Confidence intervals for the Cox model test error from cross-validation.
Stat Med
; 42(25): 4532-4541, 2023 11 10.
Article
en En
| MEDLINE
| ID: mdl-37580906
ABSTRACT
Cross-validation (CV) is one of the most widely used techniques in statistical learning for estimating the test error of a model, but its behavior is not yet fully understood. It has been shown that standard confidence intervals for test error using estimates from CV may have coverage below nominal levels. This phenomenon occurs because each sample is used in both the training and testing procedures during CV and as a result, the CV estimates of the errors become correlated. Without accounting for this correlation, the estimate of the variance is smaller than it should be. One way to mitigate this issue is by estimating the mean squared error of the prediction error instead using nested CV. This approach has been shown to achieve superior coverage compared to intervals derived from standard CV. In this work, we generalize the nested CV idea to the Cox proportional hazards model and explore various choices of test error for this setting.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Proyectos de Investigación
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Stat Med
Año:
2023
Tipo del documento:
Article
País de afiliación:
Estados Unidos