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On the choice of timescale for other cause mortality in a competing risk setting using flexible parametric survival models

In competing risks settings where the events are death due to cancer and death due to other causes, it is common practice to use time since diagnosis as the timescale for all competing events. However, attained age has been proposed as a more natural choice of timescale for modeling other cause mort...

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Autores principales: Skourlis, Nikolaos, Crowther, Michael J., Andersson, Therese M.‐L., Lambert, Paul C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9795972/
https://www.ncbi.nlm.nih.gov/pubmed/35708221
http://dx.doi.org/10.1002/bimj.202100254
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author Skourlis, Nikolaos
Crowther, Michael J.
Andersson, Therese M.‐L.
Lambert, Paul C.
author_facet Skourlis, Nikolaos
Crowther, Michael J.
Andersson, Therese M.‐L.
Lambert, Paul C.
author_sort Skourlis, Nikolaos
collection PubMed
description In competing risks settings where the events are death due to cancer and death due to other causes, it is common practice to use time since diagnosis as the timescale for all competing events. However, attained age has been proposed as a more natural choice of timescale for modeling other cause mortality. We examine the choice of using time since diagnosis versus attained age as the timescale when modeling other cause mortality, assuming that the hazard rate is a function of attained age, and how this choice can influence the cumulative incidence functions ([Formula: see text] s) derived using flexible parametric survival models. An initial analysis on the colon cancer data from the population‐based Swedish Cancer Register indicates such an influence. A simulation study is conducted in order to assess the impact of the choice of timescale for other cause mortality on the bias of the estimated [Formula: see text] and how different factors may influence the bias. We also use regression standardization methods in order to obtain marginal [Formula: see text] estimates. Using time since diagnosis as the timescale for all competing events leads to a low degree of bias in [Formula: see text] for cancer mortality ([Formula: see text]) under all approaches. It also leads to a low degree of bias in [Formula: see text] for other cause mortality ([Formula: see text]), provided that the effect of age at diagnosis is included in the model with sufficient flexibility, with higher bias under scenarios where a covariate has a time‐varying effect on the hazard rate for other cause mortality on the attained age scale.
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spelling pubmed-97959722022-12-28 On the choice of timescale for other cause mortality in a competing risk setting using flexible parametric survival models Skourlis, Nikolaos Crowther, Michael J. Andersson, Therese M.‐L. Lambert, Paul C. Biom J Time‐to‐event Analysis In competing risks settings where the events are death due to cancer and death due to other causes, it is common practice to use time since diagnosis as the timescale for all competing events. However, attained age has been proposed as a more natural choice of timescale for modeling other cause mortality. We examine the choice of using time since diagnosis versus attained age as the timescale when modeling other cause mortality, assuming that the hazard rate is a function of attained age, and how this choice can influence the cumulative incidence functions ([Formula: see text] s) derived using flexible parametric survival models. An initial analysis on the colon cancer data from the population‐based Swedish Cancer Register indicates such an influence. A simulation study is conducted in order to assess the impact of the choice of timescale for other cause mortality on the bias of the estimated [Formula: see text] and how different factors may influence the bias. We also use regression standardization methods in order to obtain marginal [Formula: see text] estimates. Using time since diagnosis as the timescale for all competing events leads to a low degree of bias in [Formula: see text] for cancer mortality ([Formula: see text]) under all approaches. It also leads to a low degree of bias in [Formula: see text] for other cause mortality ([Formula: see text]), provided that the effect of age at diagnosis is included in the model with sufficient flexibility, with higher bias under scenarios where a covariate has a time‐varying effect on the hazard rate for other cause mortality on the attained age scale. John Wiley and Sons Inc. 2022-06-16 2022-10 /pmc/articles/PMC9795972/ /pubmed/35708221 http://dx.doi.org/10.1002/bimj.202100254 Text en © 2022 The Authors. Biometrical Journal published by Wiley‐VCH GmbH. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
spellingShingle Time‐to‐event Analysis
Skourlis, Nikolaos
Crowther, Michael J.
Andersson, Therese M.‐L.
Lambert, Paul C.
On the choice of timescale for other cause mortality in a competing risk setting using flexible parametric survival models
title On the choice of timescale for other cause mortality in a competing risk setting using flexible parametric survival models
title_full On the choice of timescale for other cause mortality in a competing risk setting using flexible parametric survival models
title_fullStr On the choice of timescale for other cause mortality in a competing risk setting using flexible parametric survival models
title_full_unstemmed On the choice of timescale for other cause mortality in a competing risk setting using flexible parametric survival models
title_short On the choice of timescale for other cause mortality in a competing risk setting using flexible parametric survival models
title_sort on the choice of timescale for other cause mortality in a competing risk setting using flexible parametric survival models
topic Time‐to‐event Analysis
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9795972/
https://www.ncbi.nlm.nih.gov/pubmed/35708221
http://dx.doi.org/10.1002/bimj.202100254
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