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Potential for Bias in Case-Crossover Studies With Shared Exposures Analyzed Using SAS

Wang, Shirley V ; Coull, Brent A ; Schwartz, Joel ; Mittleman, Murray A ; Wellenius, Gregory A

American Journal of Epidemiology, 2011, Vol. 174(1), pp.118-124 [Peer Reviewed Journal]

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  • Title:
    Potential for Bias in Case-Crossover Studies With Shared Exposures Analyzed Using SAS
  • Author/Creator: Wang, Shirley V ; Coull, Brent A ; Schwartz, Joel ; Mittleman, Murray A ; Wellenius, Gregory A
  • Subjects: Air Pollution ; Bias (Epidemiology) ; Environmental Exposure ; Environmental Health ; Epidemiologic Methods
  • Is Part Of: American Journal of Epidemiology, 2011, Vol. 174(1), pp.118-124
  • Description: The case-crossover method is an efficient study design for evaluating associations between transient exposures and the onset of acute events. In one common implementation of this design, odds ratios are estimated using conditional logistic or stratified Cox proportional hazards models, with data stratified on each individual event. In environmental epidemiology, where aggregate time-series data are often used, combining strata with identical exposure histories may be computationally convenient. However, when the SAS software package (SAS Institute Inc., Cary, North Carolina) is used for analysis, users can obtain biased results if care is not taken to properly account for multiple cases observed at the same time. The authors show that fitting a stratified Cox model with the “Breslow” option for handling tied failure times (i.e., ties = Breslow) provides unbiased health-effects estimates in case-crossover studies with shared exposures. The authors’ simulations showed that using conditional logistic regression—or equivalently a stratified Cox model with the “ties = discrete” option—in this setting leads to health-effect estimates which can be biased away from the null hypothesis of no association by 22%–39%, even for small simulated relative risks. All methods tested by the authors yielded unbiased results under a simulated scenario with a relative risk of 1.0. This potential bias does not arise in R (R Foundation for Statistical Computing, Vienna, Austria) or Stata (Stata Corporation, College Station, Texas).
  • Identifier: ISSN: 0002-9262 ; E-ISSN: 1476-6256 ; DOI: 10.1093/aje/kwr038