THE HOMOGENETIC ESTIMATE FOR THE VARIANCE OF SURVIVAL RATE

The homogenetic estimate for the variance of survival rate is proposed based on generalization and reduction between the complement of the e mpirical distribution function and the Kaplan-Meier or Berkson-Gage es timate. It reduces to the binomial variance estimate when there is no censoring. A Monte Carlo simulation study was carried out under variou s sample sizes, survival and censoring configurations, number of tied observations, and confidence levels with 2000 replications. It verifie s that the commonly employed Greenwood estimate underestimates, and th e Simon and Lee expression for the Peto estimate strictly overestimate s, the variance of survival rate to an extent dependent on the censori ng distributions. The conclusions are identical with those of Peto et al. (1977) and Slud et al. (1984). The bias of the homogenetic estimat e is less than that of both the Greenwood estimate and the Simon and L ee expression for the Peto estimate. The homogenetic estimate slightly overestimates when there are no ties and becomes unbiased and then sl ightly underestimates as the number of tied observations increases.