An Optimal Design for Inference via Modified Gamma Frailty Distribution

Abstract

Frailty models play a central role 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. All approaches accommodate right censored lifetime data and account for heterogeneity in the study population. A Modified Gamma Frailty Model is compared with two existing Frailty Models. The survival-analysis was performed using the SPSS VERSION 15. The newly derived MGF was analyzed using the Matlab software and R which 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.