Improving overall risk ranking via subgroup-level information borrowing in survival risk stratification

Effective risk stratification is essential for providing tailored therapies, improving patient outcomes, and optimizing healthcare resources by identifying sub-populations with similar health risks. However, accurate risk ranking is challenging in the presence of heterogeneous subgroups. Under these instances, subgroup-level information can be leveraged to refine the overall risk ranking. We propose a novel approach that integrates within-subgroup risk ranking percentiles to enhance the overall cohort risk stratification. This method uses both a global model and subgroup-specific models along with optimized weights to improve discriminatory performance across the entire cohort. The proposed method is validated through extensive simulations and applied to a study of end-stage renal disease patients awaiting kidney transplantation.