Parametric and Survival Analysis of a Sample of Colon Cancer Patients at the National Cancer Institute in Misurata (Journal of Pure & Applied Sciences, 2026)
This article aimed to analyze the survival time of colon cancer patients using parametric models, given their ability to provide more accurate estimates compared to non-parametric models. The study relied on data from colon cancer patients at the National Cancer Center in Misrata (January 2019 to December 2023). The variables included sex, tumor grade, and whether they received radiotherapy or chemotherapy. The analytical method was used to apply parametric models, specifically the exponential, Whipple, normal logarithm, and Rayleigh distributions, and to determine the most suitable model based on the Akaiqui Information Criterion. The results showed that the data did not follow a common pattern for some of the study variables. Specifically, the survival times of grade II patients and those who did not receive radiotherapy followed a Rayleigh distribution. Conversely, the survival times of grade I and III patients, as well as those who received radiotherapy, followed a two-parameter Rayleigh distribution. The data showed a common pattern for some study variables. The one-parameter exponential distribution was found to be optimal for representing survival times for both males and females, while the one-parameter Whipple distribution was the most suitable for survival times for patients who received chemotherapy and those who did not. The results of variance tests and residual probability analysis indicated that colon cancer is directly associated with tumor grade and radiotherapy in terms of survival time. Colon cancer is not directly associated with sex or chemotherapy in terms of survival time. These results confirm that parametric models are an accurate tool for analyzing survival data and enable the deduction of deeper relationships between clinical and demographic factors and survival. This study is distinguished from previous studies as it is the first to analyze colon cancer survival in Libya using parametric models and integrate clinical and statistical analysis. This makes it a valuable scientific contribution that fills a research gap in the field of cancer survival analysis in the Libyan and Arab contexts.