Two New Methods for Nonlinear Regression in Epidemiology and Environmental Toxicology
Alex Stringer, PhD
Assistant Professor
Department of Statistics and Actuarial Science |
University of Waterloo
WHEN:Â Wednesday, October 23, 2024, from 3:30 to 4:30 p.m.
WHERE:Â Hybrid | 2001 McGill College Avenue, Room 1201; Zoom
NOTE: Alex Stringer will be presenting in-person
Abstract
I discuss two new methods involving additive models that are relevant to environmental epidemiology and toxicology. The first is a new cumulative exposure additive model for overdispersed count data in which the covariate being smoothed is the integrated weighted exposure to a pollutant. The weight function and the regression function are both unknown and modelled using penalized splines. The method is used to analyze several years of daily health outcome counts and their association with cumulative exposure to three air pollutants in various regions across Canada, as part of an active collaboration with Health Canada in support of the Air Health Trend Indicator project. The second is a new approach to the determination of allowable doses in environmental toxicology. The dose-response curve is fit using monotone splines and the benchmark dose and lower limit are obtained using fast implementations of Newton's method that make use of de Boor's algorithm for spline curve evaluation. The method is applied to the study of prenatal alcohol exposure and child cognition using data from six NIH-funded longitudinal cohort studies. The common theme of efficient computation with splines unites these two seemingly unrelated methodologies. If time permits, I will also discuss ongoing efforts to develop general hypothesis tests for linearity in multiple-component additive models and of zero variance components in random effects models more generally. Based on joint work with Tianyi Pan, Glen McGee, Tugba Akkaya Hocagil, Richard Cook, Louise Ryan, Sandra and Joseph Jacobson, and Jeffrey Negrea.
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