Boosting methods for interval-censored data with regression and classification

TL;DR

This paper introduces novel nonparametric boosting methods tailored for regression and classification with interval-censored data, addressing a common challenge in survival analysis and related fields.

Contribution

The work develops scalable boosting algorithms using censoring unbiased transformations, with theoretical guarantees and improved performance on interval-censored data.

Findings

Robust performance demonstrated in finite-sample scenarios

Theoretical properties including optimality established

Methods effectively handle complex censoring structures

Abstract

Boosting has garnered significant interest across both machine learning and statistical communities. Traditional boosting algorithms, designed for fully observed random samples, often struggle with real-world problems, particularly with interval-censored data. This type of data is common in survival analysis and time-to-event studies where exact event times are unobserved but fall within known intervals. Effective handling of such data is crucial in fields like medical research, reliability engineering, and social sciences. In this work, we introduce novel nonparametric boosting methods for regression and classification tasks with interval-censored data. Our approaches leverage censoring unbiased transformations to adjust loss functions and impute transformed responses while maintaining model accuracy. Implemented via functional gradient descent, these methods ensure scalability and adaptability. We rigorously establish their theoretical properties, including optimality and mean squared error trade-offs. Our proposed methods not only offer a robust framework for enhancing predictive accuracy in domains where interval-censored data are common but also complement existing work, expanding the applicability of existing boosting techniques. Empirical studies demonstrate robust performance across various finite-sample scenarios, highlighting the practical utility of our approaches.