<p>Interval-censored data frequently occur in epidemiological, medical, financial and other fields that need survival analysis. Some potential factors related to the hazards of interest cannot be directly observed but are characterized through multiple correlated observable surrogates. This paper develops a joint modelling analysis of case II interval-censored data with latent variables. A joint model proposed consists of a factor analytic model for associating latent variables with their multiple surrogates and an additive hazards model for assessing the potential effects of covariates and latent variables on survival times. We establish an estimation method that incorporates the expectation-maximization algorithm and a series of correlated estimating equations. The consistency and asymptotic normality of the proposed estimators are obtained. The finite-sample performance of the proposed procedure is evaluated via extensive simulation studies and a real data analysis.</p>