Non-Crossing Deep Quantile Regression for Time-to-Event Analysis

Music City Center 

Deep learning (DL) has garnered increasing attention in time-to-event prediction due to its ability to model complex nonlinear relationships while offering greater flexibility than traditional methods. In this work, we propose a non-crossing quantile regression framework that estimates multiple quantiles of event time simultaneously in right-censored survival data while ensuring valid quantile ordering. Unlike existing approaches that rely on multilayer perceptrons (MLPs), we leverage Kolmogorov-Arnold Networks (KAN) for efficient function approximation and Transformers for capturing intricate feature dependencies through self attention. To provide theoretical insights, we establish upper bounds on the prediction error of our quantile estimators. We evaluate our framework on both simulated and real-world datasets, benchmarking its performance against existing quantile-based and non-quantile-based methods. As shown in these experiments, our DL framework can achieve 30% to 50% error reduction in real data analysis compared with various baseline methods.

Deep Learning

Non-Crossing Quantile Regression

Time-to-Event Prediction

Transformer

KAN

Survival Analysis 

Section on Statistical Learning and Data Science