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Bayesian Inference on Changes in Response Densities over Predictor Clusters

David B. Dunson, Amy H. Herring and Anna Maria Siega-Riz
Journal of the American Statistical Association
Vol. 103, No. 484 (Dec., 2008), pp. 1508-1517
Stable URL: http://www.jstor.org/stable/27640199
Page Count: 10
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Bayesian Inference on Changes in Response Densities over Predictor Clusters
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Abstract

In epidemiology, it often is 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 before analysis. To extend this approach to time-varying predictors, individuals can be clustered 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 that avoids assuming a prespecified 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.

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