An approach to checking case-crossover analyses based on equivalence with time-series methods

Yun Lu, James Morel Symons, Alison S. Geyh, Scott L. Zeger

Research output: Contribution to journalReview articlepeer-review

44 Scopus citations


The case-crossover design has been increasingly applied to epidemiologic investigations of acute adverse health effects associated with ambient air pollution. The correspondence of the design to that of matched case-control studies makes it inferentially appealing for epidemiologic studies. Case-crossover analyses generally use conditional logistic regression modeling. This technique is equivalent to time-series log-linear regression models when there is a common exposure across individuals, as in air pollution studies. Previous methods for obtaining unbiased estimates for case-crossover analyses have assumed that time-varying risk factors are constant within reference windows. In this paper, we rely on the connection between case-crossover and time-series methods to illustrate model-checking procedures from log-linear model diagnostics for time-stratified case-crossover analyses. Additionally, we compare the relative performance of the time-stratified case-crossover approach to time-series methods under 3 simulated scenarios representing different temporal patterns of daily mortality associated with air pollution in Chicago, Illinois, during 1995 and 1996. Whenever a model-be it time-series or case-crossover-fails to account appropriately for fluctuations in time that confound the exposure, the effect estimate will be biased. It is therefore important to perform model-checking in time-stratified case-crossover analyses rather than assume the estimator is unbiased.

Original languageEnglish (US)
Pages (from-to)169-175
Number of pages7
Issue number2
StatePublished - Mar 2008

ASJC Scopus subject areas

  • Epidemiology


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