Journal article
Comparing machine learning approaches to incorporate time-varying covariates in predicting cancer survival time
Abstract
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 into account, if present, may lower prediction accuracy. In this retrospective, population-based, prognostic study of data from patients diagnosed with …
Authors
Cygu S; Seow H; Dushoff J; Bolker BM
Journal
Scientific Reports, Vol. 13, No. 1,
Publisher
Springer Nature
DOI
10.1038/s41598-023-28393-7
ISSN
2045-2322