2018
DOI: 10.1002/bimj.201700259
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Dynamic clinical prediction models for discrete time‐to‐event data with competing risks—A case study on the OUTCOMEREA database

Abstract: The development of clinical prediction models requires the selection of suitable predictor variables. 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-to-event data with competing risks and propose methodology to develop a clinical prediction model for the daily risk of acquiring a ventilator-associated pneumonia (VAP)… Show more

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Cited by 9 publications
(16 citation statements)
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“…Heyard et al. () were interested in the time until a first occurrence of a VAP attributed to PA. However, once ventilator‐assisted patients are extubated or dead they are not at risk for a VAP PA anymore.…”
Section: Case Studymentioning
confidence: 99%
See 4 more Smart Citations
“…Heyard et al. () were interested in the time until a first occurrence of a VAP attributed to PA. However, once ventilator‐assisted patients are extubated or dead they are not at risk for a VAP PA anymore.…”
Section: Case Studymentioning
confidence: 99%
“…Heyard et al. () developed dynamic competing risks models to answer their research question. They further developed a method for CSVS to simplify their models and account for the fact that some variables may not have a direct effect on each outcome.…”
Section: Case Studymentioning
confidence: 99%
See 3 more Smart Citations