The frailty model has become well placed in determining underlying heterogeneity or randomness in data analyzed by proportional hazards modeling when one cannot assume underlying homogeneity of the entire sample. Even if homogeneity is assumed the frailty method is imposed to determine what, if any, possible heterogeneity might exist. This has become very popular in analysis of large multicenter clinical trials when one suspects an institution or center effect on the time to event outcome. In this paper, the time to event frailty model approach is illustrated using a longitudinal study on a cohort of individuals diagnosed with mild cognitive impairment (MCI), followed annually for conversion to dementia secondary to Alzheimer's disease (AD), conducted at the Alzheimer's Disease Research Center at the University of Alabama at Birmingham. In this study, the suspected frailty is a prognostic cluster. Our approach utilizes the Bayesian Monte Carlo Markov Chain procedure. © Nova Science Publishers, Inc.