Gradient boosting machine for survival prediction in studies with general failure-time outcome-dependent sampling design.
- 1Department of Biostatistics, Boston University, Boston, USA.
- 2Department of Biostatistics, University of North Carolina at Chapel Hill, CB#7420, Chapel Hill, NC, 27599-7420, USA.
- 3Department of Biostatistics, University of North Carolina at Chapel Hill, CB#7420, Chapel Hill, NC, 27599-7420, USA. [email protected].
Abstract
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.
Related
- Partial Identification of Survival Prediction Performance under Dependent Right Censoring
- A functional central limit theorem for kernel gradient flow and infinitesimal gradient boosting
- Nonparametric autoregressive estimation of probability distributions with parallel gradient boosting
- Parallel gradient boosting for flexible estimation of conditional distributions
- Gradient boosting models for gallstone disease classification: A comparative study using demographic and clinical data
BibTeX
@article{li2026gradient,
title = {{Gradient boosting machine for survival prediction in studies with general failure-time outcome-dependent sampling design.}},
author = {Haolin Li and Haibo Zhou and Jianwen Cai},
journal = {Lifetime data analysis},
year = {2026},
month = oct,
doi = {10.1007/s10985-026-09732-y},
url = {https://europepmc.org/article/MED/42875555},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Gradient boosting machine for survival prediction in studies with general failure-time outcome-dependent sampling design.
AU - Haolin Li
AU - Haibo Zhou
AU - Jianwen Cai
PY - 2026
DA - 2026/10/15
JO - Lifetime data analysis
DO - 10.1007/s10985-026-09732-y
UR - https://europepmc.org/article/MED/42875555
AB - 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.
SN - 1380-7870
SN - 1572-9249
ER -