Aligning Data-Driven Predictors with Allocation: A Decision-Focused Approach to Survival Analysis

2026-06-01

AI Summary

Although traditional survival prediction models achieve strong performance on metrics such as the concordance index (C-index), they often perform poorly—sometimes worse than random selection—in downstream decision-making tasks like organ allocation. This work proposes a decision-oriented approach to survival analysis by introducing normalized discounted cumulative gain (NDCG), a metric from information retrieval, directly aligning prediction objectives with allocation policies. We develop a ranking-based evaluation method tailored for right-censored data and construct a bootstrap-based NDCG optimization framework to fine-tune existing survival models. Evaluated on real-world U.S. heart transplant data, our method improves baseline NDCG by 50–100%, potentially saving tens of thousands of life-years annually while providing theoretical guarantees on allocation performance.

Abstract

Machine learning predictors have become essential tools for guiding automated decision making. However, a major misalignment persists: predictive models are typically optimized in terms of standard statistical metrics in isolation from the algorithmic tasks they inform. We highlight this incongruity in the high-stakes domain of organ allocation by demonstrating that any algorithm relying on (even highly accurate) survival predictors optimized for standard metrics -- such as the Concordance index (C-index) -- can yield arbitrarily poor outcomes when used for allocation, failing to guarantee utility better than a uniform random selection. To bridge the gap between survival analysis and policy optimization, we introduce a decision-focused learning approach based on optimizing normalized discounted cumulative gain (NDCG), a mainstay metric in information retrieval. We establish the utility of NDCG in survival analysis by proving that it translates to guarantees on the performance of allocation. Empirically, we propose a bootstrapping approach to optimize the NDCG of existing survival models. Unlike prior work, we also address the challenge of right censorship when evaluating ranking. On historical heart transplant data from the US, our method dramatically boosts the NDCG of baseline models by 50-100%, which translates to tens of thousands of additional life years gained annually when deployed for transplant allocation. We anticipate that our framework will find broader applications in decision making with predictions.

Problem

• survival analysis
• organ allocation
• decision-focused learning
• predictive modeling
• ranking evaluation

Innovation

• decision-focused learning
• survival analysis
• NDCG
• organ allocation
• right censoring