JSM 2005 Online Program

Abstract #304072

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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 - #304072
Title:	Bayesian Multi-state Growth Processes with Unknown Initiation Times
Author(s):	James Slaughter*+ and Amy H. Herring
Companies:	University of North Carolina, Chapel Hill and University of North Carolina, Chapel Hill
Address:	McGavran Greenberg Hall, Chapel Hill, NC, 27599, United States
Keywords:	interval censoring ; data augmentation ; transition times ; reproductive epidemiology
Abstract:	In a multistage model, individuals move through a series of stages with each stage being indicative of their current status. Interest lies in estimating the rate of progression through each stage and covariates that might affect the transition rates. If the initiation and transition times are not known, the length of time spent in any interval is also unknown. We develop a Bayesian discrete time multistage growth model for inference from cross-sectional data with unknown initiation times. For each subject, data are collected at only one time point, at which we observe the current stage as well as covariates that measure stage progression. We do not have prior information we can apply to the parameters of interest (transition hazards) directly, so we specify priors for a function of these parameters instead. Posterior calculations are made using an efficient Markov chain Monte Carlo algorithm, and our methods are applied to a study of early pregnancy loss.

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