Apply Parametric Shared Frailty Models to Colorectal Cancer Patients

  • Hevi J. Hameed Department of Statistics and informatics, College of Administration and Economics, Sulaimani ‎University-Sulaymaniyah, Kurdistan Region, Iraq https://orcid.org/0000-0002-5756-6043
  • Mohammad M. Faqe Department of Statistics and informatics, College of Administration and Economics, Sulaimani ‎University-Sulaymaniyah, Kurdistan Region, Iraq https://orcid.org/0000-0002-8177-9620
DOI: https://doi.org/10.24086/cuesj.v6n2y2022.pp119-124
Keywords: Survival analysis, Recurrent events, Inverse Gaussian shared frailty, Heterogeneity, Gamma shared frailty model

Abstract

 Colorectal cancer is a combination of colon and rectal cancer that indicates an abnormal growth of cells in either the colon or rectum and is named according to its original location. After treatment, cancer may return to the primary site of the original tumor or to a different location in the body once or more, which is called recurrent. This paper aimed to model this type of data from 128 colorectal cancer patients collected at Hiwa hospital in Sulaimani considering the gamma shared and inverse Gaussian shared frailty models for analyzing the patient’s survival times with colorectal cancer recurrence and estimate the prognostic factor’s impact on their survival. Comparison of the results of these models with those without a frailty model using Weibull, log-logistic, and lognormal as a baseline distribution. To identify the best model for the data the (AIC) Akaike Information Criterion and (BIC) Bayesian Information Criterion were also used. Results showed that the cancer stage was the only significant factor affecting survival in recurrent events, as well as evidence of existing heterogeneity in colorectal patients. According to (AIC) and (BIC), the Weibull as baseline distribution with shared Gamma frailty model proved the most efficient model for the colorectal recurrent data. In Conclusion, the shared frailty model is better than no frailty when analyzing this type of data.

Downloads

Download data is not yet available.

Author Biographies

Hevi J. Hameed, Department of Statistics and informatics, College of Administration and Economics, Sulaimani ‎University-Sulaymaniyah, Kurdistan Region, Iraq

Hevi J. Hameed is an assistant researcher at the Department of Statistics and Informatics, College of Administration and Economics, Sulaimani University. She got a B.Sc. degree in Statistics from Sulaimani University – Sulaymaniyah, she is a master’s student in the Department of Statistics and informatics her thesis interests are in Apply Parametric Shared Frailty Models to Colorectal Cancer patient.

Mohammad M. Faqe, Department of Statistics and informatics, College of Administration and Economics, Sulaimani ‎University-Sulaymaniyah, Kurdistan Region, Iraq

Mohammad M. Faqe is an Assistant Prof. at the Department of Statistics and Informatics, College of Administration and Economics, Sulaimani University. He got a B.Sc. degree in Statistics an M.Sc. degree in regression and experimental design and a Ph.D. degree in Statistics from Sulaimani University. His thesis interests are in Apply Parametric Shared Frailty Models to Colorectal Cancer

References

M. R. Karim and M. A. Islam. Reliability and Survival Analysis. Singapore: Springer Nature Pvt Ltd., 2019.

D. D. Hanagal. Modeling Survival Data Using Frailty Models. Boca Raton, FL: Chapman & Hall/CRC, 2011.

R. G. Gutierrez. Parametric frailty and shared frailty survival models. The Stata Journal, vol. 2, no. 1, pp. 22-44, 2002.

A. Wienke. Frailty Models in Survival Analysis. London, United Kingdom: Chapman and Hall/CRC, 2010.

G. Grover and D. Seth. Application of frailty models on advance liver disease using gamma as frailty distribution. SRL, vol. 3, pp. 42-50, 2014.

H. Kobayashi, H. Mochizuki, K. Sugihara, T. Morita, K. Kotake, T. Teramoto, S. Kameoka, Y. Saito, K. Takahashi, K. Hase, M. Oya, K. Maeda, T. Hirai, M. Kameyama, K. Shirouzu and T. Muto. Characteristics of recurrence and surveillance tools after curative resection for colorectal cancer: A multicenter study. Surgery, vol. 141, no. 1, pp. 67-75, 2007.

U. Abdulkarimova. Frailty Models for Modelling Heterogeneity. Canada: McMasters University, 2013.

K. Adeleke and G. Grover. Parametric frailty models for clustered survival data: Application to recurrent asthma attack in infants. Journal of Statistics Applications and Probability Letters, vol. 6, pp. 89-99, 2019.

A. Gebeyehu. Survival Analysis of Time-to-first Birth after Marriage among Women in Ethiopia: Application of Parametric Shared Frailty Model. Jimma: Department of Statistics, College of Natural Sciences, Jimma University, 2015.

K. D. Fentaw, S. M. Fenta, H. B. Biresaw and S. S. Mulugeta, Time to first antenatal care visit among pregnant women in Ethiopia: Secondary analysis of EDHS 2016; Application of AFT shared frailty models. Archives of Public Health, vol. 79, no. 1, pp. 1-14, 2021.

P. K. Swain and G. Grover. Determination of predictors associated with HIV/AIDS patients on ART using accelerated failure time model for interval censored survival data. American Journal of Biostatistics, vol. 6, pp. 12-19, 2016

Published
2022-11-01
How to Cite
1.
Hameed H, Faqe M. Apply Parametric Shared Frailty Models to Colorectal Cancer Patients. cuesj [Internet]. 1Nov.2022 [cited 24Apr.2024];6(2):119-24. Available from: https://journals.cihanuniversity.edu.iq/index.php/cuesj/article/view/734
Issue
Vol 6 No 2 (2022)
Section
Research Article

Copyright (c) 2022 Hevi J. Hameed, Mohammad M. Faqe

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Authors who publish with this journal agree to the following terms:
1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License [CC BY-NC-ND 4.0] that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).