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Public Health Data Science: Novel Approaches to Characterizing Health

Modern public health research often relies on data with complex structures. For example, predictors of interest may be growth curves measured over time, exposures to highly-correlated environmental chemicals, or membership in rare groups. We will consider a variety of complex health studies, with a common focus on longitudinal or clustered data. In particular, we examine statistical models for longitudinal patterns of weight change and their association with diabetes risk in the China Health and Nutrition Survey before turning our attention to a study of disparities in birth outcomes as a function of maternal ethnic ancestry. Interesting substantive and statistical questions are highlighted in each example. Dr. Amy Herring is the Carol Remmer Angle Distinguished Professor of Children's Environmental Health and the associate chair in the Department of Biostatistics.She has broad expertise in biostatistics with a research focus on methods for multivariate and longitudinal data, Bayesian methods and methods for handling missing or mismeasured data. She is PI of an NIH-funded R01 to develop new statistical methods of direct relevance to reproductive and perinatal epidemiology, as well as of a large NIEHS-funded T32 to train students in environmental health science with a focus on environmental epidemiology, biostatistics and environmental health science.

Start Date
Monday, November 21, 2016
Start Time
12:00 pm
Trent 040