Gradient boosting machine for survival prediction in studies with general failure-time outcome-dependent sampling design

Outcome-dependent sampling (ODS) designs are widely used in epidemiological and biomedical research as cost-effective alternatives to full cohort studies. While existing research has focused on parameter estimation and inference under specific models, survival prediction in ODS settings remains underexplored. In this paper, we propose a gradient boosting machine tailored for survival prediction in studies conducted using general failure-time ODS designs. Our approach integrates a weighting scheme to correct for sampling bias and improves predictive accuracy. We introduce a weighted empirical risk based on the integrated Brier score for hyperparameter tuning and establish its convergence to the corresponding integrated Brier-score risk. The proposed method is evaluated through extensive simulations and applied to the Cancer Incidence and Mortality of Uranium Miners Study, and is shown to have improved effectiveness in survival prediction. This work advances machine learning methods for ODS studies, offering valuable insights for personalized medicine and clinical decision-making in failure-time ODS settings.