Open AccessApplication of Mixture Cure Models in Determining the Survival of Patients with Cervical Cancer
Author(s) -
Rasool Gharaaghaji,
Haleh Ayatollahi,
Masoumeh Mohammadpour,
Javid Fereidoni,
Hamid Reza Khalkhali
Publication year - 2021
Publication title -
international journal of cancer management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.251
H-Index - 19
eISSN - 2538-4422
pISSN - 2538-497X
DOI - 10.5812/ijcm.93966
Subject(s) - medicine , cervical cancer , survival analysis , population , cancer , logistic regression , survival function , sample size determination , statistics , mathematics , environmental health
Background: Cervical cancer is the fourth most common cancer among women and the seventh most common worldwide. Objectives: The present study aimed at investigating the usability of cure models in analyzing patients' survival. Besides, the factors affecting the long-term and short-term survival of the patients were determined, using Weibull, log-logistic, and log-normal models. Methods: The sample population of the study included 109 female patients with cervical cancer referred to Motahhari Hospital of Urmia (West Azerbaijan province) from 2004 to 2015. The cure survival analysis was used to determine the patients' survival. Results: The mean and standard deviation age at diagnosis was 50.1 ± 11.7 years. The patients' age, age at marriage, and the disease relapse were significant in the single-variable model on the long-term survival function of the patients. Moreover, the findings showed that Cured Log Logistic Parametric Model was more suitable for analyzing survival data in West Azerbaijan Province, Iran. The relapse variable was significant for all the parametric models. Conclusions: Given the divided sample population into immune and susceptible groups, the mixture cure models can be used to analyze the long-term and short-term survival of the patients with cervical cancer. Moreover, these models can be used to recognize the factors affecting both groups simultaneously.