Mount Sinai Health System

A Weighted Kernel Machine Regression Approach to Environmental Pollutants and Infertility; Zhen Chen, PhD

Dr. Zhen Chen is a currently a Senior Investigator in the Biostatistics and Bioinformatics Branch at NICHD, NIH, where he conducts methodological research in diagnostic accuracy, high dimensional and complex data modeling, and Bayesian methods. He obtained his Ph.D. in statistics from the University of Connecticut and was an Assistant Professor at UPenn before he joined NICHD. Dr. Chen is an elected Fellow of the American Statistical Association.


Title of Talk: A Weighted Kernel Machine Regression Approach to Environmental Pollutants and Infertility


Abstract: In epidemiological studies of environmental pollutants in relation to human infertility, it is common that concentrations of a large number of exposures are collected in both male and female partners. Such a couple-based study poses some new challenges in statistical analysis, especially when the effect of the totality of these chemical mixtures is of interest, because these exposures may have complex non-linear and non-additive relationships with the infertility outcome. Kernel machine regression, as a nonparametric regression method, can be applied to model such effects, while accounting for the highly-correlated structure within and across the exposures. However, it doesn’t consider the partner-specific structure in these study data which may lead to suboptimal estimation for the effects of environmental exposures. To overcome this limitation, we developed a weighted kernel machine regression method (wKRM) to model the joint effect of partner-specific exposures, in which a linear weight procedure is used to combine the female and male partners’ exposure concentrations. The proposed wKRM is not only able to reduce the number of analyzed exposures, but also provides an overall importance index of female and male partner’s exposures in the risk of infertility. Simulation studies demonstrate good performance of wKRM in both estimating the joint effects of exposures and fitting the infertility outcome. Application of the proposed method to a prospective infertility study dataset suggests that male partner’s exposure to polychlorinated biphenyls might contribute more toward infertility than female partner’s.


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Thursday, October 14 at 12:00pm to 1:00pm

Virtual Event