Advanced Regression for Environmental Epidemiology

This course covers applied advanced regression analysis. Its focus is on relaxing classical assumptions in regression analysis to better match what epidemiological data really looks like. Specifically, the course will cover nonlinear exposure-response relationships and repeated measure designs, including non-parametric and semi-parametric smoothing techniques, generalized additive models, quantile regression, and time series models. In addition to the theoretical material, students will apply these techniques using R to actual datasets including modeling the effects of environmental exposures on health outcomes. These techniques also are widely applicable to problems in infectious disease, psychiatric, nutritional, occupational, and cancer epidemiology. Course Note: Basic biostatistics and a course in regression analysis recommended. THIS CLASS HAS PRIORITY ENROLLMENT. Any student who does not meet the Wave 1 or Wave 2 criteria can add themselves to the waitlist (if enrollment requirements are met) at any time during the enrollment period. At the beginning of each priority wave, students on the waitlist who meet the Wave’s criteria will be automatically enrolled into any remaining seats in the course (pending no time conflicts). *Cross-Registrants and Non-Degree Students will be enrolled on a space available basis after the enrollment deadline for the course.