NDLI: Bayesian Modeling of Multiple Episode Occurrence and Severity with a Terminating Event

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Bayesian Modeling of Multiple Episode Occurrence and Severity with a Terminating Event

Content Provider	Scilit
Author	Herring, Amy H. Yang, Juan
Copyright Year	2007
Description	Journal: Biometrics 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.
Related Links	http://onlinelibrary.wiley.com/doi/10.1111/j.1541-0420.2006.00720.x/pdf
Ending Page	388
Page Count	8
Starting Page	381
e-ISSN	15410420
DOI	10.1111/j.1541-0420.2006.00720.x
Journal	Biometrics
Issue Number	2
Volume Number	63
Language	English
Publisher	Wiley-Blackwell
Publisher Date	2007-01-23
Access Restriction	Open
Subject Keyword	Journal: Biometrics Statistics and Probability Bayesian Analysis Data Augmentation Dependent Censoring Dynamic Latent Variables Gibbs Sampling Multiple Event Times
Content Type	Text
Resource Type	Article

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