Yang, LiliYu, MenggangGao, Sujuan2016-12-092016-12-092016Yang, L., Yu, M., & Gao, S. (2016). Joint models for multiple longitudinal processes and time-to-event outcome. Journal of Statistical Computation and Simulation, 86(18), 3682–3700. https://doi.org/10.1080/00949655.2016.1181760https://hdl.handle.net/1805/11600Joint models are statistical tools for estimating the association between time-to-event and longitudinal outcomes. One challenge to the application of joint models is its computational complexity. Common estimation methods for joint models include a two-stage method, Bayesian and maximum-likelihood methods. In this work, we consider joint models of a time-to-event outcome and multiple longitudinal processes and develop a maximum-likelihood estimation method using the expectation–maximization algorithm. We assess the performance of the proposed method via simulations and apply the methodology to a data set to determine the association between longitudinal systolic and diastolic blood pressure measures and time to coronary artery disease.enPublisher Policyjoint modelsEM algorithmsimulationArticle