Authors: Lilian W. Bialokozowicz, Hoang M. Le, Tristan Sylvain, Peter A. I. Forsyth, Vineel Nagisetty, Greg Mori

Published on: February 02, 2024

Impact Score: 8.3

Arxiv code: Arxiv:2402.01955

Summary

• What is new: Introduction of the Orthogonal Polynomials Quadrature Algorithm (OPSurv) for survival analysis, a novel method that offers time-continuous functional outputs for single and competing risks scenarios.
• Why this is important: The challenge in survival analysis of accurately modeling time-to-event data, especially under competing risks scenarios, while preventing overfitting.
• What the research proposes: OPSurv employs orthogonal polynomials for probability density decomposition and Cumulative Incidence Function estimates, enhancing model control and expressiveness.
• Results: Empirical validations and theoretical justifications reveal OPSurv’s robust performance in survival analysis, showcasing its advantage over existing methods.

Technical Details

Technological frameworks used: Gauss–Legendre quadrature

Models used: OPSurv algorithm

Data used: Cumulative Incidence function, orthogonal polynomials for probability density decomposition

Potential Impact

Healthcare analytics, insurance companies, and any industry reliant on survival analysis for risk assessment.

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