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Validation of discrete time‐to‐event prediction models in the presence of competing risks
Author(s) -
Heyard Rachel,
Timsit JeanFrançois,
Held Leonhard
Publication year - 2020
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201800293
Subject(s) - event (particle physics) , computer science , intensive care , predictive modelling , discrete event simulation , data mining , statistics , intensive care medicine , machine learning , medicine , mathematics , simulation , physics , quantum mechanics
Abstract Clinical prediction models play a key role in risk stratification, therapy assignment and many other fields of medical decision making. Before they can enter clinical practice, their usefulness has to be demonstrated using systematic validation. Methods to assess their predictive performance have been proposed for continuous, binary, and time‐to‐event outcomes, but the literature on validation methods for discrete time‐to‐event models with competing risks is sparse. The present paper tries to fill this gap and proposes new methodology to quantify discrimination, calibration, and prediction error (PE) for discrete time‐to‐event outcomes in the presence of competing risks. In our case study, the goal was to predict the risk of ventilator‐associated pneumonia (VAP) attributed to Pseudomonas aeruginosa in intensive care units (ICUs). Competing events are extubation, death, and VAP due to other bacteria. The aim of this application is to validate complex prediction models developed in previous work on more recently available validation data.