An approximate joint model for multiple paired longitudinal outcomes and time-to-event data

Joint modeling of multivariate paired longitudinal data and time-to-event data presents computational challenges that supersede full likelihood estimation due to the large dimensional random effects vector needed to capture correlation due to clustering with respect to pairs, subjects, and outcomes. We propose an alternative, computationally simpler approach to estimation of complex shared parameter models where missing data is imputed based on the Posterior Predictive Distribution from a Conditional Linear Model (CLM) approximation. Existing methods for complete data are then implemented to obtain estimates of the event time model parameters. Our method is applied to examine the effects of discordant growth in anthropometric measures of longitudinal fetal growth in twin fetuses and the timing of birth. Simulation results are presented to show that our method performs relatively well with moderate measurement errors under certain CLM approximations.