Using temporal recalibration to improve the calibration of risk prediction models in competing risk settings when there are trends in survival over time.

ABSTRACT: We have previously proposed temporal recalibration to account for trends in survival over time to improve the calibration of predictions from prognostic models for new patients. This involves first estimating the predictor effects using data from all individuals (full dataset) and then re-estimating the baseline using a subset of the most recent data whilst constraining the predictor effects to remain the same. In this article, we demonstrate how temporal recalibration can be applied in competing risk settings by recalibrating each cause-specific (or subdistribution) hazard model separately. We illustrate this using an example of colon cancer survival with data from the Surveillance Epidemiology and End Results (SEER) program. Data from patients diagnosed in 1995-2004 were used to fit two

SUBMITTER: Booth S 

PROVIDER: S-EPMC10946485 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature