The PHREG Procedure

This section uses the two-sample vaginal cancer mortality data from Kalbfleisch and Prentice (1980, p. 2) in two examples to illustrate some of the basic features of PROC PHREG. The first example carries out a classical Cox regression analysis and the second example performs a Bayesian analysis of the Cox model.

Two groups of rats received different pretreatment regimes and then were exposed to a carcinogen. Investigators recorded the survival times of the rats from exposure to mortality from vaginal cancer. Four rats died of other causes, so their survival times are censored. Interest lies in whether the survival curves differ between the two groups.

The following DATA step creates the data set Rats, which contains the variable Days (the survival time in days), the variable Status (the censoring indicator variable: 0 if censored and 1 if not censored), and the variable Group (the pretreatment group indicator).

data Rats;
   label Days  ='Days from Exposure to Death';
   input Days Status Group @@;
   datalines;
143 1 0   164 1 0   188 1 0   188 1 0
190 1 0   192 1 0   206 1 0   209 1 0
213 1 0   216 1 0   220 1 0   227 1 0
230 1 0   234 1 0   246 1 0   265 1 0
304 1 0   216 0 0   244 0 0   142 1 1
156 1 1   163 1 1   198 1 1   205 1 1
232 1 1   232 1 1   233 1 1   233 1 1
233 1 1   233 1 1   239 1 1   240 1 1
261 1 1   280 1 1   280 1 1   296 1 1
296 1 1   323 1 1   204 0 1   344 0 1
;

By using ODS Graphics, PROC PHREG allows you to plot the survival curve for Group=0 and the survival curve for Group=1, but first you must save these two covariate values in a SAS data set as in the following DATA step:

data Regimes;
   Group=0;
   output;
   Group=1;
   output;
run;