A review on statistical and machine learning competing risks methods.
Biom J
; 66(2): e2300060, 2024 Mar.
Article
em En
| MEDLINE
| ID: mdl-38351217
ABSTRACT
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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Base de dados:
MEDLINE
Assunto principal:
Modelos Estatísticos
/
Aprendizado de Máquina
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article