The use of longitudinal data for predicting a subsequent binary event is often the focus of diagnostic studies. This is particularly important in obstetrics, where ultrasound measurements taken during fetal development may be useful for predicting various poor pregnancy outcomes. The focus of this paper is on developing a class of joint models for the longitudinal measurements and binary events that can be used for prediction. A shared random parameter model is proposed for linking the two processes together. Under a Gaussian random effects assumption, the approach is simple to implement with standard statistical software. Under this assumption, it is also easy to develop a predictor using multivariate or high dimensional longitudinal data. Using asymptotic and simulation results, we show that estimates of predictive accuracy under a Gaussian random effects distribution are robust to severe misspecification of this distribution. However, under some circumstances, estimates of individual risk may be sensitive to severe random effects misspecification. We illustrate the methodology with data from a longitudinal fetal growth study. If time permits, I will also discuss some statistical issues encountered in the on-going NICHD fetal growth studies.