Department of Biostatistics Seminar/Workshop Series

Bayesian Inference on Changes in Response Densities over Predictor Clusters

Amy Herring, ScD

Associate Professor, Department of Biostatistics, The University of North Carolina at Chapel Hill

Wednesday, January 7, 2009, 1:30-2:55pm, MRBIII Conference Room 1220

Intended Audience: Persons interested in applied statistics, statistical theory, epidemiology, health services research, clinical trials methodology, statistical computing, statistical graphics, R users or potential users

In epidemiology, it is often of interest to assess how individuals with different trajectories over time in an environmental exposure or biomarker differ with respect to a continuous response. For example, the Institute of Medicine is currently interested in potential health effects of patterns of maternal weight gain during pregnancy. For ease in interpretation and presentation of results, epidemiologists typically categorize predictors prior to analysis. To extend this clustering approach to time-varying predictors, such as pregnancy weight, one can cluster individuals by predictor trajectories, with the cluster index included as a predictor in a regression model for the response. We develop a semiparametric Bayes approach, which avoids assuming a pre-specified number of clusters and allows the response to vary nonparametrically over predictor clusters. This methodology is motivated by interest in relating trajectories in weight gain during pregnancy to the distribution of birth weight adjusted for gestational age at delivery. In this setting, the proposed approach allows the tails of the birth weight density to vary flexibly over weight gain clusters, facilitating flexible inferences on changes in birth weight percentiles of interest.

This talk is based on joint work with David Dunson at Duke University and Anna Maria Siega-Riz at UNC.
Topic revision: r1 - 29 Dec 2008, DianeKolb

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