Introduction: Lifetime distribution has drawn so much attention in recent research, and this has lead to the development of new lifetime distribution. Addition of parameters to the existing distribution makes the distribution more flexible and reliable and applicable model has become the focus of the recent search. This paper proposed a two-parameter Pranav distribution which has its base from a one-parameter Pranav and Ishita distribution. Methods Two parameter Pranav distribution was proposed. Mathematical and statistical properties of the distribution which includes; moments, coefficient of variation, skewness, kurtosis, index of dispersion, hazard rate function, mean residual life function, stochastic ordering, mean deviation, Bonferroni and Lorenz curves were developed. Other lifetime distributions such as Ishita, Akash, Sujatha, Shanker, Lindley, and Exponential distributions were considered in the study. Results: This new distribution was compared with two-parameter Akash, Lindley, one parameter Pranav, Ishita, Akash, Sujatha, Shanker, Lindley, and Exponential distributions to determine the efficiency of the new model. The estimation of parameters has been X-rayed using the method of moments and maximum likelihood. Also, AIC and BIC were used to test for the goodness of fit of the model which was applied to a real-life data of hypertensive patients. The results show that the new two-parameter Pranav distribution has the lowest value of AIC and BIC Conclusion: Based on the AIC and BIC values we can conclude that the two-parameter Pranav is more efficient than the other distribution for modeling survival of hypertensive patients. Hence two-parameter Pranav can be seen as an important distribution in modeling lifetime data.
This paper developed an alternative statistical method for the analysis of time or space ordered data. The proposed method used the ranks of successive differences between the observations adjust for the order, direction and magnitudes of the successive differences. Chi-squared test statistic based on the ranks of the successively ordered data is developed to test for the possible existence of any statistical differences between the scores by subjects in the time or space ordered population. Some sample data were used to illustrate the proposed method. The result of the analysis shows that the present method is as powerful as some of the existing nonparametric methods that can be used for the same purpose at least for the present data but preferably for use if the available data set is ordered in time or space.
Often in controlled comparative either prospective, retrospective or cross–sectional study involving matched samples of subjects or patients, the response of a subject or patient to a predisposing factor in a retrospective study or to a condition or treatment in a prospective study may be dichotomous. But there may also be several other possible response options other than dichotomous but numbering some c possible mutually exclusive response options or categories. Specifically , suppose we have a total random sample of n- matched pairs of patients or subjects matched on certain characteristics to be exposed to two treatments, drugs, tests; suppose nil pairs of these subject pairs are in the response category by case and response category by the control subject, for responses. There may be no replications; that is there is only one pair of case-control subjects.
Introduction: In Nigeria, hypertension is a common sickness among grownups. This research was carried out to determine the best model for predicting survival of hypertensive patients using goodness of fit criteria, Standard Error (SE), Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Method: A total of 105 patients who were diagnosed with hypertension from January 2013 to July 2018 were considered in which death is the event of interest. Six parametric models such as; exponential, Weibull, Lognormal, Log-logistic, Gompertz and hypertabastic distribution were fitted to the data using goodness of fit such as S.E, AIC and BIC to determine the best model. The parametric models were considered because they are all lifetime distributions. Results The result shows that the hypertabastic distribution has the lowest AIC and BIC, followed by Gompertz distribution. The standard error also indicates the hypertabastic model is better because it has the least value of standard error. This indicates that in terms of relative efficiency and parameterization the hypertabastic model is the best. The Survival Probability Plot of the six parametric models shows that the Hypertabastic distribution best fitted the data because it shows a clear step function than the other distribution and this justifies the result SE, AIC and BIC presented. Conclusion: Since hypertabastic distribution has the lowest SE, AIC and BIC it indicates that it is the best parametric model for predicting survival of hypertensive patients in chukwuemeka Odumegwu Ojukwu university teaching hospital Awka, Nigeria.
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