The research project will be conducted in collaboration with Dr. Xiaodan Yu, MD, PhD, Professor of Pediatrics, at Xinhua Hospital, Shanghai Jiaotong University in Shanghai, China. The project will apply novel statistical methods developed from the researcher?s biostatistics dissertation research to an ongoing study of children?s environmental health in Shandong, China. The dataset complements the researcher?s dissertation research, in which the researcher has developed a novel, computationally efficient statistical model to flexibly study the time-varying exposures of chemical mixtures and their effects on health, as well as identify critical exposure windows of chemical mixtures. The researcher will refine the statistical method through analysis of the dataset, develop subject-matter insights through analysis, and develop an open-source software package to bring the statistical method to a wider research community.
The project aims to analyze data from an ongoing study of children?s environmental health in Shandong, China, in which investigators have followed mother-infant pairs in Shandong over time and recorded children?s exposures to heavy metals and other chemicals, and collected neurodevelopmental health outcomes. A statistical method, called Mean Field Variational Bayes for Lagged Kernel Machine Regression, which was developed by the researcher, will be applied to analysis to study the cognitive effects of exposure to mixtures of chemicals during early child development. The researcher also aims to develop an open-source software package to bring ease of use of the statistical model to a wider environmental health research community.
This award under the East Asia and Pacific Summer Institutes program supports summer research by a U.S. graduate student and is jointly funded by NSF and China?s Ministry of Science and Technology.
