JSM 2005 Online Program

Abstract #303942

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	404
Type:	Contributed
Date/Time:	Wednesday, August 10, 2005 : 10:30 AM to 12:20 PM
Sponsor:	Section on Statistics in Epidemiology
Abstract - #303942
Title:	Semiparametric Classification of Longitudinal Trajectories with Application to Hormone Curves
Author(s):	Jamie Bigelow*+ and
Companies:	National Institute of Environmental Health Sciences and National Institute of Environmental Health Sciences
Address:	PO Box 12233, Research Triangle Park, NC, 27709, United States
Keywords:	Dirichlet process ; growth mixture ; reversible jump ; latent class
Abstract:	Growth-mixture models, which classify individuals according to trajectory shape, have become widely used in the social sciences. Analyses typically proceed using maximum likelihood estimation, with simple parametric models for the trajectory curves. Motivated by the problem of classifying menstrual cycle hormone curves, we propose a semiparametric generalization of a Bayesian spline model for hierarchical data. Individuals are grouped into classes using Dirichlet process priors for the unknown distribution of the random basis coefficients. By selecting basis coefficients and knot locations using reversible jump Markov chain Monte Carlo, a nonparametric specification of the class-specific curves is developed. The Dirichlet process formulation automatically accommodates uncertainty in the number of latent classes. Methods are developed that allow classification of trajectories in a time-varying covariate, and the approach is used to identify hormone trajectories predictive of fecundability.

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