A review on statistical and machine learning competing risks methods
© 2024 The Authors. Biometrical Journal published by Wiley-VCH GmbH..
When modeling competing risks (CR) survival data, several techniques have been proposed in both the statistical and machine learning literature. State-of-the-art methods have extended classical approaches with more flexible assumptions that can improve predictive performance, allow high-dimensional data and missing values, among others. Despite this, modern approaches have not been widely employed in applied settings. This article aims to aid the uptake of such methods by providing a condensed compendium of CR survival methods with a unified notation and interpretation across approaches. We highlight available software and, when possible, demonstrate their usage via reproducible R vignettes. Moreover, we discuss two major concerns that can affect benchmark studies in this context: the choice of performance metrics and reproducibility.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:66 |
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| Enthalten in: |
Biometrical journal. Biometrische Zeitschrift - 66(2024), 2 vom: 27. Feb., Seite e2300060 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Monterrubio-Gómez, Karla [VerfasserIn] |
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| Links: |
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| Themen: |
Competing risks |
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| Anmerkungen: |
Date Completed 15.02.2024 Date Revised 27.02.2024 published: Print Citation Status MEDLINE |
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| doi: |
10.1002/bimj.202300060 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a When modeling competing risks (CR) survival data, several techniques have been proposed in both the statistical and machine learning literature. State-of-the-art methods have extended classical approaches with more flexible assumptions that can improve predictive performance, allow high-dimensional data and missing values, among others. Despite this, modern approaches have not been widely employed in applied settings. This article aims to aid the uptake of such methods by providing a condensed compendium of CR survival methods with a unified notation and interpretation across approaches. We highlight available software and, when possible, demonstrate their usage via reproducible R vignettes. Moreover, we discuss two major concerns that can affect benchmark studies in this context: the choice of performance metrics and reproducibility | ||
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