American Statistical Association
Ambient air pollution has important public health impacts and children are particularly susceptible to these effects. Using data from the Southern California Childrenīs Health study, I discuss two related projects: (1) to quantify, in a unified way, the association of long-term air pollution exposure with a respiratory health outcome while taking into account information from a biomarker of airway inflammation, namely the fractional concentration of exhaled nitric oxide (FeNO) and (2) to relate indicators of long-term exposure to traffic related pollutants to FeNO. For the first project, I use simulation (in a Bayesian approach) and likelihood theory (in a Frequentist approach) to explore the properties of a basic integrated model, comparing it to a standard regression model. The second project brings to light a potential problem with the widespread practice of scaling regression coefficients by interquartile ranges (IQR) to enable implicit or explicit comparisons of effect estimates. I use simple linear regression to demonstrate that when the predictors of interest have different distributions, comparison of the magnitudes of IQR scaled regression coefficients can be confounded by the differences in the distributions.
Sandy Eckel is a postdoctoral research associate in the Division of Biostatistics, Department of Preventive Medicine, at the Keck School of Medicine, University of Southern California (USC). Sandy obtained her Ph.D. in Biostatistics from the Johns Hopkins Bloomberg School of Public Health in 2009.
|Date:||Thursday, March 3, 2011|
|Time:||4:00 - 5:00 P.M.|
Mailman School of Public Health
Department of Biostatistics
722 West 168th Street
Biostatistics Computer Lab
6th Floor - Room 656
New York, New York