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

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Standard

Modeling multi-level survival data in multi-center epidemiological cohort studies : Applications from the ELAPSE project. / Samoli, Evangelia; Rodopoulou, Sophia; Hvidtfeldt, Ulla A.; Wolf, Kathrin; Stafoggia, Massimo; Brunekreef, Bert; Strak, Maciej; Chen, Jie; Andersen, Zorana J.; Atkinson, Richard; Bauwelinck, Mariska; Bellander, Tom; Brandt, Jørgen; Cesaroni, Giulia; Forastiere, Francesco; Fecht, Daniela; Gulliver, John; Hertel, Ole; Hoffmann, Barbara; de Hoogh, Kees; Janssen, Nicole A.H.; Ketzel, Matthias; Klompmaker, Jochem O.; Liu, Shuo; Ljungman, Petter; Nagel, Gabriele; Oftedal, Bente; Pershagen, Göran; Peters, Annette; Raaschou-Nielsen, Ole; Renzi, Matteo; Kristoffersen, Doris T.; Severi, Gianluca; Sigsgaard, Torben; Vienneau, Danielle; Weinmayr, Gudrun; Hoek, Gerard; Katsouyanni, Klea.

I: Environment International, Bind 147, 106371, 2021.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Samoli, E, Rodopoulou, S, Hvidtfeldt, UA, Wolf, K, Stafoggia, M, Brunekreef, B, Strak, M, Chen, J, Andersen, ZJ, Atkinson, R, Bauwelinck, M, Bellander, T, Brandt, J, Cesaroni, G, Forastiere, F, Fecht, D, Gulliver, J, Hertel, O, Hoffmann, B, de Hoogh, K, Janssen, NAH, Ketzel, M, Klompmaker, JO, Liu, S, Ljungman, P, Nagel, G, Oftedal, B, Pershagen, G, Peters, A, Raaschou-Nielsen, O, Renzi, M, Kristoffersen, DT, Severi, G, Sigsgaard, T, Vienneau, D, Weinmayr, G, Hoek, G & Katsouyanni, K 2021, 'Modeling multi-level survival data in multi-center epidemiological cohort studies: Applications from the ELAPSE project', Environment International, bind 147, 106371. https://doi.org/10.1016/j.envint.2020.106371

APA

Samoli, E., Rodopoulou, S., Hvidtfeldt, U. A., Wolf, K., Stafoggia, M., Brunekreef, B., Strak, M., Chen, J., Andersen, Z. J., Atkinson, R., Bauwelinck, M., Bellander, T., Brandt, J., Cesaroni, G., Forastiere, F., Fecht, D., Gulliver, J., Hertel, O., Hoffmann, B., ... Katsouyanni, K. (2021). Modeling multi-level survival data in multi-center epidemiological cohort studies: Applications from the ELAPSE project. Environment International, 147, [106371]. https://doi.org/10.1016/j.envint.2020.106371

Vancouver

Samoli E, Rodopoulou S, Hvidtfeldt UA, Wolf K, Stafoggia M, Brunekreef B o.a. Modeling multi-level survival data in multi-center epidemiological cohort studies: Applications from the ELAPSE project. Environment International. 2021;147. 106371. https://doi.org/10.1016/j.envint.2020.106371

Author

Samoli, Evangelia ; Rodopoulou, Sophia ; Hvidtfeldt, Ulla A. ; Wolf, Kathrin ; Stafoggia, Massimo ; Brunekreef, Bert ; Strak, Maciej ; Chen, Jie ; Andersen, Zorana J. ; Atkinson, Richard ; Bauwelinck, Mariska ; Bellander, Tom ; Brandt, Jørgen ; Cesaroni, Giulia ; Forastiere, Francesco ; Fecht, Daniela ; Gulliver, John ; Hertel, Ole ; Hoffmann, Barbara ; de Hoogh, Kees ; Janssen, Nicole A.H. ; Ketzel, Matthias ; Klompmaker, Jochem O. ; Liu, Shuo ; Ljungman, Petter ; Nagel, Gabriele ; Oftedal, Bente ; Pershagen, Göran ; Peters, Annette ; Raaschou-Nielsen, Ole ; Renzi, Matteo ; Kristoffersen, Doris T. ; Severi, Gianluca ; Sigsgaard, Torben ; Vienneau, Danielle ; Weinmayr, Gudrun ; Hoek, Gerard ; Katsouyanni, Klea. / Modeling multi-level survival data in multi-center epidemiological cohort studies : Applications from the ELAPSE project. I: Environment International. 2021 ; Bind 147.

Bibtex

@article{a8fb0dfeec73446f88c5fa3e061fa65a,
title = "Modeling multi-level survival data in multi-center epidemiological cohort studies: Applications from the ELAPSE project",
abstract = "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 (PM2.5 and NO2) and health (natural-cause mortality and cerebrovascular, coronary and lung cancer incidence). 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{\textquoteright} 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.",
keywords = "Air pollution, Cox model, Frailty models, Health effects, Mixed models, Multi-level analysis",
author = "Evangelia Samoli and Sophia Rodopoulou and Hvidtfeldt, {Ulla A.} and Kathrin Wolf and Massimo Stafoggia and Bert Brunekreef and Maciej Strak and Jie Chen and Andersen, {Zorana J.} and Richard Atkinson and Mariska Bauwelinck and Tom Bellander and J{\o}rgen Brandt and Giulia Cesaroni and Francesco Forastiere and Daniela Fecht and John Gulliver and Ole Hertel and Barbara Hoffmann and {de Hoogh}, Kees and Janssen, {Nicole A.H.} and Matthias Ketzel and Klompmaker, {Jochem O.} and Shuo Liu and Petter Ljungman and Gabriele Nagel and Bente Oftedal and G{\"o}ran Pershagen and Annette Peters and Ole Raaschou-Nielsen and Matteo Renzi and Kristoffersen, {Doris T.} and Gianluca Severi and Torben Sigsgaard and Danielle Vienneau and Gudrun Weinmayr and Gerard Hoek and Klea Katsouyanni",
year = "2021",
doi = "10.1016/j.envint.2020.106371",
language = "English",
volume = "147",
journal = "Environment international",
issn = "0160-4120",
publisher = "Pergamon Press",

}

RIS

TY - JOUR

T1 - Modeling multi-level survival data in multi-center epidemiological cohort studies

T2 - Applications from the ELAPSE project

AU - Samoli, Evangelia

AU - Rodopoulou, Sophia

AU - Hvidtfeldt, Ulla A.

AU - Wolf, Kathrin

AU - Stafoggia, Massimo

AU - Brunekreef, Bert

AU - Strak, Maciej

AU - Chen, Jie

AU - Andersen, Zorana J.

AU - Atkinson, Richard

AU - Bauwelinck, Mariska

AU - Bellander, Tom

AU - Brandt, Jørgen

AU - Cesaroni, Giulia

AU - Forastiere, Francesco

AU - Fecht, Daniela

AU - Gulliver, John

AU - Hertel, Ole

AU - Hoffmann, Barbara

AU - de Hoogh, Kees

AU - Janssen, Nicole A.H.

AU - Ketzel, Matthias

AU - Klompmaker, Jochem O.

AU - Liu, Shuo

AU - Ljungman, Petter

AU - Nagel, Gabriele

AU - Oftedal, Bente

AU - Pershagen, Göran

AU - Peters, Annette

AU - Raaschou-Nielsen, Ole

AU - Renzi, Matteo

AU - Kristoffersen, Doris T.

AU - Severi, Gianluca

AU - Sigsgaard, Torben

AU - Vienneau, Danielle

AU - Weinmayr, Gudrun

AU - Hoek, Gerard

AU - Katsouyanni, Klea

PY - 2021

Y1 - 2021

N2 - 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 (PM2.5 and NO2) and health (natural-cause mortality and cerebrovascular, coronary and lung cancer incidence). 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.

AB - 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 (PM2.5 and NO2) and health (natural-cause mortality and cerebrovascular, coronary and lung cancer incidence). 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.

KW - Air pollution

KW - Cox model

KW - Frailty models

KW - Health effects

KW - Mixed models

KW - Multi-level analysis

U2 - 10.1016/j.envint.2020.106371

DO - 10.1016/j.envint.2020.106371

M3 - Journal article

C2 - 33422970

AN - SCOPUS:85098956014

VL - 147

JO - Environment international

JF - Environment international

SN - 0160-4120

M1 - 106371

ER -

ID: 257739584