Browsing by Author "Rasheed Kehinde Lamidi"

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    A New Generalized Gamma-Weibull Distribution with Applications to Time-to-event Data
    (2023-11-18) Kazeem Adesina Dauda; Rasheed Kehinde Lamidi; Adeshola Adediran Dauda; Waheed Babatunde Yahya
    In this research, a new class of probability distributions referred to as Generalized Gamma Weibull (GGW) distributions was introduced within the context of parametric survival analysis. This distribution represents a modification of the gamma Weibull distribution and offers valuable insights, particularly when dealing with highly skewed lifetime data. The study extensively examined the mathematical characteristics of these distributions, encompassing hazard functions, moments, quantile functions, and order statistics. Furthermore, the research delved into parameter estimation methods for these newly proposed distributions, employing the maximum likelihood technique, Fisher Information (FI), and deriving asymptotic confidence intervals for both censored and uncensored scenarios. To illustrate the practical utility of these proposed distributions, the study applied them to analyze two sets of real-life survival data and two sets of real-life data, resulting in a total of four distinct datasets. To gauge the effectiveness of the GGW distributions in comparison to existing methods such as Generalized Weibull and Generalized gamma (G-Weibull and G-Gamma) distributions, the research employed statistical indices including the Akaike Information Criterion (AIC), Corrected Akaike Information Criterion (CAIC), and Bayesian Information Criterion (BIC). The outcomes of this comparative analysis demonstrated the superior performance of the newly introduced GGW distributions (AIC=338.6313, BIC=346.2794, and CAIC=339.5202) when contrasted with the existing methods (G-Weibull: AIC=376.1946, BIC=381.9307, and CAIC=376.5424) across all three criteria, thereby highlighting the enhanced suitability of GGW distributions for modeling and analyzing skewed lifetime data.
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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.