Abstract
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.
Citation
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. https://doi.org/10.1214/09-BA419
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