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

Epidemiology. 2008 Mar;19(2):169-75. doi: 10.1097/EDE.0b013e3181632c24.


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.

Publication types

  • Comparative Study
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Comment

MeSH terms

  • Aged
  • Air Pollution / adverse effects*
  • Bias
  • Case-Control Studies*
  • Chicago / epidemiology
  • Computer Simulation
  • Data Interpretation, Statistical*
  • Epidemiologic Methods
  • Humans
  • Linear Models*
  • Logistic Models*
  • Mortality
  • Poisson Distribution
  • Risk Factors
  • Time Factors