Bayesian Inference on Changes in Response Densities over Predictor Clusters.

Published

Journal Article

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 ease in interpretation and presentation of results, epidemiologists typically categorize predictors prior to analysis. To extend this approach to time-varying predictors, one can cluster individuals by their predictor trajectory, with the cluster index included as a predictor in a regression model for the response. This article develops 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.

Full Text

Duke Authors

Cited Authors

  • Dunson, DB; Herring, A; Siega-Riz, AM

Published Date

  • January 2008

Published In

Volume / Issue

  • 103 / 484

Start / End Page

  • 1508 - 1517

PubMed ID

  • 32148338

Pubmed Central ID

  • 32148338

Electronic International Standard Serial Number (EISSN)

  • 1537-274X

International Standard Serial Number (ISSN)

  • 0162-1459

Digital Object Identifier (DOI)

  • 10.1198/016214508000001039

Language

  • eng