biostudies-literatureCygu SNatural Sciences and Engineering Research Council of CanadaInstitute for Clinical Evaluative SciencesCanadian Institutes of Health ResearchCIHR1370https://www.ebi.ac.uk/biostudies/studies/S-EPMC9877029biostudies-literatureUnknown13(1)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 cancer from 2008 to 2015 in Ontario, Canada, we applied machine learning-based time-to-event prediction methods and compared their predictive performance in two sets of analyses: (1) yearly-cohort-based time-invariant and (2) fully time-varying covariates analysis. Machine learning-based methods-gradient boosting model (gbm), random survival forest Scientific reportsComparing machine learning approaches to incorporate time-varying covariates in predicting cancer survival time.PMC9877029379009Bolker BMDushoff JSeow HCygu SfalseComparing 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 into account, if present, may lower prediction accuracy. In this retrospective, population-based, prognostic study of data from patients diagnosed with cancer from 2008 to 2015 in Ontario, Canada, we applied machine learning-based time-to-event prediction methods and compared their predictive performance in two sets of analyses: (1) yearly-cohort-based time-invariant and (2) fully time-varying covariates analysis. Machine learning-based methods-gradient boosting model (gbm), random survival forest 2023-01-01T00:00:00Z2023 Jan2025-04-22T06:31:28.398Z2025-04-05T21:47:05.603ZS-EPMC98770293669745510.1038/s41598-023-28393-7