A NEW APPROACH FOR ANALYSING SURVIVAL MODELS: THE MODIFIED GAMMA FRAILTY DISTRIBUTION

Sikiru Adeyinka Abdulazeez

Department Of Mathematics, Statistics and Computer Science

 Kaduna Polytechnic, Kaduna.

E-mail: ysabdul94@yahoo.com,

Abstract: Survival analysis examines and models the time it takes for events to occur. The prototypical such event is death, from which the name ‘survival analysis’ and much of its terminology derives, but the ambit of application of survival analysis is much broader.Frailty models is effective in formulating the effects of covariates on potentially censored failure times and in the joint modelling of incomplete repeated measures and failure times in longitudinal studies. Survival data are often subject to right censoring and to a subsequent loss of information about the effect of explanatory variables. Three frailty models are used to analyze bivariate time-to-event data. Each approach accommodates right censored lifetime data and account for heterogeneity in the study population. A Modified Gamma Frailty [MGF] Model is compared with two existing Frailty Models. The newly derived MGF is more robust when sample size is more than forty.The MGF model performs better than the existing models in the presence of clustering. However the CGF is preferable in the absence of clusters in a given data set.