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!
|
| _version_ | 1825206974357176320 |
|---|---|
| author | John Abonongo Anuwoje Ida L. Abonongo Abdussalam Aljadani Mahmoud M. Mansour Haitham M. Yousof |
| author_facet | John Abonongo Anuwoje Ida L. Abonongo Abdussalam Aljadani Mahmoud M. Mansour Haitham M. Yousof |
| author_sort | John Abonongo |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ba45b512b9f846a690fef2ac633844de |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-ba45b512b9f846a690fef2ac633844de2025-02-07T04:46:58ZengElsevierAlexandria Engineering Journal1110-01682025-02-01113391408Accelerated failure model with empirical analysis and application to colon cancer data: Testing and validationJohn Abonongo0Anuwoje Ida L. Abonongo1Abdussalam Aljadani2Mahmoud M. Mansour3Haitham M. Yousof4Department of Statistics and Actuarial Science, School of Mathematical Sciences, C. K. Tedam University of Technology and Applied Sciences, Navrongo, Ghana; Corresponding author.Department of Statistics and Actuarial Science, School of Mathematical Sciences, C. K. Tedam University of Technology and Applied Sciences, Navrongo, GhanaDepartment of Management, College of Business Administration in Yanbu, Taibah University, Al-Madinah, Al-Munawarah 41411, Kingdom of Saudi ArabiaManagement Information Systems Department, Taibah University, Yanbu 46421, Saudi Arabia; Department of Statistics, Mathematics and Insurance, Benha University, EgyptDepartment of Statistics, Mathematics and Insurance, Benha University, EgyptThis 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.http://www.sciencedirect.com/science/article/pii/S1110016824014169Hazard-based regression modelAccelerated failure timeTime-to-eventCox-PHSchoenfeld residual testMaximum likelihood |
| spellingShingle | John Abonongo Anuwoje Ida L. Abonongo Abdussalam Aljadani Mahmoud M. Mansour Haitham M. Yousof Accelerated failure model with empirical analysis and application to colon cancer data: Testing and validation Alexandria Engineering Journal Hazard-based regression model Accelerated failure time Time-to-event Cox-PH Schoenfeld residual test Maximum likelihood |
| title | Accelerated failure model with empirical analysis and application to colon cancer data: Testing and validation |
| title_full | Accelerated failure model with empirical analysis and application to colon cancer data: Testing and validation |
| title_fullStr | Accelerated failure model with empirical analysis and application to colon cancer data: Testing and validation |
| title_full_unstemmed | Accelerated failure model with empirical analysis and application to colon cancer data: Testing and validation |
| title_short | Accelerated failure model with empirical analysis and application to colon cancer data: Testing and validation |
| title_sort | accelerated failure model with empirical analysis and application to colon cancer data testing and validation |
| topic | Hazard-based regression model Accelerated failure time Time-to-event Cox-PH Schoenfeld residual test Maximum likelihood |
| url | http://www.sciencedirect.com/science/article/pii/S1110016824014169 |
| work_keys_str_mv | AT johnabonongo acceleratedfailuremodelwithempiricalanalysisandapplicationtocoloncancerdatatestingandvalidation AT anuwojeidalabonongo acceleratedfailuremodelwithempiricalanalysisandapplicationtocoloncancerdatatestingandvalidation AT abdussalamaljadani acceleratedfailuremodelwithempiricalanalysisandapplicationtocoloncancerdatatestingandvalidation AT mahmoudmmansour acceleratedfailuremodelwithempiricalanalysisandapplicationtocoloncancerdatatestingandvalidation AT haithammyousof acceleratedfailuremodelwithempiricalanalysisandapplicationtocoloncancerdatatestingandvalidation |