Bayesian modeling of embryonic growth using latent variables.

Published

Journal Article

In a growth model, individuals move progressively through a series of states in which each state is indicative of developmental status. Interest lies in estimating the rate of progression through each state while incorporating covariates that might affect the transition rates. We develop a Bayesian discrete-time multistate 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 state as well as covariates that measure developmental progress. We link the developmental progress variables to an underlying latent growth variable that can also affect the state transition rates. A subject with slow latent growth will then have relatively small developmental progress covariates and move through state transitions slowly. We then examine the association between latent growth and the probability of future events in a novel study of embryonic development and pregnancy loss. Using a Markov chain Monte Carlo (MCMC) algorithm for posterior computation, we found evidence in favor of a previously hypothesized but unproven association between slow growth early in pregnancy and increased risk of future spontaneous abortion.

Full Text

Duke Authors

Cited Authors

  • Slaughter, JC; Herring, AH; Hartmann, KE

Published Date

  • April 2008

Published In

Volume / Issue

  • 9 / 2

Start / End Page

  • 373 - 389

PubMed ID

  • 18056115

Pubmed Central ID

  • 18056115

Electronic International Standard Serial Number (EISSN)

  • 1468-4357

International Standard Serial Number (ISSN)

  • 1465-4644

Digital Object Identifier (DOI)

  • 10.1093/biostatistics/kxm040

Language

  • eng