A semiparametric Bayesian proportional hazards model for interval censored data with frailty effects

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.