Work

Accelerated failure model with empirical analysis and application to colon cancer data: Testing and validation

Year: 2024

Type: article

Abstract: This paper introduces and examines a novel accelerated failure time (AFT) model, a versatile multi-parameter regression model suitable for representing diverse time-to-event datasets. Serving as an alternative to the Cox model and other established AFT models, the proposed model’s parameters are estimated through maximum likelihood estimation. A Monte Carlo simulation study is conducted to evaluate the proposed model’s performance across various scenarios, demonstrating its superiority over competing AFT models. Application of the proposed model to a colon cancer dataset reveal its superior parametric fit compared to the Cox proportional hazards (Cox-PH) model and other competing AFT models, as indicated by information criteria and goodness-of-fit measures. This new AFT model contributes to the repertoire of tools/models for analyzing survival datasets and offers an advantageous hazard-based regression approach.

Source: Alexandria Engineering Journal

DOI: 10.1016/j.aej.2024.10.122

Authors: John Abonongo, Anuwoje Ida L. Abonongo, Abdussalam Aljadani, Mahmoud M. Mansour, Haitham M. Yousof

Institutions: University of Technology and Applied Sciences, Taibah University, Benha University

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: $1,300