Open Access Open Badges Research article

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

Volkmar Henschel13, Jutta Engel1, Dieter Hölzel1 and Ulrich Mansmann12*

Author Affiliations

1 Institute for Medical Informatics, Biometry and Epidemiology, and Tumour Registry Munich, University of Munich, Marchioninistr, 15, D-81377 Munich, Germany

2 Institute of Statistics, University of Munich, Ludwigstr, 33, 80539 München, Germany

3 Biostatistics, Hoffmann-La Roche Basel, PDIB Bau 670/413, Malzgasse 30, CH-4070 Basel, Switzerland

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BMC Medical Research Methodology 2009, 9:9  doi:10.1186/1471-2288-9-9

Published: 10 February 2009



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.


MCMC procedures combined with imputation are used to implement hierarchical models for interval censored data within a Bayesian framework.


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.


The proposed software supports the solution of complex analyses in many fields of clinical epidemiology as well as health services research.