Accelerated failure model with empirical analysis and application to colon cancer data: Testing and validation
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 est...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-02-01
|
| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016824014169 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|---|
| ISSN: | 1110-0168 |