019). Quantifying the association between progression-free survival and overall survival in oncology trials using Kendall's tau. Statistics in Medicine, 38(5), 703-719. https://doi.org/10.1002/sim.8001

Vancouver

Weber E, Titman AC. Quantifying the association between progression-free survival and overall survival in oncology trials using Kendall's tau. Statistics in Medicine. 2019 Feb 28;38(5):703-719. Epub 2018 Oct 12. doi: 10.1002/sim.8001

Author

Weber, Enya ; Titman, Andrew Charles. / Quantifying the association between progression-free survival and overall survival in oncology trials using Kendall's tau. In: Statistics in Medicine. 2019 ; Vol. 38, No. 5. pp. 703-719.

Bibtex

@article{bcfc4f4a6e294558b0a5e7cfddb00ca6,
title = "Quantifying the association between progression-free survival and overall survival in oncology trials using Kendall's tau.",
abstract = "This paper considers methods for estimating the association between progression-free and overall survival in oncology trials. Copula-based, non-parametric and illness-death model-based methods are reviewed. In addition, the approach based on an underlying illness-death model is generalized to allow general parametric models. The performance of these methods, in terms of bias and efficiency, is investigated through simulation and also illustrated using data from a clinical trial of treatments for colon cancer. The simulations suggest that the illness-death model-based method provides good estimates of Kendall's tau across several scenarios. In some situations copula-based methods perform well but their performance is sensitive to the choice of copula. The Clayton copula is most appropriate in scenarios which might realistically reflect an oncology trial, but the use of copula models in practice is questionable.",
keywords = "Kendall's τ, copula, inverse probability of censoring weights, multistate model, overall survival, progression-free survival",
author = "Enya Weber and Titman, {Andrew Charles}",
year = "2019",
month = feb,
day = "28",
doi = "10.1002/sim.8001",
language = "English",
volume = "38",
pages = "703--719",
journal = "Statistics in Medicine",
issn = "0277-6715",
publisher = "John Wiley and Sons Ltd",
number = "5",

}

RIS

TY - JOUR

T1 - Quantifying the association between progression-free survival and overall survival in oncology trials using Kendall's tau.

AU - Weber, Enya

AU - Titman, Andrew Charles

PY - 2019/2/28

Y1 - 2019/2/28

N2 - This paper considers methods for estimating the association between progression-free and overall survival in oncology trials. Copula-based, non-parametric and illness-death model-based methods are reviewed. In addition, the approach based on an underlying illness-death model is generalized to allow general parametric models. The performance of these methods, in terms of bias and efficiency, is investigated through simulation and also illustrated using data from a clinical trial of treatments for colon cancer. The simulations suggest that the illness-death model-based method provides good estimates of Kendall's tau across several scenarios. In some situations copula-based methods perform well but their performance is sensitive to the choice of copula. The Clayton copula is most appropriate in scenarios which might realistically reflect an oncology trial, but the use of copula models in practice is questionable.

AB - This paper considers methods for estimating the association between progression-free and overall survival in oncology trials. Copula-based, non-parametric and illness-death model-based methods are reviewed. In addition, the approach based on an underlying illness-death model is generalized to allow general parametric models. The performance of these methods, in terms of bias and efficiency, is investigated through simulation and also illustrated using data from a clinical trial of treatments for colon cancer. The simulations suggest that the illness-death model-based method provides good estimates of Kendall's tau across several scenarios. In some situations copula-based methods perform well but their performance is sensitive to the choice of copula. The Clayton copula is most appropriate in scenarios which might realistically reflect an oncology trial, but the use of copula models in practice is questionable.

KW - Kendall's τ

KW - copula

KW - inverse probability of censoring weights

KW - multistate model

KW - overall survival

KW - progression-free survival

U2 - 10.1002/sim.8001

DO - 10.1002/sim.8001

M3 - Journal article

VL - 38

SP - 703

EP - 719

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 0277-6715

IS - 5

ER -