Distribution of Computerized Datasets To Fit Weibull, Log-Logistics and Gompertz Survival Models

Bilkisu Muhammad Bello¹, Farouq Ndamadu Musa², and Ibrahim Aliyu Hassan^3*

Abstract

The lack of detailed and analytic study on the risk factors of Breast cancer in Nigerian presents us with the challenge of not knowing the risk factors distinct to the Nigerian settings. Effectively handling the breast cancer treatment in co-infected patients is delicate. Some challenges to the treatment of breast cancer infection include drug-to-drug interactions between procedures required to treat the cancer cells, thus the hormonal drugs burdens patient’s inability to comply with dosage intake thereby making women with breast cancer prone to developing other forms of cancer. The aim of the research was to analyzed distributed dataset to fit Weibull, Log-logistics and Gompertz survival models. The research methodology involved the collection of data cases on oncological study analyzed using descriptive statistics and parametric survival models. The result shows that the Cramer-von Misses (W), the Anderson Darling (A) and the Kolmogorov Smirnov (D) statistics, Log-logistic distribution has greater p-value than other distributions, hence it is the best fit for survival times of a group of patients given chemotherapy treatment. The research concluded that Gompertz model was the best fitted model to survival times of a group of patients given chemotherapy treatment data, while log-logistic distribution was the best fit distribution for the data. It also concluded that Weibull and log-logistic models perform better than Gompertz model in fatigue fracture data, while log-logistic distribution was the best fit for the data and Gompertz model and the distribution was the best fit to nicotine measurements.

Keywords

Gompertz; Weibull; log-logistic; distribution

Cite This Article

Bello, B. M., Musa, F. N., Hassan, I. A. (2023). Distribution of Computerized Datasets To Fit Weibull, Log-Logistics and Gompertz Survival Models. International Journal of Scientific Advances (IJSCIA), Volume 4| Issue 3: May-Jun 2023, Pages 359-366, URL: https://www.ijscia.com/wp-content/uploads/2023/05/Volume4-Issue3-May-Jun-No.440-359-366.pdf

Volume 4 | Issue 3: May-Jun 2023