Skip Navigation


Biostatistics Advance Access originally published online on June 29, 2006
Biostatistics 2007 8(2):337-344; doi:10.1093/biostatistics/kxl013
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow Supplementary Material
Right arrow All Versions of this Article:
8/2/337    most recent
kxl013v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Disclaimer
Google Scholar
Right arrow Articles by Lu, Y.
Right arrow Articles by Zeger, S. L.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Lu, Y.
Right arrow Articles by Zeger, S. L.
Social Bookmarking
 Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2006. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

On the equivalence of case-crossover and time series methods in environmental epidemiology

Yun Lu*

Department of Biostatistics, Johns Hopkins Bloomberg School of Public health, 615 North Wolfe Street, Baltimore, MD 21205-2179, USA ylu{at}jhsph.edu

Scott L. Zeger

Department of Biostatistics, Johns Hopkins Bloomberg School of Public health, 615 North Wolfe Street, Baltimore, MD 21205-2179, USA

* To whom correspondence should be addressed.

The case-crossover design was introduced in epidemiology 15 years ago as a method for studying the effects of a risk factor on a health event using only cases. The idea is to compare a case's exposure immediately prior to or during the case-defining event with that same person's exposure at otherwise similar "reference" times. An alternative approach to the analysis of daily exposure and case-only data is time series analysis. Here, log-linear regression models express the expected total number of events on each day as a function of the exposure level and potential confounding variables. In time series analyses of air pollution, smooth functions of time and weather are the main confounders. Time series and case-crossover methods are often viewed as competing methods. In this paper, we show that case-crossover using conditional logistic regression is a special case of time series analysis when there is a common exposure such as in air pollution studies. This equivalence provides computational convenience for case-crossover analyses and a better understanding of time series models. Time series log-linear regression accounts for overdispersion of the Poisson variance, while case-crossover analyses typically do not. This equivalence also permits model checking for case-crossover data using standard log-linear model diagnostics.

Keywords: Air pollution; Case-crossover design; Environmental epidemiology; Log-linear model; Overdispersion; Poisson regression; Time series

Received March 11, 2006; revised May 31, 2006; revised June 15, 2006; revised June 21, 2006; accepted for publication June 27, 2006.


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
Am J EpidemiolHome page
M. Stafoggia, J. Schwartz, F. Forastiere, C. A. Perucci, and the SISTI Group
Does Temperature Modify the Association between Air Pollution and Mortality? A Multicity Case-Crossover Analysis in Italy
Am. J. Epidemiol., June 15, 2008; 167(12): 1476 - 1485.
[Abstract] [Full Text] [PDF]



Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.