title: An approach to checking case-crossover analyses based on equivalence with time-series methods.
abstract: 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.
authors.affiliation: Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA. ylu@jhsph.edu
authors.forename: Yun
authors.initials: Y
authors.lastname: Lu
authors.forename: James Morel
authors.initials: JM
authors.lastname: Symons
authors.forename: Alison S
authors.initials: AS
authors.lastname: Geyh
authors.forename: Scott L
authors.initials: SL
authors.lastname: Zeger
mesh_terms: D000368:Aged; D000397:Air Pollution / Q000009:adverse effects*; D015982:Bias; D016022:Case-Control Studies; D002641:Chicago / Q000453:epidemiology; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004812:Epidemiologic Methods; D006801:Humans; D016014:Linear Models; D016015:Logistic Models; D009026:Mortality; D016012:Poisson Distribution; D012307:Risk Factors; D013997:Time Factors