In assessing relationships between a response and multiple predictors, it is appealing to allow the conditional response distribution to vary flexibly, allowing non-linear and varying relationships with the different quantiles and predictors. Such flexibility is of critical importance in applications in which the tails of the distribution are of primary interest. For example, in epidemiology studies of continuous health responses, the tails of the distribution typically correspond to those individuals having the most adverse health conditions. We would like a method that can allow an environmental exposure, genetic factor or demographic covariate to flexibly impact risk of an adverse response, with adverse corresponding to values in the tails of the distribution. Values further in the tails vary in their severity, so it is important to avoid categorization or grouping. Motivated by studies of pregnancy outcomes and premature delivery, this talk proposes Bayesian nonparametric methods for density regression. I will also describe applications to molecular epidemiology studies. The talk is designed to be accessible to a general audience of biostatisticians and epidemiologists, so technical details will kept to a minimum.
Group for Research in Decision Analysis