Bayesian modelling of Dupuytren disease by using Gaussian copula graphical models

A. Mohammadi, F. Abegaz, E.R. van den Heuvel, E.C. Wit

Research output: Contribution to journalArticleAcademicpeer-review

27 Citations (Scopus)
151 Downloads (Pure)


Dupuytren disease is a fibroproliferative disorder with unknown aetiology that often progresses and eventually can cause permanent contractures of the fingers affected. We provide a computationally efficient Bayesian framework to discover potential risk factors and investigate which fingers are jointly affected. Our Bayesian approach is based on Gaussian copula graphical models, which provide a way to discover the underlying conditional independence structure of variables in multivariate data of mixed types. In particular, we combine the semiparametric Gaussian copula with extended rank likelihood to analyse multivariate data of mixed types with arbitrary marginal distributions. For structural learning, we construct a computationally efficient search algorithm by using a transdimensional Markov chain Monte Carlo algorithm based on a birth–death process. In addition, to make our statistical method easily accessible to other researchers, we have implemented our method in C++ and provide an interface with R software as an R package BDgraph, which is freely available from

Original languageEnglish
Pages (from-to)629-645
Number of pages17
JournalJournal of the Royal Statistical Society. Series C: Applied Statistics
Issue number3
Publication statusPublished - 1 Apr 2017


  • Bayesian inference
  • Bayesian model averaging
  • Birth–death process
  • Dupuytren disease
  • Gaussian copula graphical models
  • Risk factors


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