Work

Quantile Reparameterized Regression Model and Machine Learning with Long-Term Survivors

Year: 2026

Type: article

Abstract: We propose a quantile-based survival model that accounts for a cure fraction. By modeling unobserved event causes with a negative binomial distribution and time-to-event with the generalized log-logistic Weibull distribution, we estimate parameters using a frequentist framework. We validated our model and applied it to prostate cancer data to prove its practical use. Comparing it to a Random Survival Forest model, we highlight how different approaches handle long-term survival, ultimately weighing the balance between flexibility, interpretability, and predictive performance.

Source: Axioms

DOI: 10.3390/axioms15060451

Authors: Edwin M. M. Ortega, Gabriela M. Rodrigues, Valdemiro P. Vigas, Gauss M. Cordeiro, Vicente G. Cancho, Michael W. Kattan

Institutions: Universidade de São Paulo, Universidade Federal de Mato Grosso do Sul, Universidade Federal de Pernambuco, Cleveland Clinic

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,896