Conditional Poisson models: a flexible alternative to conditional logistic case cross-over analysis

[Background] The time stratified case cross-over approach is a popular alternative to conventional time series regression for analysing associations between time series of environmental exposures (air pollution, weather) and counts of health outcomes. These are almost always analyzed using condition...

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Detalles Bibliográficos
Autores: Armstrong, Ben, Gasparrini, Antonio, Tobías, Aurelio
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2014
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/125815
Acceso en línea:http://hdl.handle.net/10261/125815
Access Level:acceso abierto
Palabra clave:Statistics
Conditional distributions
Poisson regression
Time series regression
Environment
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spelling Conditional Poisson models: a flexible alternative to conditional logistic case cross-over analysisArmstrong, BenGasparrini, AntonioTobías, AurelioStatisticsConditional distributionsPoisson regressionTime series regressionEnvironment[Background] The time stratified case cross-over approach is a popular alternative to conventional time series regression for analysing associations between time series of environmental exposures (air pollution, weather) and counts of health outcomes. These are almost always analyzed using conditional logistic regression on data expanded to case–control (case crossover) format, but this has some limitations. In particular adjusting for overdispersion and auto-correlation in the counts is not possible. It has been established that a Poisson model for counts with stratum indicators gives identical estimates to those from conditional logistic regression and does not have these limitations, but it is little used, probably because of the overheads in estimating many stratum parameters.[Methods] The conditional Poisson model avoids estimating stratum parameters by conditioning on the total event count in each stratum, thus simplifying the computing and increasing the number of strata for which fitting is feasible compared with the standard unconditional Poisson model. Unlike the conditional logistic model, the conditional Poisson model does not require expanding the data, and can adjust for overdispersion and auto-correlation. It is available in Stata, R, and other packages.[Results] By applying to some real data and using simulations, we demonstrate that conditional Poisson models were simpler to code and shorter to run than are conditional logistic analyses and can be fitted to larger data sets than possible with standard Poisson models. Allowing for overdispersion or autocorrelation was possible with the conditional Poisson model but when not required this model gave identical estimates to those from conditional logistic regression.[Conclusions] Conditional Poisson regression models provide an alternative to case crossover analysis of stratified time series data with some advantages. The conditional Poisson model can also be used in other contexts in which primary control for confounding is by fine stratification.AG was funded by a Methodology Research Fellowship from Medical Research Council UK (grant ID G1002296). AT was supported by a Salvador Madariaga’s grant of the Ministry of Education of the Spanish Government.Peer reviewedBioMed CentralMedical Research Council (UK)Ministerio de Educación, Cultura y Deporte (España)Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2015201520142015info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/125815reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttp://dx.doi.org/10.1186/1471-2288-14-122Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/1258152026-05-22T06:33:51Z
dc.title.none.fl_str_mv Conditional Poisson models: a flexible alternative to conditional logistic case cross-over analysis
title Conditional Poisson models: a flexible alternative to conditional logistic case cross-over analysis
spellingShingle Conditional Poisson models: a flexible alternative to conditional logistic case cross-over analysis
Armstrong, Ben
Statistics
Conditional distributions
Poisson regression
Time series regression
Environment
title_short Conditional Poisson models: a flexible alternative to conditional logistic case cross-over analysis
title_full Conditional Poisson models: a flexible alternative to conditional logistic case cross-over analysis
title_fullStr Conditional Poisson models: a flexible alternative to conditional logistic case cross-over analysis
title_full_unstemmed Conditional Poisson models: a flexible alternative to conditional logistic case cross-over analysis
title_sort Conditional Poisson models: a flexible alternative to conditional logistic case cross-over analysis
dc.creator.none.fl_str_mv Armstrong, Ben
Gasparrini, Antonio
Tobías, Aurelio
author Armstrong, Ben
author_facet Armstrong, Ben
Gasparrini, Antonio
Tobías, Aurelio
author_role author
author2 Gasparrini, Antonio
Tobías, Aurelio
author2_role author
author
dc.contributor.none.fl_str_mv Medical Research Council (UK)
Ministerio de Educación, Cultura y Deporte (España)
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Statistics
Conditional distributions
Poisson regression
Time series regression
Environment
topic Statistics
Conditional distributions
Poisson regression
Time series regression
Environment
description [Background] The time stratified case cross-over approach is a popular alternative to conventional time series regression for analysing associations between time series of environmental exposures (air pollution, weather) and counts of health outcomes. These are almost always analyzed using conditional logistic regression on data expanded to case–control (case crossover) format, but this has some limitations. In particular adjusting for overdispersion and auto-correlation in the counts is not possible. It has been established that a Poisson model for counts with stratum indicators gives identical estimates to those from conditional logistic regression and does not have these limitations, but it is little used, probably because of the overheads in estimating many stratum parameters.
publishDate 2014
dc.date.none.fl_str_mv 2014
2015
2015
2015
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/10261/125815
url http://hdl.handle.net/10261/125815
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv http://dx.doi.org/10.1186/1471-2288-14-122
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv BioMed Central
publisher.none.fl_str_mv BioMed Central
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
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repository.mail.fl_str_mv
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score 15.812429

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