This paper is based on statistical analysis of rate of kidney renal failure taking into account that the variables of interest are sex and age group. The nature of the data used herein is secondary data, which was obtained from University of Maiduguri Teaching Hospital (UMTH) medical record for consecutive ten (10) years (1998-2007), while monthly reported cases was collected and analyzed. Our present study has been carried out in order to determine whether the effect of renal failure depends on age and sex, and to look at the prevalence of kidney (renal) failure, over the period of study. Appropriate statistical techniques have been used to test the difference of means (ttest) and contingency table (x 2-test), based on the analysis of results. The analysis has been done for significant at 5% level of significance. The empirical results are obtained from the tests of two different means which reveal that there is a significant difference in the prevalent of renal failure between male and female. Resultantly, the impact of kidney renal failure has been focused both on two parameters of age and sex. Finally, some significant suggestions based on our empirical results and observations have also been proposed for preventing kidney renal failure and future scope of present study.

Models for survival data that includes the proportion of individuals who are not subject to the event under study are known as a cure fraction models or simply called long-term survival models. The two most common models used to estimate the cure fraction are the mixture model and the non-mixture model. in this work, we present mixture and the non-mixture cure fraction models for survival data based on the beta-Weibull distribution. This four parameter distribution has been proposed as an alternative extension of the Weibull distribution in the analysis of lifetime data. This approach allows the inclusion of covariates in the models, where the estimation of the parameters was obtained under a Bayesian approach using Gibbs sampling methods.

No abstract

Log-gamma distribution is the extension of gamma distribution which is more flexible, versatile and provides a great fit to some skewed and censored data. Problem/Objective: In this paper we introduce a solution to closed forms of its survival function of the model which shows the suitability and flexibility towards modelling real life data. Methods/Analysis: Alternatively, Bayesian estimation by MCMC simulation using the Random-walk Metropolis algorithm was applied, using AIC and BIC comparison makes it the smallest and great choice for fitting the survival models and simulations by Markov Chain Monte Carlo Methods. Findings/Conclusion: It shows that this procedure and methods are better option in modelling Bayesian regression and survival/reliability analysis integrations in applied statistics, which based on the comparison criterion log-gamma model have the least values. However, the results of the censored data have been clarified with the simulation results.