Joint Modelling Approaches to Survival Analysis via Likelihood-Based Boosting Techniques

Joint models are a powerful class of statistical models which apply to any data where event times are recorded alongside a longitudinal outcome by connecting longitudinal and time-to-event data within a joint likelihood allowing for quantification of the association between the two outcomes without...

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Autores principales:	Colin Griesbach, Andreas Groll, Elisabeth Bergherr
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/fd186089ca9d408f93050b1c5d914618
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spelling	oai:doaj.org-article:fd186089ca9d408f93050b1c5d9146182021-11-29T00:55:38ZJoint Modelling Approaches to Survival Analysis via Likelihood-Based Boosting Techniques1748-671810.1155/2021/4384035https://doaj.org/article/fd186089ca9d408f93050b1c5d9146182021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/4384035https://doaj.org/toc/1748-6718Joint models are a powerful class of statistical models which apply to any data where event times are recorded alongside a longitudinal outcome by connecting longitudinal and time-to-event data within a joint likelihood allowing for quantification of the association between the two outcomes without possible bias. In order to make joint models feasible for regularization and variable selection, a statistical boosting algorithm has been proposed, which fits joint models using component-wise gradient boosting techniques. However, these methods have well-known limitations, i.e., they provide no balanced updating procedure for random effects in longitudinal analysis and tend to return biased effect estimation for time-dependent covariates in survival analysis. In this manuscript, we adapt likelihood-based boosting techniques to the framework of joint models and propose a novel algorithm in order to improve inference where gradient boosting has said limitations. The algorithm represents a novel boosting approach allowing for time-dependent covariates in survival analysis and in addition offers variable selection for joint models, which is evaluated via simulations and real world application modelling CD4 cell counts of patients infected with human immunodeficiency virus (HIV). Overall, the method stands out with respect to variable selection properties and represents an accessible way to boosting for time-dependent covariates in survival analysis, which lays a foundation for all kinds of possible extensions.Colin GriesbachAndreas GrollElisabeth BergherrHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7ENComputational and Mathematical Methods in Medicine, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Colin Griesbach Andreas Groll Elisabeth Bergherr Joint Modelling Approaches to Survival Analysis via Likelihood-Based Boosting Techniques
description	Joint models are a powerful class of statistical models which apply to any data where event times are recorded alongside a longitudinal outcome by connecting longitudinal and time-to-event data within a joint likelihood allowing for quantification of the association between the two outcomes without possible bias. In order to make joint models feasible for regularization and variable selection, a statistical boosting algorithm has been proposed, which fits joint models using component-wise gradient boosting techniques. However, these methods have well-known limitations, i.e., they provide no balanced updating procedure for random effects in longitudinal analysis and tend to return biased effect estimation for time-dependent covariates in survival analysis. In this manuscript, we adapt likelihood-based boosting techniques to the framework of joint models and propose a novel algorithm in order to improve inference where gradient boosting has said limitations. The algorithm represents a novel boosting approach allowing for time-dependent covariates in survival analysis and in addition offers variable selection for joint models, which is evaluated via simulations and real world application modelling CD4 cell counts of patients infected with human immunodeficiency virus (HIV). Overall, the method stands out with respect to variable selection properties and represents an accessible way to boosting for time-dependent covariates in survival analysis, which lays a foundation for all kinds of possible extensions.
format	article
author	Colin Griesbach Andreas Groll Elisabeth Bergherr
author_facet	Colin Griesbach Andreas Groll Elisabeth Bergherr
author_sort	Colin Griesbach
title	Joint Modelling Approaches to Survival Analysis via Likelihood-Based Boosting Techniques
title_short	Joint Modelling Approaches to Survival Analysis via Likelihood-Based Boosting Techniques
title_full	Joint Modelling Approaches to Survival Analysis via Likelihood-Based Boosting Techniques
title_fullStr	Joint Modelling Approaches to Survival Analysis via Likelihood-Based Boosting Techniques
title_full_unstemmed	Joint Modelling Approaches to Survival Analysis via Likelihood-Based Boosting Techniques
title_sort	joint modelling approaches to survival analysis via likelihood-based boosting techniques
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/fd186089ca9d408f93050b1c5d914618
work_keys_str_mv	AT colingriesbach jointmodellingapproachestosurvivalanalysisvialikelihoodbasedboostingtechniques AT andreasgroll jointmodellingapproachestosurvivalanalysisvialikelihoodbasedboostingtechniques AT elisabethbergherr jointmodellingapproachestosurvivalanalysisvialikelihoodbasedboostingtechniques
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Joint Modelling Approaches to Survival Analysis via Likelihood-Based Boosting Techniques

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