Modeling multi-level survival data in multi-center epidemiological cohort studies: Applications from the ELAPSE project.

Air Pollutants / analysis Air Pollution / analysis Cohort Studies Environmental Exposure / analysis Humans Particulate Matter / analysis
Air pollution Cox model Frailty models Health effects Mixed models Multi-level analysis

Journal

Environment international
ISSN: 1873-6750
Titre abrégé: Environ Int
Pays: Netherlands
ID NLM: 7807270

Informations de publication

Date de publication:
02 2021
Historique:
received: 30 10 2020
revised: 18 12 2020
accepted: 25 12 2020
pubmed: 11 1 2021
medline: 24 4 2021
entrez: 10 1 2021
Statut: ppublish

Résumé

We evaluated methods for the analysis of multi-level survival data using a pooled dataset of 14 cohorts participating in the ELAPSE project investigating associations between residential exposure to low levels of air pollution (PM We applied five approaches in a multivariable Cox model to account for the first level of clustering corresponding to cohort specification: (1) not accounting for the cohort or using (2) indicator variables, (3) strata, (4) a frailty term in frailty Cox models, (5) a random intercept under a mixed Cox, for cohort identification. We accounted for the second level of clustering due to common characteristics in the residential area by (1) a random intercept per small area or (2) applying variance correction. We assessed the stratified, frailty and mixed Cox approach through simulations under different scenarios for heterogeneity in the underlying hazards and the air pollution effects. Effect estimates were stable under approaches used to adjust for cohort but substantially differed when no adjustment was applied. Further adjustment for the small area grouping increased the effect estimates' standard errors. Simulations confirmed identical results between the stratified and frailty models. In ELAPSE we selected a stratified multivariable Cox model to account for between-cohort heterogeneity without adjustment for small area level, due to the small number of subjects and events in the latter. Our study supports the need to account for between-cohort heterogeneity in multi-center collaborations using pooled individual level data.

Sections du résumé

BACKGROUND
We evaluated methods for the analysis of multi-level survival data using a pooled dataset of 14 cohorts participating in the ELAPSE project investigating associations between residential exposure to low levels of air pollution (PM
METHODS
We applied five approaches in a multivariable Cox model to account for the first level of clustering corresponding to cohort specification: (1) not accounting for the cohort or using (2) indicator variables, (3) strata, (4) a frailty term in frailty Cox models, (5) a random intercept under a mixed Cox, for cohort identification. We accounted for the second level of clustering due to common characteristics in the residential area by (1) a random intercept per small area or (2) applying variance correction. We assessed the stratified, frailty and mixed Cox approach through simulations under different scenarios for heterogeneity in the underlying hazards and the air pollution effects.
RESULTS
Effect estimates were stable under approaches used to adjust for cohort but substantially differed when no adjustment was applied. Further adjustment for the small area grouping increased the effect estimates' standard errors. Simulations confirmed identical results between the stratified and frailty models. In ELAPSE we selected a stratified multivariable Cox model to account for between-cohort heterogeneity without adjustment for small area level, due to the small number of subjects and events in the latter.
CONCLUSIONS
Our study supports the need to account for between-cohort heterogeneity in multi-center collaborations using pooled individual level data.

Identifiants

DOI: 10.1016/j.envint.2020.106371 PMID: 33422970
pubmed: 33422970
pii: S0160-4120(20)32325-4
doi: 10.1016/j.envint.2020.106371
pii:
doi:

Substances chimiques

Air Pollutants 0
Particulate Matter 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

106371

Subventions

Organisme : Medical Research Council
ID : MR/S019669/1
Pays : United Kingdom

Informations de copyright

Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.

Auteurs

Evangelia Samoli (E)

Dept. of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, 75 Mikras Asias Str, 115 27 Athens, Greece.

Sophia Rodopoulou (S)

Dept. of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, 75 Mikras Asias Str, 115 27 Athens, Greece.

Ulla A Hvidtfeldt (UA)

Danish Cancer Society Research Centre, Copenhagen, Denmark.

Kathrin Wolf (K)

Institute of Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany.

Massimo Stafoggia (M)

Department of Epidemiology, Lazio Region Health Service ASL Roma 1, Rome, Italy; Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.

Bert Brunekreef (B)

Institute for Risk Assessment Sciences (IRAS), Utrecht University, Postbus 80125, 3508 TC Utrecht, the Netherlands.

Maciej Strak (M)

Institute for Risk Assessment Sciences (IRAS), Utrecht University, Postbus 80125, 3508 TC Utrecht, the Netherlands; National Institute for Public Health and the Environment (RIVM), Antonie van Leeuwenhoeklaan 9, 3721 MA Bilthoven, the Netherlands.

Jie Chen (J)

Institute for Risk Assessment Sciences (IRAS), Utrecht University, Postbus 80125, 3508 TC Utrecht, the Netherlands.

Zorana J Andersen (ZJ)

University of Copenhagen, Department of Public Health, Section of Environmental Health, Øster Farimagsgade 5, 1014, Copenhagen, Denmark.

Richard Atkinson (R)

Population Health Research Institute, St George's, University of London, Cranmer Terrace, London SW17 0RE, UK.

Mariska Bauwelinck (M)

Interface Demography, Department of Sociology, Vrije Universiteit Brussel, Brussels, Belgium.

Tom Bellander (T)

Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden; Centre for Occupational and Environmental Medicine, Region Stockholm, Stockholm, Sweden.

Jørgen Brandt (J)

Department of Environmental Science, Aarhus University, Frederiksborgvej 399, Roskilde, Denmark.

Giulia Cesaroni (G)

Department of Epidemiology, Lazio Region Health Service ASL Roma 1, Rome, Italy.

Francesco Forastiere (F)

NIHR HPRU Health Impact of Environmental Hazards, Environmental Research Group, Analytical, Environmental & Forensic Sciences, King's College London, UK.

Daniela Fecht (D)

Small Area Health Statistics Unit, MRC Centre for Environment and Health, School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, UK.

John Gulliver (J)

Centre for Environmental Health and Sustainability & School of Geography, Geology and the Environment, University of Leicester, Leicester, UK.

Ole Hertel (O)

Department of Environmental Science, Aarhus University, Frederiksborgvej 399, Roskilde, Denmark.

Barbara Hoffmann (B)

Institute for Occupational, Social and Environmental Medicine, Medical Faculty, Heinrich-Heine-University of Düsseldorf, Germany.

Kees de Hoogh (K)

Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland.

Nicole A H Janssen (NAH)

Institute for Risk Assessment Sciences (IRAS), Utrecht University, Postbus 80125, 3508 TC Utrecht, the Netherlands.

Matthias Ketzel (M)

Department of Environmental Science, Aarhus University, Frederiksborgvej 399, Roskilde, Denmark; Global Centre for Clean Air Research (GCARE), University of Surrey, Guildford GU2 7XH, UK.

Jochem O Klompmaker (JO)

Institute for Risk Assessment Sciences (IRAS), Utrecht University, Postbus 80125, 3508 TC Utrecht, the Netherlands; National Institute for Public Health and the Environment (RIVM), Antonie van Leeuwenhoeklaan 9, 3721 MA Bilthoven, the Netherlands.

Shuo Liu (S)

University of Copenhagen, Department of Public Health, Section of Environmental Health, Øster Farimagsgade 5, 1014, Copenhagen, Denmark.

Petter Ljungman (P)

Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden; Department of Cardiology, Danderyd University Hospital, Stockholm, Sweden.

Gabriele Nagel (G)

Institute of Epidemiology and Medical Biometry, Ulm University, Ulm, Germany.

Bente Oftedal (B)

Department of Environmental Health, Norwegian Institute of Public Health, Oslo, Norway.

Göran Pershagen (G)

Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden; Centre for Occupational and Environmental Medicine, Region Stockholm, Stockholm, Sweden.

Annette Peters (A)

Institute of Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany.

Ole Raaschou-Nielsen (O)

Danish Cancer Society Research Centre, Copenhagen, Denmark; Department of Environmental Science, Aarhus University, Frederiksborgvej 399, Roskilde, Denmark.

Matteo Renzi (M)

Department of Epidemiology, Lazio Region Health Service ASL Roma 1, Rome, Italy.

Doris T Kristoffersen (DT)

Cluster for Health Services Research, Norwegian Institute of Public Health, Oslo, Norway.

Gianluca Severi (G)

Department of Epidemiology, Lazio Region Health Service ASL Roma 1, Rome, Italy.

Torben Sigsgaard (T)

Department of Public Health, Environment Occupation and Health, Danish Ramazzini Centre, Aarhus University, Aarhus, Denmark.

Danielle Vienneau (D)

Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland.

Gudrun Weinmayr (G)

Institute of Epidemiology and Medical Biometry, Ulm University, Ulm, Germany.

Gerard Hoek (G)

Institute for Risk Assessment Sciences (IRAS), Utrecht University, Postbus 80125, 3508 TC Utrecht, the Netherlands.

Klea Katsouyanni (K)

Dept. of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, 75 Mikras Asias Str, 115 27 Athens, Greece; NIHR HPRU Health Impact of Environmental Hazards, Environmental Research Group, Analytical, Environmental & Forensic Sciences, King's College London, UK.

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Classifications MeSH

questionsmedicales.fr › Actions chimiques et utilisations › Actions toxiques › Polluants environnementaux › Polluants atmosphériques › Modeling multi-level survival data in...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Pollution de l'environnement › Pollution de l'air › Modeling multi-level survival data in...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Caractéristiques des études épidémiologiques › Études épidémiologiques › Études de cohortes › Modeling multi-level survival data in...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Pollution de l'environnement › Exposition environnementale › Modeling multi-level survival data in...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Modeling multi-level survival data in...
questionsmedicales.fr › Mélanges complexes › Matière particulaire › Modeling multi-level survival data in...