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An approach to checking case-crossover analyses based on equivalence with time-series methods
Yun Lu1, James Morel Symons, Alison S Geyh
1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA. ylu@jhsph.edu
Epidemiology (Cambridge, Mass.)
|January 29, 2008
Summary
The case-crossover design is useful for studying air pollution health effects. Model-checking is crucial in time-stratified case-crossover analyses to prevent biased exposure effect estimates.
Area of Science:
- Epidemiology
- Environmental Health
- Biostatistics
Background:
- The case-crossover design is frequently used for studying acute health effects of ambient air pollution.
- This design is similar to matched case-control studies, making it appealing for epidemiological research.
- Standard case-crossover analyses often employ conditional logistic regression, equivalent to time-series log-linear models for air pollution studies.
Purpose of the Study:
- To adapt log-linear model diagnostics for model-checking in time-stratified case-crossover analyses.
- To compare the performance of time-stratified case-crossover methods with time-series methods.
- To highlight the importance of accounting for temporal confounders in exposure assessment.
Main Methods:
- Utilized the connection between case-crossover and time-series methods.
- Applied log-linear model diagnostics for model-checking.
- Conducted simulations with different temporal mortality patterns from Chicago air pollution data (1995-1996).
Main Results:
- Demonstrated model-checking procedures for time-stratified case-crossover analyses.
- Compared the performance of time-stratified case-crossover and time-series approaches under simulated scenarios.
- Showed that failure to account for temporal confounders leads to biased effect estimates.
Conclusions:
- Model-checking is essential for time-stratified case-crossover analyses to ensure unbiased exposure effect estimates.
- The study emphasizes the need to address temporal fluctuations that confound exposure.
- Proper model-checking enhances the reliability of epidemiological findings on air pollution and health.
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