How Breast Cancer Survival Predictions Change over Time: A Large SEER Study of Model Calibration and Updating

Background/Objectives: Survival models can retain their ability to rank patients while their absolute-risk predictions become less accurate over time. We compared temporal calibration drift and updating across statistical and machine-learning models under competing risks. Methods: This retrospective SEER study included 881,201 women with breast cancer. Cause-specific ridge Cox, elastic-net Cox, random survival forest, and DeepSurv models were trained on diagnoses from 2004–2007 and evaluated across five two-year eras from 2008–2017. We compared fixed prediction, isotonic recalibration using the preceding two-year window, and refitting using the preceding four-year window for 5-year mortality. Results: Fixed-model breast cancer-specific mortality overprediction increased from 0.52–1.22 percentage points (pp) in 2008–2009 to 1.55–2.41 pp in 2016–2017. Discrimination remained stable or improved slightly; the latest-era inverse-probability-of-censoring-weighted area under the curve was 0.870–0.884. Latest-era residual mean calibration bias was at most 0.36 pp after isotonic recalibration and 0.57–0.97 pp after refitting. The coupled-bootstrap interval for the random survival forest bias difference included zero; refitting yielded lower Brier scores for random survival forest and DeepSurv. Calibration patterns differed by age and predicted risk. Three families first exceeded the illustrative 2-pp reference value in 2016–2017; random survival forest remained below it. Conclusions: Recalibration reduced average bias, but its advantage depended on the model and metric. Prospective evaluation must account for delayed outcome availability before these updating protocols can inform clinical practice.