Browsing by Author "Bello Ishola Sanni"

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    A New Lifetime Distribution and its Application to Cancer Data
    (Journal of Biostatistics and Epidemiology, 2023-12-15) Samuel Adewale Aderoju; Nihimat Iyebuhola Aleshinloye; Bako Lukmon Taiwo; Bello Ishola Sanni
    Introduction: Recently, researchers have introduced new generated families of univariate lifetime distributions. These new generators are obtained by adding one or more extra shape parameters to the underlying distribution or compounding two distributions to get more flexibility in fitting data in different areas such as medical sciences, environmental sciences, and engineering. The addition of parameter(s) has been proven useful in exploring tail properties and for improving the goodness-of-fit of the family of the proposed distributions. Methods:A new Three-Parameter Weibull-Generalized Gamma distribution which provides more flexibility in modeling lifetime data is developed using a two-component mixture of Weibull distribution (with parameters θ and λ) and Generalised Gamma distribution (with parameters α=4,θ and λ). Some of its mathematical properties such as the density function, cumulative distribution function, survival function, hazard rate function, moment generating function, Renyi entropy and order statistics are obtained. The maximum likelihood estimation method was used in estimating the parameters of the proposed distribution and a simulation study is performed to examine the performance of the maximum likelihood estimators of the parameters. Results: Real life applications of the proposed distribution to two cancer datasets are presented and its fit was compared with the fit attained by some existing lifetime distributions to show how the Three-Parameter Weibull-Generalized Gamma distribution works in practice. Conclusion: The results suggest that the proposed model performed better than its competitors and it’s a useful alternative to the existing models.
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    Cumulative Incidence Function in Competing Risks: A Case Study of Primary Biliary Cirrhosis in Liver Disease
    (Faculty of Science, Federal University Gusau, 2025-12) Rasheed Kehinde Lamidi; Bello Ishola Sanni; Saheed Kunle Ajibade; Bulus Ibrahim Doroh; Aishat Olaosebikan
    Competing risks have a potential to cause biased estimates in the context of survival analysis using both traditional tools like the Kaplan-Meier estimator and Cox proportional hazards model. This is especially applicable in Primary Biliary Cirrhosis (PBC) which is a chronic liver disease where the patients can die or undergo liver transplantation as a mutually exclusive outcome. This study used the cumulative incidence function (CIF) and Fine-Gray sub-distribution hazard model to measure competing risks in patients with PBC using data in the Mayo Clinic randomised trial of 312 patients. The likelihoods of death and liver transplantation as time progressed were estimated using CIFs which adequately considered competing events, and Fine-Gray was also used to determine the impact of D-penicillmain therapy versus placebo and the important prognostic factors. The findings revealed that, the cumulative death rates were always higher than the cumulative transplantation rates of the liver during the follow-up time. Even though the patients who were treated with D-penicillmain reported a slightly low mortality and slightly higher rate of transplantation compared to the patients provided with placebo; the difference was considered insignificant. Competing outcomes were found to be significantly predicted by age, ascites, disease stage, and platelet count and not by sex, bilirubin, and albumin. Altogether, the research proves that the competing risks approach is better in terms of its accuracy and clinical significance of assessment outcomes in PBC and the significance of CIF-based methodology in the assessment of treatment effects and prognosis in chronic liver disease.