Improving Survival Models in Health Care by Balancing Imbalanced Cohorts: A Novel Approach

We explore whether survival model performance in underrepresented high- and low-risk subgroups-regions of the prognostic spectrum where clinical decisions are most consequential-can be improved through targeted restructuring of the training data set. Rather than modifying model architecture, we propose a novel risk-stratified sampling method that addresses imbalances in prognostic subgroup density to support more reliable learning in underrepresented tail strata.

We introduce a novel methodology that partitions patients by baseline prognostic risk and applies matching within each stratum to equalize representation across the risk distribution. We implement this framework on a cohort of 1,799 patients with resected colorectal liver metastases (CRLM), including 1,197 who received adjuvant chemotherapy and 602 who did not. All models used in this study are Cox proportional hazards models trained on the same set of selected variables. Model performance is assessed via Harrell's C index and Integrated Calibration Index, with internal validation using Efron's bias-corrected bootstrapping. External validation is conducted on two independent CRLM data sets.

Cox models trained on risk-balanced cohorts showed consistent improvements in internal validation compared with models trained on the full data set. The proposed approach preserved overall model calibration while noticeably improving stratified C index values in underrepresented high- and low-risk strata of the external cohorts.

Our findings suggest that survival model performance in observational oncology cohorts can be meaningfully improved through targeted rebalancing of the training data across prognostic risk strata. This approach offers a practical and model-agnostic complement to existing methods, especially in applications where predictive reliability across the full risk continuum is critical to downstream clinical decisions.