Flexible Distributed Lag Models for Count Data Using mgcv

In this tutorial we present the use of R package mgcv to implement Distributed Lag Non-Linear Models (DLNMs) in a flexible way. Interpretation of smoothing splines as random quantities enables approximate Bayesian inference, which in turn allows uncertainty quantification and comprehensive model che...

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Detalles Bibliográficos
Autores: Economou, Theo, Parliari, Daphne, Tobias, Aurelio, Dawkins, Laura, Steptoe, Hamish, Sarran, Christophe, Stoner, Oliver, Lowe, Rachel, Lelieveld, Jos
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/394946
Acceso en línea:http://hdl.handle.net/10261/394946
https://api.elsevier.com/content/abstract/scopus_id/105009712776
Access Level:acceso abierto
Palabra clave:Penalized splines
Bayesian inference
DLNM
Environmental epidemiology
Heat-stress
http://metadata.un.org/sdg/11
http://metadata.un.org/sdg/3
Ensure healthy lives and promote well-being for all at all ages
Make cities and human settlements inclusive, safe, resilient and sustainable
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spelling Flexible Distributed Lag Models for Count Data Using mgcvEconomou, TheoParliari, DaphneTobias, AurelioDawkins, LauraSteptoe, HamishSarran, ChristopheStoner, OliverLowe, RachelLelieveld, JosPenalized splinesBayesian inferenceDLNMEnvironmental epidemiologyHeat-stresshttp://metadata.un.org/sdg/11http://metadata.un.org/sdg/3Ensure healthy lives and promote well-being for all at all agesMake cities and human settlements inclusive, safe, resilient and sustainableIn this tutorial we present the use of R package mgcv to implement Distributed Lag Non-Linear Models (DLNMs) in a flexible way. Interpretation of smoothing splines as random quantities enables approximate Bayesian inference, which in turn allows uncertainty quantification and comprehensive model checking. We illustrate various modeling situations using open-access epidemiological data in conjunction with simulation experiments. We demonstrate the inclusion of temporal structures and the use of mixture distributions to allow for extreme outliers. Moreover, we demonstrate interactions of the temporal lagged structures with other covariates with different lagged periods for different covariates. Spatial structures are also demonstrated, including smooth spatial variability and Markov random fields, in addition to hierarchical formulations to allow for non-structured dependency. Posterior predictive simulation is used to ensure models verify well against the data.TE and JL were funded by the EU’s Horizon 2020 research and innovation program (grant agreement No. 856612) and the Cyprus Government. DP acknowledges support by the LIFE Programme of the European Union in the framework of the project LIFE21-GIE-EL-LIFE-SIRIUS/101074365. RL acknowledges funding from EU’s Horizon Europe research and innovation program (IDAlert; grant agreement 101057554) and a Royal Society Dorothy Hodgkin Fellowship. CS acknowledges support from the NIHR Health Protection Research Unit in Environmental Change and Health.Peer reviewedTaylor & FrancisEuropean CommissionConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202520252025info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/394946https://api.elsevier.com/content/abstract/scopus_id/105009712776reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/EC/H2020/1856612info:eu-repo/grantAgreement/EC/HE/101074365American StatisticianSíinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3949462026-05-22T06:33:51Z
dc.title.none.fl_str_mv Flexible Distributed Lag Models for Count Data Using mgcv
title Flexible Distributed Lag Models for Count Data Using mgcv
spellingShingle Flexible Distributed Lag Models for Count Data Using mgcv
Economou, Theo
Penalized splines
Bayesian inference
DLNM
Environmental epidemiology
Heat-stress
http://metadata.un.org/sdg/11
http://metadata.un.org/sdg/3
Ensure healthy lives and promote well-being for all at all ages
Make cities and human settlements inclusive, safe, resilient and sustainable
title_short Flexible Distributed Lag Models for Count Data Using mgcv
title_full Flexible Distributed Lag Models for Count Data Using mgcv
title_fullStr Flexible Distributed Lag Models for Count Data Using mgcv
title_full_unstemmed Flexible Distributed Lag Models for Count Data Using mgcv
title_sort Flexible Distributed Lag Models for Count Data Using mgcv
dc.creator.none.fl_str_mv Economou, Theo
Parliari, Daphne
Tobias, Aurelio
Dawkins, Laura
Steptoe, Hamish
Sarran, Christophe
Stoner, Oliver
Lowe, Rachel
Lelieveld, Jos
author Economou, Theo
author_facet Economou, Theo
Parliari, Daphne
Tobias, Aurelio
Dawkins, Laura
Steptoe, Hamish
Sarran, Christophe
Stoner, Oliver
Lowe, Rachel
Lelieveld, Jos
author_role author
author2 Parliari, Daphne
Tobias, Aurelio
Dawkins, Laura
Steptoe, Hamish
Sarran, Christophe
Stoner, Oliver
Lowe, Rachel
Lelieveld, Jos
author2_role author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv European Commission
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Penalized splines
Bayesian inference
DLNM
Environmental epidemiology
Heat-stress
http://metadata.un.org/sdg/11
http://metadata.un.org/sdg/3
Ensure healthy lives and promote well-being for all at all ages
Make cities and human settlements inclusive, safe, resilient and sustainable
topic Penalized splines
Bayesian inference
DLNM
Environmental epidemiology
Heat-stress
http://metadata.un.org/sdg/11
http://metadata.un.org/sdg/3
Ensure healthy lives and promote well-being for all at all ages
Make cities and human settlements inclusive, safe, resilient and sustainable
description In this tutorial we present the use of R package mgcv to implement Distributed Lag Non-Linear Models (DLNMs) in a flexible way. Interpretation of smoothing splines as random quantities enables approximate Bayesian inference, which in turn allows uncertainty quantification and comprehensive model checking. We illustrate various modeling situations using open-access epidemiological data in conjunction with simulation experiments. We demonstrate the inclusion of temporal structures and the use of mixture distributions to allow for extreme outliers. Moreover, we demonstrate interactions of the temporal lagged structures with other covariates with different lagged periods for different covariates. Spatial structures are also demonstrated, including smooth spatial variability and Markov random fields, in addition to hierarchical formulations to allow for non-structured dependency. Posterior predictive simulation is used to ensure models verify well against the data.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/394946
https://api.elsevier.com/content/abstract/scopus_id/105009712776
url http://hdl.handle.net/10261/394946
https://api.elsevier.com/content/abstract/scopus_id/105009712776
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/EC/H2020/1856612
info:eu-repo/grantAgreement/EC/HE/101074365
American Statistician
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Taylor & Francis
publisher.none.fl_str_mv Taylor & Francis
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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