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 proportional hazards regression models to interval-censored data. You can select a piecewise constant function as the baseline hazard function, or you can model the cumulative baseline hazard function by cubic splines.
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.