Eliminating systematic bias from case-crossover designs
| Autor: | Sukun Wang, Xiaoming Wang, Warren B. Kindzierski |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Statistics and Probability Epidemiology Computer science Calibration (statistics) Myocardial Infarction Interval (mathematics) Alberta 03 medical and health sciences 0302 clinical medicine CASE CROSSOVER Bias Health Information Management Air pollutants Statistics Humans 030212 general & internal medicine p-value Estimation Air Pollutants Cross-Over Studies Models Statistical Confounding Hospitalization 030104 developmental biology Research Design Calibration |
| Zdroj: | Statistical Methods in Medical Research. 28:3100-3111 |
| ISSN: | 1477-0334 0962-2802 |
| DOI: | 10.1177/0962280218797145 |
| Popis: | 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 (coefficient estimate, 95% confident interval, and p-value). Bias check for coefficient estimation, size check and power check for significance test were done via simulation experiments to show advantages of the calibrated case-crossover studies over the ones without calibration. An application was done to investigate associations between air pollutants and acute myocardial infarction hospitalizations in urban Edmonton. In conclusion, systematic bias in a case-crossover design is often unavoidable, leading to an obvious bias in the estimated effect and an unreliable p value in the significance test. The proposed calibration technique provides an efficient approach to eliminating systematic bias in a case-crossover study. |
| Databáze: | OpenAIRE |
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