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2009 Prediction of pregnancy: a joint model for longitudinal and binary data
Julie Horrocks, Marianne J. van Den Heuvel
Bayesian Anal. 4(3): 523-538 (2009). DOI: 10.1214/09-BA419


We consider the problem of predicting the achievement of successful pregnancy, in a population of women undergoing treatment for infertility, based on longitudinal measurements of adhesiveness of certain blood lymphocytes. A goal of the analysis is to provide, for each woman, an estimated probability of becoming pregnant. We discuss various existing approaches, including multiple t-tests, mixed models, discriminant analysis and two-stage models. We use a joint model developed by Wange et al. (2000), consisting of a linear mixed effects model for the longitudinal data and a generalized linear model (glm) for the primary endpoint, (here a binary indicator of successful pregnancy). The joint longitudinal/glm model is analogous to the popular joint models for longitudinal and survival data. We estimate the parameters using Bayesian methodology.


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Julie Horrocks. Marianne J. van Den Heuvel. "Prediction of pregnancy: a joint model for longitudinal and binary data." Bayesian Anal. 4 (3) 523 - 538, 2009.


Published: 2009
First available in Project Euclid: 22 June 2012

zbMATH: 1330.62026
MathSciNet: MR2551044
Digital Object Identifier: 10.1214/09-BA419

Keywords: Binary data , generalized linear model , joint model , longitudinal data , mixed linear model

Rights: Copyright © 2009 International Society for Bayesian Analysis


Vol.4 • No. 3 • 2009
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