Work

Estimating Quantiles and Cure Rate in Survival Data: A Parametric Regression Framework Using the Exponentiated Weibull Distribution

Year: 2025

Type: article

Abstract: This paper presents a parametric quantile regression model for survival data that incorporates a cure fraction, addressing limitations of traditional survival models related to clinical interpretability and their limited capacity to account for cured individuals. The proposed model is built upon the exponentiated Weibull distribution and employs a logarithmic link between survival quantiles and covariates, offering a flexible and interpretable framework. Parameter estimation is conducted via an expectation-maximization algorithm within the maximum likelihood framework. Monte Carlo simulation studies assess the model’s performance under varying censoring levels and sample sizes, confirming its ability to yield stable estimates and reliable inference across diverse scenarios. An application to gastric cancer data demonstrates the model’s effectiveness in capturing heterogeneity and uncovering clinically relevant patterns. This methodology advances survival analysis by integrating cure fraction modeling with quantile-based inference in a coherent and robust manner.

Source: Journal of Statistical Theory and Applications

DOI: 10.1007/s44199-025-00121-2

Authors: Patrick Borges, Agatha Rodrigues

Institutions: Universidade Federal do Espírito Santo, Universidade de São Paulo, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo

Language: English

Topic: Statistical Distribution Estimation and Applications

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,490