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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 conditiona...

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Autores principales: Armstrong, Ben G, Gasparrini, Antonio, Tobias, Aurelio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4280686/
https://www.ncbi.nlm.nih.gov/pubmed/25417555
http://dx.doi.org/10.1186/1471-2288-14-122
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author Armstrong, Ben G
Gasparrini, Antonio
Tobias, Aurelio
author_facet Armstrong, Ben G
Gasparrini, Antonio
Tobias, Aurelio
author_sort Armstrong, Ben G
collection PubMed
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. 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2288-14-122) contains supplementary material, which is available to authorized users.
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spelling pubmed-42806862015-01-01 Conditional Poisson models: a flexible alternative to conditional logistic case cross-over analysis Armstrong, Ben G Gasparrini, Antonio Tobias, Aurelio BMC Med Res Methodol Research Article 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2288-14-122) contains supplementary material, which is available to authorized users. BioMed Central 2014-11-24 /pmc/articles/PMC4280686/ /pubmed/25417555 http://dx.doi.org/10.1186/1471-2288-14-122 Text en © Armstrong et al.; licensee BioMed Central Ltd. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Armstrong, Ben G
Gasparrini, Antonio
Tobias, Aurelio
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
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_short 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
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4280686/
https://www.ncbi.nlm.nih.gov/pubmed/25417555
http://dx.doi.org/10.1186/1471-2288-14-122
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