Bayesian modelling of Dupuytren disease by using Gaussian copula graphical models

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

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12 Citations (Scopus)
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Abstract

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 http://CRAN.R-project.org/package=BDgraph.

Original languageEnglish
Pages (from-to)629-645
Number of pages17
JournalJournal of the Royal Statistical Society. Series C: Applied Statistics
Volume66
Issue number3
DOIs
Publication statusPublished - 1 Apr 2017

Keywords

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

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