Bayesian Inference on Changes in Response Densities Over Predictor Clusters

• 成果类型: Article
• 署名作者: Dunson, David B.; Herring, Amy H.; Siega-Riz, Anna Maria
• 署名单位: Duke University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214508000001039
• 发表日期: 2008
• 页码: 1508-1517
• 关键词: maternal weight-gain; sas procedure; pregnancy; distributions; models

摘要

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 then 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.