A comparison of time to event analysis methods, using weight status and breast cancer as a case study

Abstract Survival analysis with cohort study data has been traditionally performed using Cox proportional hazards models. Random survival forests (RSFs), a machine learning method, now present an alternative method. Using the UK Women’s Cohort Study (n = 34,493) we evaluate two methods: a Cox model...

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Autores principales:	Georgios Aivaliotis, Jan Palczewski, Rebecca Atkinson, Janet E. Cade, Michelle A. Morris
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/bdff11217d9a40ce95f2803a85af8a03
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spelling	oai:doaj.org-article:bdff11217d9a40ce95f2803a85af8a032021-12-02T18:34:13ZA comparison of time to event analysis methods, using weight status and breast cancer as a case study10.1038/s41598-021-92944-z2045-2322https://doaj.org/article/bdff11217d9a40ce95f2803a85af8a032021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92944-zhttps://doaj.org/toc/2045-2322Abstract Survival analysis with cohort study data has been traditionally performed using Cox proportional hazards models. Random survival forests (RSFs), a machine learning method, now present an alternative method. Using the UK Women’s Cohort Study (n = 34,493) we evaluate two methods: a Cox model and an RSF, to investigate the association between Body Mass Index and time to breast cancer incidence. Robustness of the models were assessed by cross validation and bootstraping. Histograms of bootstrap coefficients are reported. C-Indices and Integrated Brier Scores are reported for all models. In post-menopausal women, the Cox model Hazard Ratios (HR) for Overweight (OW) and Obese (O) were 1.25 (1.04, 1.51) and 1.28 (0.98, 1.68) respectively and the RSF Odds Ratios (OR) with partial dependence on menopause for OW and O were 1.34 (1.31, 1.70) and 1.45 (1.42, 1.48). HR are non-significant results. Only the RSF appears confident about the effect of weight status on time to event. Bootstrapping demonstrated Cox model coefficients can vary significantly, weakening interpretation potential. An RSF was used to produce partial dependence plots (PDPs) showing OW and O weight status increase the probability of breast cancer incidence in post-menopausal women. All models have relatively low C-Index and high Integrated Brier Score. The RSF overfits the data. In our study, RSF can identify complex non-proportional hazard type patterns in the data, and allow more complicated relationships to be investigated using PDPs, but it overfits limiting extrapolation of results to new instances. Moreover, it is less easily interpreted than Cox models. The value of survival analysis remains paramount and therefore machine learning techniques like RSF should be considered as another method for analysis.Georgios AivaliotisJan PalczewskiRebecca AtkinsonJanet E. CadeMichelle A. MorrisNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Georgios Aivaliotis Jan Palczewski Rebecca Atkinson Janet E. Cade Michelle A. Morris A comparison of time to event analysis methods, using weight status and breast cancer as a case study
description	Abstract Survival analysis with cohort study data has been traditionally performed using Cox proportional hazards models. Random survival forests (RSFs), a machine learning method, now present an alternative method. Using the UK Women’s Cohort Study (n = 34,493) we evaluate two methods: a Cox model and an RSF, to investigate the association between Body Mass Index and time to breast cancer incidence. Robustness of the models were assessed by cross validation and bootstraping. Histograms of bootstrap coefficients are reported. C-Indices and Integrated Brier Scores are reported for all models. In post-menopausal women, the Cox model Hazard Ratios (HR) for Overweight (OW) and Obese (O) were 1.25 (1.04, 1.51) and 1.28 (0.98, 1.68) respectively and the RSF Odds Ratios (OR) with partial dependence on menopause for OW and O were 1.34 (1.31, 1.70) and 1.45 (1.42, 1.48). HR are non-significant results. Only the RSF appears confident about the effect of weight status on time to event. Bootstrapping demonstrated Cox model coefficients can vary significantly, weakening interpretation potential. An RSF was used to produce partial dependence plots (PDPs) showing OW and O weight status increase the probability of breast cancer incidence in post-menopausal women. All models have relatively low C-Index and high Integrated Brier Score. The RSF overfits the data. In our study, RSF can identify complex non-proportional hazard type patterns in the data, and allow more complicated relationships to be investigated using PDPs, but it overfits limiting extrapolation of results to new instances. Moreover, it is less easily interpreted than Cox models. The value of survival analysis remains paramount and therefore machine learning techniques like RSF should be considered as another method for analysis.
format	article
author	Georgios Aivaliotis Jan Palczewski Rebecca Atkinson Janet E. Cade Michelle A. Morris
author_facet	Georgios Aivaliotis Jan Palczewski Rebecca Atkinson Janet E. Cade Michelle A. Morris
author_sort	Georgios Aivaliotis
title	A comparison of time to event analysis methods, using weight status and breast cancer as a case study
title_short	A comparison of time to event analysis methods, using weight status and breast cancer as a case study
title_full	A comparison of time to event analysis methods, using weight status and breast cancer as a case study
title_fullStr	A comparison of time to event analysis methods, using weight status and breast cancer as a case study
title_full_unstemmed	A comparison of time to event analysis methods, using weight status and breast cancer as a case study
title_sort	comparison of time to event analysis methods, using weight status and breast cancer as a case study
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/bdff11217d9a40ce95f2803a85af8a03
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A comparison of time to event analysis methods, using weight status and breast cancer as a case study

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