Volume 35, Issue 246 (7-2025)
J Mazandaran Univ Med Sci 2025, 35(246): 72-82 |
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Rahimi E, Yazdani Charati J, Eslami Juybari M, Maleki I, Mohammadpour Tahamtan R A, Sanjarnipour M. Identifying the Optimal Parametric Survival Model with Minimal Estimation Error to Determine Factors Influencing Survival in Colon Cancer Patients. J Mazandaran Univ Med Sci 2025; 35 (246) :72-82
URL: http://jmums.mazums.ac.ir/article-1-20917-en.html
URL: http://jmums.mazums.ac.ir/article-1-20917-en.html
Elahe Rahimi 
, Jamshid Yazdani Charati , Mohammad Eslami Juybari , Iradj Maleki , Raza ali Mohammadpour Tahamtan , Murteza Sanjarnipour
, Jamshid Yazdani Charati , Mohammad Eslami Juybari , Iradj Maleki , Raza ali Mohammadpour Tahamtan , Murteza Sanjarnipour
Abstract: (88 Views)
Background and purpose: Colon cancer accounts for approximately 10% of all cancer cases. Identifying the most appropriate model for data analysis can significantly improve the accuracy of survival estimates. Therefore, this study aimed to identify the best parametric survival model with the least error for estimating survival in patients with colon cancer.
Materials and methods: In this historical cohort study, data from 761 patients with colon cancer in Mazandaran Province, referred between 2012 and 2017 and followed up until 2019, were reviewed. The Kaplan-Meier method was used to estimate patient survival. The Brier score index was employed to identify the model with the lowest estimation error. Data analysis was conducted using R software version 3.6.1, with a significance level set at 0.05.
Results: The patients studied ranged in age from 17 to 91 years. The mean age was 60.14 ± 8.35 years, and 79.4% of patients were over 50 years old. Among the patients, 55.6% (423 patients) were males with a mean age of (61.14 ± 9.57) years and 44.4% (338 patients) were females with a mean age of (59.5 ± 13.96) years. The median and mean life expectancy of the patients were determined to be 60 and 53.71 ± 2.07 months, respectively. The three-, five-, and seven-year survival rates were 70%, 49%, and 37%, respectively. In this study, the Weibull parametric model was identified as the best model with the highest accuracy and the lowest error in predicting survival and identifying the most important related factors.
Conclusion: In this study, the optimal model was the Weibull model, given that the failure rate increased uniformly over time, resulting in a better fit and lower error compared to the other models studied. Therefore, choosing the Weibull model can be useful for analyzing data with this distribution.
Materials and methods: In this historical cohort study, data from 761 patients with colon cancer in Mazandaran Province, referred between 2012 and 2017 and followed up until 2019, were reviewed. The Kaplan-Meier method was used to estimate patient survival. The Brier score index was employed to identify the model with the lowest estimation error. Data analysis was conducted using R software version 3.6.1, with a significance level set at 0.05.
Results: The patients studied ranged in age from 17 to 91 years. The mean age was 60.14 ± 8.35 years, and 79.4% of patients were over 50 years old. Among the patients, 55.6% (423 patients) were males with a mean age of (61.14 ± 9.57) years and 44.4% (338 patients) were females with a mean age of (59.5 ± 13.96) years. The median and mean life expectancy of the patients were determined to be 60 and 53.71 ± 2.07 months, respectively. The three-, five-, and seven-year survival rates were 70%, 49%, and 37%, respectively. In this study, the Weibull parametric model was identified as the best model with the highest accuracy and the lowest error in predicting survival and identifying the most important related factors.
Conclusion: In this study, the optimal model was the Weibull model, given that the failure rate increased uniformly over time, resulting in a better fit and lower error compared to the other models studied. Therefore, choosing the Weibull model can be useful for analyzing data with this distribution.
Keywords: survival analysis, survival parametric model, Akaike index, Brier score, Cox-Snell residual, colon cancer
Type of Study: Research(Original) |
Subject:
Biostatistics
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