Dr. Navidi and colleagues at the University of Southern California discussed the development of three sophisticated statistical methods that would improve the estimates of the health effects of air pollution obtained from epidemiologic studies. First, they took a standard case-crossover design and introduced a bidirectional element where control data were obtained both before and after the health event of interest. The use of the bidirectional case-crossover promises to be an advance over the use of the case-crossover design and offers promise for reducing bias in environmental epidemiologic studies. Second, because measurement error can have a substantial impact on the accuracy of estimated health effects, Navidi constructed a model to evaluate the reliability of two approaches to estimating cumulative exposure to air pollutants. Third, the investigators adapted a multilevel analytic design to air pollution epidemiology. This design has the potential to combine individual and group level comparisons. One important feature of this study was that the investigators were able to test the statistical models they developed using data from the University of Southern California Children\'s Health Study of the long-term effects of air pollutants on children.