Header

UZH-Logo

Maintenance Infos

Validation of discrete time‐to‐event prediction models in the presence of competing risks


Heyard, Rachel; Timsit, Jean‐François; Held, Leonhard (2020). Validation of discrete time‐to‐event prediction models in the presence of competing risks. Biometrical journal, 62(3):643-657.

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.

Keywords: area under the curve; calibration slope; competing events; discrete time-to-event model; dynamic prediction models; prediction error; validation.

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.

Keywords: area under the curve; calibration slope; competing events; discrete time-to-event model; dynamic prediction models; prediction error; validation.

Statistics

Citations

Dimensions.ai Metrics
2 citations in Web of Science®
2 citations in Scopus®
Google Scholar™

Altmetrics

Downloads

3 downloads since deposited on 13 Jan 2021
3 downloads since 12 months
Detailed statistics

Additional indexing

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
Scopus Subject Areas:Physical Sciences > Statistics and Probability
Social Sciences & Humanities > Statistics, Probability and Uncertainty
Uncontrolled Keywords:Statistics, Probability and Uncertainty, Statistics and Probability, General Medicine
Language:English
Date:1 May 2020
Deposited On:13 Jan 2021 13:28
Last Modified:01 Feb 2021 16:21
Publisher:Wiley-Blackwell Publishing, Inc.
ISSN:0323-3847
OA Status:Hybrid
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.1002/bimj.201800293
PubMed ID:31368172

Download

Hybrid Open Access

Download PDF  'Validation of discrete time‐to‐event prediction models in the presence of competing risks'.
Preview
Content: Published Version
Filetype: PDF
Size: 692kB
View at publisher
Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)