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Dynamic clinical prediction models for discrete time-to-event data with competing risks-A case study on the OUTCOMEREA database


Heyard, Rachel; Timsit, Jean-François; Essaied, Wafa Ibn; Held, Leonhard (2019). Dynamic clinical prediction models for discrete time-to-event data with competing risks-A case study on the OUTCOMEREA database. Biometrical journal, 61(3):514-534.

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) attributed to P. aeruginosa (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-specific variable selection.

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) attributed to P. aeruginosa (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-specific variable selection.

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Additional indexing

Contributors:The COMBACTE-MAGNET consortium
Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Epidemiology, Biostatistics and Prevention Institute (EBPI)
Dewey Decimal Classification:610 Medicine & health
Language:English
Date:1 May 2019
Deposited On:27 Feb 2019 16:56
Last Modified:13 Apr 2019 01:04
Publisher:Wiley-Blackwell Publishing, Inc.
ISSN:0323-3847
OA Status:Closed
Publisher DOI:https://doi.org/10.1002/bimj.201700259
PubMed ID:30221403

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