Abstract #300912

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JSM 2003 Abstract #300912
Activity Number: 465
Type: Topic Contributed
Date/Time: Thursday, August 7, 2003 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Stat. Sciences
Abstract - #300912
Title: Modeling Daily Growth in Children
Author(s): Christopher H. Schmid*+ and Emery N. Brown and John Ogren
Companies: New England Medical Center and Massachusetts General Hospital and Tufts-New England Medical Center
Address: 750 Washington Streetschoenf, Box 63, Boston, MA, 02111-1526,
Keywords: Bayesian inference ; Markov chain Monte Carlo ; multilevel model ; posterior predictive checks ; reversible jump MCMC ; growth curves
Abstract:

Using daily measurements of children's height taken for several months, we examine whether growth is continuous or saltatory (pulsatile), consisting of short intervals of growth interspersed with longer intervals of no growth. We develop a Bayesian probability model that incorporates random measurement error, random waiting times and random pulse amplitudes. The model uses reversible jump Markov chain Monte Carlo to handle the changes in dimensions needed to estimate the number of growth events. Applying this model to six series of daily height measurements taken over intervals between 113 and 149 days, we can describe the initial size of the individual, as well as the number, locations and amplitudes of the saltatory growth events and the variability associated with the measurement error and growth amplitudes. Growth patterns differ between children, but demonstrate that while short-term growth is not strictly saltatory, it exhibits periods of rapid increase, extended intervals of very little or no increase and other periods of consistent, slow growth.

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