Eliminating systematic bias from case-crossover designs

What is it about?

Case-crossover designs have been widely applied to epidemiological and medical investigations of associations between short-term exposures and risk of acute adverse health events. Much effort has been made in literature on understanding source of confounding and reducing systematic bias by reference-select strategies. In this paper, we explored the nature of bias in the ambi-directional and time-stratified case-crossover designs via simulation using actual air pollution data from urban Edmonton, Alberta, Canada. We further proposed a calibration approach for eliminating systematic bias in estimates.

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Why is it important?

The proposed calibration approach can eliminate system bias from the case-crossover design. It could be the BEST solution to deal with bias in a case-crossover design.

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