Toward Conditional Distribution Calibration in Survival Prediction (2410.20579v3)

Abstract: Survival prediction often involves estimating the time-to-event distribution from censored datasets. Previous approaches have focused on enhancing discrimination and marginal calibration. In this paper, we highlight the significance of conditional calibration for real-world applications -- especially its role in individual decision-making. We propose a method based on conformal prediction that uses the model's predicted individual survival probability at that instance's observed time. This method effectively improves the model's marginal and conditional calibration, without compromising discrimination. We provide asymptotic theoretical guarantees for both marginal and conditional calibration and test it extensively across 15 diverse real-world datasets, demonstrating the method's practical effectiveness and versatility in various settings.

• The paper introduces CSD-iPOT to achieve reliable conditional and marginal calibration, enhancing individualized survival predictions.
• It leverages conformal prediction with iPOT scores to address calibration challenges in censored data across subpopulations.
• Experimental evaluations on 15 datasets demonstrate significant improvements over traditional methods like Cox models and neural networks.

Toward Conditional Distribution Calibration in Survival Prediction

The paper "Toward Conditional Distribution Calibration in Survival Prediction" proposes an innovative approach to improving calibration in survival analysis, specifically through conditional calibration. The authors introduce the CSD-iPOT framework, which extends beyond traditional marginal calibration techniques to address the need for calibrated predictions in specific sub-populations, enhancing their utility in real-world applications.

Background and Motivation

Survival analysis seeks to predict time-to-event outcomes, a task often complicated by censored data. Traditional models focus on two aspects: discrimination and marginal calibration. Discrimination measures how well predictions distinguish between individuals' survival times, while marginal calibration evaluates alignment between predicted and observed survival probabilities across the entire dataset. However, real-world scenarios frequently demand precise predictions conditioned on specific features, making conditional calibration an important yet underexplored aspect.

The CSD-iPOT Framework

The framework leverages the concept of conformal prediction, implementing a novel post-processing technique that maintains discriminative ability while offering both marginal and conditional calibration guarantees. By using the Individual survival Probability at Observed Time (iPOT) as a conformity score, CSD-iPOT enhances calibration without sacrificing discrimination.

1. Conditional Calibration: The paper underscores the significance of predictions that are calibrated within various subgroups of the population, ensuring that predictions are reliable for individual decision-making, such as treatment prioritization or resource allocation in medical settings.
2. Theoretical Guarantees: One notable feature of CSD-iPOT is its asymptotic guarantee for both marginal and conditional calibration, which standard conformal prediction approaches have struggled with, especially under censorship. This addresses feature-dependent variability in survival distributions, crucial for equitable model performance across different demographic or clinical subgroups.
3. Experimental Evaluation: CSD-iPOT was evaluated on 15 diverse datasets, demonstrating notable improvements in calibration performance. The empirical investigation highlighted CSD-iPOT's robustness, with superior performance in marginal and conditional calibration compared to existing baselines, such as Cox-based methods and neural network-based discrete-time models.

Implications and Future Directions

The paper's contributions extend the body of knowledge in survival analysis by emphasizing conditional calibration, bridging a critical gap in model evaluation metrics. This focus can substantially benefit clinical applications, supporting decision-making processes that rely on personalized survival estimates. Furthermore, the method's computational efficiency positions it as a practical choice for large-scale applications, particularly in healthcare.

Future research could explore extending CSD-iPOT's theoretical framework to provide stronger finite sample guarantees and adapt the approach to continuous-time survival models, broadening its applicability. Additionally, integrating fairness constraints into the calibration process could further enhance the framework's relevance to the assessment of Fair Machine Learning models.

In conclusion, the CSD-iPOT framework offers a substantial enhancement in survival prediction, particularly by addressing gaps in calibration methods. It provides a robust, scalable solution that aligns with the growing demand for personalized and equitable predictive modeling in complex, real-world scenarios.