Prediction of pregnancy: A joint model for longitudinal and binary data

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 Wang 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 longitu-dinal/glm model is analogous to the popular joint models for longitudinal and survival data. We estimate the parameters using Bayesian methodology. © 2009 International Society for Bayesian Analysis.