Background: Multivariate analysis of interval censored event data based on classical likelihood methods is notoriously cumbersome. Likelihood inference for models which additionally include random effects are not available at all. Developed algorithms bear problems for practical users like: matrix inversion, slow convergence, no assessment of statistical uncertainty. Methods: MCMC procedures combined with imputation are used to implement hierarchical models for interval censored data within a Bayesian framework. Results: Two examples from clinical practice demonstrate the handling of clustered interval censored event times as well as multilayer random effects for inter-institutional quality assessment. The software developed is called survBayes and is freely available at CRAN. Conclusion: The proposed software supports the solution of complex analyses in many fields of clinical epidemiology as well as health services research.