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We develop a 2\u2010state Markov regression model in a Bayesian framework to characterise disease progression in JDM over time and gain a better understanding of the factors influencing disease risk. The transition probabilities between disease and remission state (and vice versa) are a function of time\u2010homogeneous and time\u2010varying covariates. These latter types of covariates are introduced in the model through a latent health state function, which describes patient\u2010specific health over time and accounts for variability among patients. We assume a nonparametric prior based on the Dirichlet process to model the health state function and the baseline transition intensities between disease and remission state and vice versa. The Dirichlet process induces a clustering of the patients in homogeneous risk groups. To highlight clinical variables that most affect the transition probabilities, we perform variable selection using spike and slab prior distributions. Posterior inference is performed through Markov chain Monte Carlo methods. Data were made available from the UK JDM Cohort and Biomarker Study and Repository, hosted at the UCL Institute of Child Health.<\/jats:p>","DOI":"10.1002\/sim.7613","type":"journal-article","created":{"date-parts":[[2018,2,21]],"date-time":"2018-02-21T19:58:00Z","timestamp":1519243080000},"page":"1711-1731","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Bayesian semiparametric Markov regression model for juvenile dermatomyositis"],"prefix":"10.1002","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3109-0478","authenticated-orcid":false,"given":"Maria","family":"De Iorio","sequence":"first","affiliation":[{"name":"Department of Statistical Science University College London  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Natacha","family":"Gallot","sequence":"additional","affiliation":[{"name":"Veramed  ClinBay 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