Gao, SujuanZheng, MengjieXu, HuipingZhang, JianjunZhang, Ying2018-08-132018-08-132018-05-07https://hdl.handle.net/1805/17117https://doi.org/10.7912/C2KS92http://dx.doi.org/10.7912/C2/2799Indiana University-Purdue University Indianapolis (IUPUI)Joint models for longitudinal and time-to-event data has been introduced to study the association between repeatedly measured exposures and the risk of an event. The use of joint models allows a survival outcome to depend on some characteristic functions from the longitudinal measures. Current estimation methods include a two-stage approach, Bayesian and maximum likelihood estimation (MLEs) methods. The twostage method is computationally straightforward but often yields biased estimates. Bayesian and MLE methods rely on the joint likelihood of longitudinal and survival outcomes and can be computationally intensive. In this work, we propose a joint generalized estimating equation framework using an inverse intensity weighting approach for parameter estimation from joint models. The proposed method can be used to longitudinal outcomes from the exponential family of distributions and is computationally e cient. The performance of the proposed method is evaluated in simulation studies. The proposed method is used in an aging cohort to determine the relationship between longitudinal biomarkers and the risk of coronary artery disease.en-USGEEJoint modelingMultiple longitudinalSurvivalDissertation10.7912/C2KS92