Conditional poisson regression with random effects for the analysis of multi-site time series studies
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- dc.contributor.author Barrera Gómez, Jose
- dc.contributor.author Puig Oriol, Xavier
- dc.contributor.author Ginebra Molins, Josep
- dc.contributor.author Basagaña Flores, Xavier
- dc.date.accessioned 2024-01-17T08:22:34Z
- dc.date.available 2024-01-17T08:22:34Z
- dc.date.issued 2023
- dc.description.abstract The analysis of time series studies linking daily counts of a health indicator with environmental variables (e.g., mortality or hospital admissions with air pollution concentrations or temperature; or motor vehicle crashes with temperature) is usually conducted with Poisson regression models controlling for long-term and seasonal trends using temporal strata. When the study includes multiple zones, analysts usually apply a two-stage approach: first, each zone is analyzed separately, and the resulting zone-specific estimates are then combined using meta-analysis. This approach allows zone-specific control for trends. A one-stage approach uses spatio-temporal strata and could be seen as a particular case of the case-time series framework recently proposed. However, the number of strata can escalate very rapidly in a long time series with many zones. A computationally efficient alternative is to fit a conditional Poisson regression model, avoiding the estimation of the nuisance strata. To allow for zone-specific effects, we propose a conditional Poisson regression model with a random slope, although available frequentist software does not implement this model. Here, we implement our approach in the Bayesian paradigm, which also facilitates the inclusion of spatial patterns in the effect of interest. We also provide a possible extension to deal with overdispersed data. We first introduce the equations of the framework and then illustrate their application to data from a previously published study on the effects of temperature on the risk of motor vehicle crashes. We provide R code and a semi-synthetic dataset to reproduce all analyses presented.
- dc.description.sponsorship We acknowledge support from the grant CEX2018-000806-S funded by MCIN/AEI/ 10.13039/501100011033, support from Ministry of Research and Universities of the Government of Catalonia (2021 SGR 01563) and support from the Generalitat de Catalunya through the CERCA Program.
- dc.format.mimetype application/pdf
- dc.identifier.citation Barrera-Gómez J, Puig X, Ginebra J, Basagaña X. Conditional poisson regression with random effects for the analysis of multi-site time series studies. Epidemiology. 2023 Nov 1;34(6):873-8. DOI: 10.1097/EDE.0000000000001664
- dc.identifier.doi http://dx.doi.org/10.1097/EDE.0000000000001664
- dc.identifier.issn 1044-3983
- dc.identifier.uri http://hdl.handle.net/10230/58737
- dc.language.iso eng
- dc.publisher Wolters Kluwer (LWW)
- dc.relation.ispartof Epidemiology. 2023 Nov 1;34(6):873-8
- dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/CEX2018-000806-S
- dc.rights © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/
- dc.subject.keyword Epidemiologic methods
- dc.subject.keyword Multi-site
- dc.subject.keyword One-stage
- dc.subject.keyword Spatial structure
- dc.subject.keyword Time series
- dc.title Conditional poisson regression with random effects for the analysis of multi-site time series studies
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/publishedVersion