OPSurv: Orthogonal Polynomials Quadrature Algorithm for Survival Analysis
This addresses survival analysis for medical or reliability domains, offering a novel method for competing risks scenarios.
The paper tackled the problem of survival analysis with competing risks by introducing OPSurv, a method that provides time-continuous functional outputs, resulting in enhanced model expressiveness and control while countering overfitting.
This paper introduces the Orthogonal Polynomials Quadrature Algorithm for Survival Analysis (OPSurv), a new method providing time-continuous functional outputs for both single and competing risks scenarios in survival analysis. OPSurv utilizes the initial zero condition of the Cumulative Incidence function and a unique decomposition of probability densities using orthogonal polynomials, allowing it to learn functional approximation coefficients for each risk event and construct Cumulative Incidence Function estimates via Gauss--Legendre quadrature. This approach effectively counters overfitting, particularly in competing risks scenarios, enhancing model expressiveness and control. The paper further details empirical validations and theoretical justifications of OPSurv, highlighting its robust performance as an advancement in survival analysis with competing risks.