SEMINAR

Partitioning Evidence of Statistical Association: A Case Study of Air Pollution and Mortality

Holly Janes, Ph.D.
Johns Hopkins Bloomberg
School of Public Health

Wednesday, February 21, 2007
3:30pm, Moos 2-690
Minneapolis Campus

Abstract:

Spatio-temporal studies of the association between air pollution and mortality are challenged by the strong effects of unmeasured confounders. Some examples are seasonality and changes in government policy that cause trends in air pollution and mortality over time. Motivated by two recent studies, the National Morbidity and Mortality Air Pollution Study (NMMAPS) and the Medicare Air Pollution Study (MCAPS), we propose decomposing the measure of association into orthogonal components, roughly corresponding to distinct scales of spatio-temporal variation. We show that typical regression models combine these components with a specific set of weights. Viewing the constituents and their weights helps the scientist to understand a given model and to compare competing models. Excess variation among the components is evidence of the influence of unmeasured confounders acting at particular spatio-temporal scales. We develop a test to detect unmeasured confounding. Using NMMAPS data for the New York City elderly population, we find very different evidence of association between air pollution and mortality across several distinct temporal scales, suggesting the influence of unmeasured confounders. Our analysis of the MCAPS data also reveals unmeasured confounding, as evidenced by the different associations between trends in air pollution and mortality at different spatial scales. Typical regression models combine these very different components into a single summary measure. Our methods represent a more transparent way of summarizing association, and motivate the development of more confounding-robust measures of association. This approach is useful more broadly for partitioning evidence of statistical association and testing for confounding by omitted covariates in studies of exposures and outcomes that vary in space and time.

A social tea will be held at 3:00 P.M. in A434 Mayo. All are Welcome.
For more details contact 612-624-4655 or see http://www.biostat.umn.edu/seminar_academic.html