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Techniques to perform objective Bayesian variable selection in the linear model are well developed and have been extended to the generalized linear model setting as well as to the Cox proportional hazards model. Here, we consider discrete time\u2010to\u2010event data with competing risks and propose methodology to develop a clinical prediction model for the daily risk of acquiring a ventilator\u2010associated pneumonia (VAP) attributed to <jats:italic>P. aeruginosa<\/jats:italic> (PA) in intensive care units. The competing events for a PA VAP are extubation, death, and VAP due to other bacteria. Baseline variables are potentially important to predict the outcome at the start of ventilation, but may lose some of their predictive power after a certain time. Therefore, we use a landmark approach for dynamic Bayesian variable selection where the set of relevant predictors depends on the time already spent at risk. We finally determine the direct impact of a variable on each competing event through cause\u2010specific variable selection.<\/jats:p>","DOI":"10.1002\/bimj.201700259","type":"journal-article","created":{"date-parts":[[2018,9,17]],"date-time":"2018-09-17T06:41:05Z","timestamp":1537166465000},"page":"514-534","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Dynamic clinical prediction models for discrete time\u2010to\u2010event data with competing risks\u2014A case study on the OUTCOMEREA database"],"prefix":"10.1002","volume":"61","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7531-4333","authenticated-orcid":false,"given":"Rachel","family":"Heyard","sequence":"first","affiliation":[{"name":"Department of Biostatistics at the Epidemiology, Biostatistics and Prevention Institute University of Zurich Hirschengraben 84 Zurich 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