You can use the LIFETEST procedure to compute nonparametric estimates of the survivor functions, to compare survival curves, and to compute rank tests for association of the failure time variable with covariates.
For simple analyses, only the PROC LIFETEST and TIME statements are required. Consider a sample of survival data. Suppose that the time variable is T and the censoring variable is C with value 1 indicating censored observations. The following statements compute the productlimit estimate for the sample:
proc lifetest; time t*c(1); run;
You can use the STRATA statement to divide the data into various strata. A separate survivor function is then estimated for each stratum, and tests of the homogeneity of strata are performed. However, if the GROUP= option is also specified in the STRATA statement, the GROUP= variable is used to identify the samples whose survivor functions are to be compared, and the STRATA variables are used to define the strata for the stratified tests. You can specify covariates (prognostic variables) in the TEST statement, and PROC LIFETEST computes linear rank statistics to test the effects of these covariates on survival.
For example, consider the results of a small randomized trial on rats. Suppose you randomize 40 rats that have been exposed to a carcinogen into two treatment groups (Drug X and Placebo). The event of interest is death from cancer induced by the carcinogen. The response is the time from randomization to death. Four rats died of other causes; their survival times are regarded as censored observations. Interest lies in whether the survival distributions differ between the two treatments.
The following DATA step creates the data set Exposed, which contains four variables: Days (survival time in days from treatment to death), Status (censoring indicator variable: 0 if censored and 1 if not censored), Treatment (treatment indicator), and Sex (gender: F if female and M if male).
proc format; value Rx 1='Drug X' 0='Placebo'; data exposed; input Days Status Treatment Sex $ @@; format Treatment Rx.; datalines; 179 1 1 F 378 0 1 M 256 1 1 F 355 1 1 M 262 1 1 M 319 1 1 M 256 1 1 F 256 1 1 M 255 1 1 M 171 1 1 F 224 0 1 F 325 1 1 M 225 1 1 F 325 1 1 M 287 1 1 M 217 1 1 F 319 1 1 M 255 1 1 F 264 1 1 M 256 1 1 F 237 0 0 F 291 1 0 M 156 1 0 F 323 1 0 M 270 1 0 M 253 1 0 M 257 1 0 M 206 1 0 F 242 1 0 M 206 1 0 F 157 1 0 F 237 1 0 M 249 1 0 M 211 1 0 F 180 1 0 F 229 1 0 F 226 1 0 F 234 1 0 F 268 0 0 M 209 1 0 F ;
PROC LIFETEST is invoked as follows to compute the productlimit estimate of the survivor function for each treatment and to compare the survivor functions between the two treatments:
ods graphics on; proc lifetest data=Exposed plots=(survival(atrisk) logsurv); time Days*Status(0); strata Treatment; run; ods graphics off;
In the TIME statement, the survival time variable, Days, is crossed with the censoring variable, Status, with the value 0 indicating censoring. That is, the values of Days are considered censored if the corresponding values of Status are 0; otherwise, they are considered as event times. In the STRATA statement, the variable Treatment is specified, which indicates that the data are to be divided into strata based on the values of Treatment. ODS Graphics must be enabled before producing graphs. Two plots are requested through the PLOTS= option—a plot of the survival curves with at risk numbers and a plot of the negative log of the survival curves.
The results of the analysis are displayed in the following figures.
Figure 51.1 displays the productlimit survival estimate for the Drug X group (Treatment=1). The figure lists, for each observed time, the survival estimate, failure rate, standard error of the estimate, cumulative number of failures, and number of subjects remaining in the study.
ProductLimit Survival Estimates  

Days  Survival  Failure  Survival Standard Error 
Number Failed 
Number Left 

0.000  1.0000  0  0  0  20  
171.000  0.9500  0.0500  0.0487  1  19  
179.000  0.9000  0.1000  0.0671  2  18  
217.000  0.8500  0.1500  0.0798  3  17  
224.000  *  .  .  .  3  16 
225.000  0.7969  0.2031  0.0908  4  15  
255.000  .  .  .  5  14  
255.000  0.6906  0.3094  0.1053  6  13  
256.000  .  .  .  7  12  
256.000  .  .  .  8  11  
256.000  .  .  .  9  10  
256.000  0.4781  0.5219  0.1146  10  9  
262.000  0.4250  0.5750  0.1135  11  8  
264.000  0.3719  0.6281  0.1111  12  7  
287.000  0.3187  0.6813  0.1071  13  6  
319.000  .  .  .  14  5  
319.000  0.2125  0.7875  0.0942  15  4  
325.000  .  .  .  16  3  
325.000  0.1062  0.8938  0.0710  17  2  
355.000  0.0531  0.9469  0.0517  18  1  
378.000  *  0.0531  .  .  18  0 
Note:  The marked survival times are censored observations. 
Figure 51.2 displays summary statistics of survival times for the Drug X group. It contains estimates of the 25th, 50th, and 75th percentiles and the corresponding 95% confidence limits. The median survival time for rats in this treatment is 256 days. The mean and standard error are also displayed; however, these values are underestimated because the largest observed time is censored and the estimation is restricted to the largest event time.
Quartile Estimates  

Percent  Point Estimate 
95% Confidence Interval  
Transform  [Lower  Upper)  
75  319.000  LOGLOG  256.000  355.000 
50  256.000  LOGLOG  255.000  319.000 
25  255.000  LOGLOG  171.000  256.000 
Mean  Standard Error 

271.131  11.877 
Note:  The mean survival time and its standard error were underestimated because the largest observation was censored and the estimation was restricted to the largest event time. 
Figure 51.3 and Figure 51.4 display the survival estimates and the summary statistics of the survival times for Placebo (Treatment=0). The median survival time for rats in this treatment is 235 days.
ProductLimit Survival Estimates  

Days  Survival  Failure  Survival Standard Error 
Number Failed 
Number Left 

0.000  1.0000  0  0  0  20  
156.000  0.9500  0.0500  0.0487  1  19  
157.000  0.9000  0.1000  0.0671  2  18  
180.000  0.8500  0.1500  0.0798  3  17  
206.000  .  .  .  4  16  
206.000  0.7500  0.2500  0.0968  5  15  
209.000  0.7000  0.3000  0.1025  6  14  
211.000  0.6500  0.3500  0.1067  7  13  
226.000  0.6000  0.4000  0.1095  8  12  
229.000  0.5500  0.4500  0.1112  9  11  
234.000  0.5000  0.5000  0.1118  10  10  
237.000  0.4500  0.5500  0.1112  11  9  
237.000  *  .  .  .  11  8 
242.000  0.3938  0.6063  0.1106  12  7  
249.000  0.3375  0.6625  0.1082  13  6  
253.000  0.2813  0.7188  0.1038  14  5  
257.000  0.2250  0.7750  0.0971  15  4  
268.000  *  .  .  .  15  3 
270.000  0.1500  0.8500  0.0891  16  2  
291.000  0.0750  0.9250  0.0693  17  1  
323.000  0  1.0000  .  18  0 
Note:  The marked survival times are censored observations. 
Quartile Estimates  

Percent  Point Estimate 
95% Confidence Interval  
Transform  [Lower  Upper)  
75  257.000  LOGLOG  237.000  323.000 
50  235.500  LOGLOG  206.000  253.000 
25  207.500  LOGLOG  156.000  229.000 
Mean  Standard Error 

235.156  10.211 
A summary of the number of censored and event observations is shown in Figure 51.5. The figure lists, for each stratum, the number of event and censored observations, and the percentage of censored observations.
Summary of the Number of Censored and Uncensored Values  

Stratum  Treatment  Total  Failed  Censored  Percent Censored 
1  Drug X  20  18  2  10.00 
2  Placebo  20  18  2  10.00 
Total  40  36  4  10.00 
Figure 51.6 displays the graph of the productlimit survivor function estimates versus survival time. The two treatments differ primarily at larger survival times. Note the number of subjects at risk in the plot. You can display the number of subjects at risk at specific time points by using the ATRISK= option.
Figure 51.7 displays the graph of the log survivor function estimates versus survival time. Neither curve approximates a straight line through the origin—the exponential model is not appropriate for the survival data.
Note that these graphical displays are generated through ODS. For general information about ODS Graphics, see Chapter 21, Statistical Graphics Using ODS.
Results of the comparison of survival curves between the two treatments are shown in Figure 51.8. The rank tests for homogeneity indicate a significant difference between the treatments (p=0.0175 for the logrank test and p=0.0249 for the Wilcoxon test). Rats treated with Drug X live significantly longer than those treated with Placebo. Since the survival curves for the two treatments differ primarily at longer survival times, the Wilcoxon test, which places more weight on shorter survival times, becomes less significant than the logrank test. As noted earlier, the exponential model is not appropriate for the given survival data; consequently, the result of the likelihood ratio test should be ignored.
Test of Equality over Strata  

Test  ChiSquare  DF  Pr > ChiSquare 
LogRank  5.6485  1  0.0175 
Wilcoxon  5.0312  1  0.0249 
2Log(LR)  0.1983  1  0.6561 
Next, suppose male rats and female rats are thought to have different survival rates, and you want to assess the treatment effect while adjusting for the gender differences. By specifying the variable Sex in the STRATA statement as a stratifying variable and by specifying the variable Treatment in the GROUP= option, you can carry out a stratified test to test Treatment while adjusting for Sex. The test statistics are computed by pooling over the strata defined by the values of Sex, thus controlling for the effect of Sex. The NOTABLE option is added to the PROC LIFETEST statement as follows to avoid estimating a survival curve for each gender:
proc lifetest data=Exposed notable; time Days*Status(0); strata Sex / group=Treatment; run;
Results of the stratified tests are shown in Figure 51.9. The treatment effect is statistically significant for both the logrank test (p=0.0071) and the Wilcoxon test (p=0.0150). As compared to the results of the unstratified tests in Figure 51.8, the significance of the treatment effect has been sharpened by controlling for the effect of the gender of the subjects.
Stratified Test of Equality over Group  

Test  ChiSquare  DF  Pr > ChiSquare 
LogRank  7.2466  1  0.0071 
Wilcoxon  5.9179  1  0.0150 
Since Treatment is a binary variable, another way to study the effect of Treatment is to carry out a censored linear rank test with Treatment as an independent variable. This test is less popular than the twosample test; nevertheless, in situations where the independent variables are continuous and are difficult to discretize, it might be infeasible to perform a ksample test. To compute the censored linear rank statistics to test the Treatment effect, Treatment is specified in the TEST statement as follows:
proc lifetest data=Exposed notable; time Days*Status(0); test Treatment; run;
Results of the linear rank tests are shown Figure 51.10. The pvalues are very similar to those of the twosample tests in Figure 51.8.
Univariate ChiSquares for the Wilcoxon Test  

Variable  Test Statistic 
Standard Error 
ChiSquare  Pr > ChiSquare 
Treatment  3.9525  1.7524  5.0875  0.0241 
Univariate ChiSquares for the LogRank Test  

Variable  Test Statistic 
Standard Error 
ChiSquare  Pr > ChiSquare 
Treatment  6.2708  2.6793  5.4779  0.0193 
With Sex as a prognostic factor that you want to control, you can compute a stratified linear rank statistic to test the effect of Treatment by specifying Sex in the STRATA statement and Treatment in the TEST statement as in the following program. The TEST=NONE option is specified in the STRATA statement to suppress the twosample tests for Sex.
proc lifetest data=Exposed notable; time Days*Status(0); strata Sex / test=none; test Treatment; run;
Results of the stratified linear rank tests are shown in Figure 51.11. The pvalues are very similar to those of the stratified tests in Figure 51.9.
Univariate ChiSquares for the Wilcoxon Test  

Variable  Test Statistic 
Standard Error 
ChiSquare  Pr > ChiSquare 
Treatment  4.2372  1.7371  5.9503  0.0147 
Univariate ChiSquares for the LogRank Test  

Variable  Test Statistic 
Standard Error 
ChiSquare  Pr > ChiSquare 
Treatment  6.8021  2.5419  7.1609  0.0075 