Cargando…
Comparing machine learning approaches to incorporate time-varying covariates in predicting cancer survival time
The Cox proportional hazards model is commonly used in evaluating risk factors in cancer survival data. The model assumes an additive, linear relationship between the risk factors and the log hazard. However, this assumption may be too simplistic. Further, failure to take time-varying covariates int...
Autores principales: | Cygu, Steve, Seow, Hsien, Dushoff, Jonathan, Bolker, Benjamin M. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9877029/ https://www.ncbi.nlm.nih.gov/pubmed/36697455 http://dx.doi.org/10.1038/s41598-023-28393-7 |
Ejemplares similares
-
Generating survival times to simulate Cox proportional hazards models with time-varying covariates
por: Austin, Peter C
Publicado: (2012) -
Bias of time‐varying exposure effects due to time‐varying covariate measurement strategies
por: Penning de Vries, Bas B. L., et al.
Publicado: (2021) -
Generating Survival Times Using Cox Proportional Hazards Models with Cyclic and Piecewise Time-Varying Covariates
por: Huang, Yunda, et al.
Publicado: (2020) -
Using Time-Varying Covariates in Multilevel Growth Models
por: McCoach, D. Betsy, et al.
Publicado: (2010) -
Bayesian imputation of time-varying covariates in linear mixed
models
por: Erler, Nicole S, et al.
Publicado: (2017)