Bayesian modeling of multiple episode occurrence and severity with a terminating event.

Abstract:

:An individual's health condition can affect the frequency and intensity of episodes that can occur repeatedly and that may be related to an event time of interest. For example, bleeding episodes during pregnancy may indicate problems predictive of preterm delivery. Motivated by this application, we propose a joint model for a multiple episode process and an event time. The frequency of occurrence and severity of the episodes are characterized by a latent variable model, which allows an individual's episode intensity to change dynamically over time. This latent episode intensity is then incorporated as a predictor in a discrete time model for the terminating event. Time-varying coefficients are used to distinguish among effects earlier versus later in gestation. Formulating the model within a Bayesian framework, prior distributions are chosen so that conditional posterior distributions are conjugate after data augmentation. Posterior computation proceeds via an efficient Gibbs sampling algorithm. The methods are illustrated using bleeding episode and gestational length data from a pregnancy study.

journal_name

Biometrics

journal_title

Biometrics

authors

Herring AH,Yang J

doi

10.1111/j.1541-0420.2006.00720.x

subject

Has Abstract

pub_date

2007-06-01 00:00:00

pages

381-8

issue

2

eissn

0006-341X

issn

1541-0420

pii

BIOM720

journal_volume

63

pub_type

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