The variability of survival data is split into a part that depends on risk factors, and is therefore theoretically predictable, and a part that is initially unpredictable, even when all relevant information is known. The notion of frailty provides a convenient way to introduce random effects, association, and unobserved heterogeneity into models for survival data. Frailty models have a great deal of potential in accounting for correlation arising in clustered survival data. The chapter presents a simple sample size formula for log-rank statistics applied to clustered survival data with variable cluster sizes and arbitrary treatment assignments within clusters. Thus the analysis of survival data requires techniques that are able to incorporate the possibility of skewed and censored observations. Normally, in most clinical applications, survival analysis implicitly assumes a homogenous population to be studied. Time-to-event or survival studies involve observing units until an event is experienced.