We propose to develop new statistical methods for improving analyses of multivariate, longitudinal and functional data from biomedical studies. There is increasing concern that exposures occurring during critical windows can lead to later adverse health effects, motivating prospective studies collecting detailed data on multiple time-varying exposures and health outcomes. New statistical methods are needed to efficiently discover critical windows and time-varying dependencies in such high-dimensional data sets, while limiting false discoveries. These methods may lead to fundamental new insights into mechanisms by which exposures induce adverse health effects, while also allowing for the development of targeted interventions and more accurate predictions of disease risk. Our goals include the following. 1. Develop nonparametric Bayes statistical methods for flexibly characterizing differences among individuals in functional data, such as trajectories over time in oxidative stress, reproductive hormones, nutrients and pregnancy weight. 2. Develop methods for flexibly predicting a health response based on multiple time- varying factors, while also estimating critical windows and discovering dynamic relationships between the different factors. 3. Apply these methods to assess relationships between oxidative stress, nutrients and reproductive hormones over the menstrual cycle accounting for the role of age, obesity and smoking. Also consider applications to identify patterns of pregnancy weight gain associated with short-term infant health outcomes.
The development of adverse health conditions, such as infertility and diabetes, depends on the interaction between genetic predisposition and a variety of lifestyle factors, including diet and environmental exposures. Changes with age in these factors is an important determinate of risk, as critical windows can occur many years before disease onset. We provide the statistical tools necessary to identify critical windows of exposure in order to reduce risk through targeted interventions.
|Yuan, Ying; Gilmore, John H; Geng, Xiujuan et al. (2014) FMEM: functional mixed effects modeling for the analysis of longitudinal white matter Tract data. Neuroimage 84:753-64|
|Pati, Debdeep; Dunson, David B (2014) Bayesian nonparametric regression with varying residual density. Ann Inst Stat Math 66:1-31|
|Kundu, Suprateek; Dunson, David B (2014) Bayes variable selection in semiparametric linear models. J Am Stat Assoc 109:437-447|
|Gu, Kelvin; Pati, Debdeep; Dunson, David B (2014) Bayesian Multiscale Modeling of Closed Curves in Point Clouds. J Am Stat Assoc 109:1481-1494|
|Hyun, Jung Won; Li, Yimei; Gilmore, John H et al. (2014) SGPP: spatial Gaussian predictive process models for neuroimaging data. Neuroimage 89:70-80|
|Kessler, David C; Taylor, Jack A; Dunson, David B (2014) Learning phenotype densities conditional on many interacting predictors. Bioinformatics 30:1562-8|
|Cui, Kai; Dunson, David B (2014) Generalized Dynamic Factor Models for Mixed-Measurement Time Series. J Comput Graph Stat 23:169-191|
|Scarpa, Bruno; Dunson, David B (2014) Enriched Stick Breaking Processes for Functional Data. J Am Stat Assoc 109:647-660|
|Li, Daniel; Longnecker, Matthew P; Dunson, David B (2013) Lipid adjustment for chemical exposures: accounting for concomitant variables. Epidemiology 24:921-8|
|Murray, Jared S; Dunson, David B; Carin, Lawrence et al. (2013) Bayesian Gaussian Copula Factor Models for Mixed Data. J Am Stat Assoc 108:656-665|