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Abstract: Joint models are often used to analyze longitudinal and time-to-event data, where latent random effects are used to describe the association between the two outcome processes. It is typically assumed that the random effects follow a multivariate normal distribution. The likelihood analysis under a correctly specified random effects distribution may provide valid inferences. But if the distribution is misspecified, then the maximum likelihood (ML) estimators can be biased and hence may lead to invalid inferences. In this talk, I will discuss the joint analysis under various types of normal and nonnormal random effects. We propose a robust method of estimation that can address uncertainties in the distribution of random effects. I will discuss empirical properties of the proposed estimators based on a simulation study. I will also present an application using a large clinical dataset from the genetic and inflammatory markers of sepsis (GenIMS) study.