Survival Analysis Procedures

The following SAS/STAT procedures are specifically designed for analyzing survival data:


computes nonparametric estimates of survivor functions for interval-censored data. You can use this procedure to compare the underlying survival distributions of two or more samples of interval-censored data.


fits parametric models to failure time data that can be left-censored, right-censored, or interval-censored. The log of the survival time is modeled as a linear effect of covariates and a random disturbance term, the distribution of which includes the Weibull, log-normal, and log-logistic distributions.


computes the Kaplan-Meier estimate of a survivor function and provides the log-rank test to compare the underlying hazards of two or more samples of right-censored data. You can also use this procedure to study the association between the failure time and a number of concomitant variables.


fits the Cox proportional hazards model and its extensions, which include the multiplicative intensity model, the shared frailty model, and the Fine-Gray model for competing-risks data.


performs quantile regression for survival data by modeling the quantiles of the lifetime variable as a function of the covariates. Because lifetime distributions are usually more skewed, the quantiles of the lifetime are more informative than the mean for summarizing the lifetime distribution.


is a Cox modeling procedure similar to PROC PHREG, appropriate for analyzing data that are collected from a survey sample.

The SEVERITY procedure in SAS/ETS software is also a survival analysis procedure.