This article proposes a joint modeling framework for longitudinal insomnia measurements and a stochastic smoking cessation process in the presence of a latent permanent quitting state (i.e., “cure”). A generalized linear mixed-effects model is used for the longitudinal measurements of insomnia symptom and a stochastic mixed-effects model is used for the smoking cessation process. These two models are linked together via the latent random effects. A Bayesian framework and Markov Chain Monte Carlo algorithm are developed to obtain the parameter estimates. The likelihood functions involving time-dependent covariates are formulated and computed. The within-subject correlation between insomnia and smoking processes is explored. The proposed methodology is applied to simulation studies and the motivating dataset, i.e., the Alpha-Tocopherol, Beta-Carotene (ATBC) Lung Cancer Prevention study, a large longitudinal cohort study of smokers from Finland.