Abstract and Keywords
This article focuses on flexible Bayes regression of epidemiologic data involving pregnancy outcomes. It first provides an overview of finite mixture models and nonparametric Bayes methods before discussing some of the possibilities focusing on gestational age at delivery, DDE and age data from the Longnecker et al. (2001) study. More specifically, it examines how risk of premature delivery is impacted by maternal exposure to the pesticide DDT. The results showcase the use of Bayesian analysis in epidemiological studies that collect continuous health outcomes data, and in which the scientific and clinical interest typically focuses on the relationships between exposures and risks of an abnormal response, corresponding to an observation in the tails of the distribution. The article also highlights the limitations of current standard approaches that can be overcome by means of Bayesian analysis using density regression, mixtures and nonparametric models, as developed and applied in this pregnancy outcome study.
Keywords: flexible Bayes regression, epidemiologic data, pregnancy outcomes, finite mixture models, nonparametric Bayes methods, DDT, DDE, Bayesian analysis, epidemiological studies, density regression
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