Let represent the distinct event times. For each , let be the number of surviving units (the size of the risk set) just prior to and let be the number of units that fail at . If the NOTRUNCATE option is specified in the FREQ statement, and can be nonintegers.
The Breslow estimate of the survivor function is
Note that the Breslow estimate is the exponentiation of the negative NelsonAalen estimate of the cumulative hazard function.
The FlemingHarrington estimate (Fleming and Harrington 1984) of the survivor function is
If the frequency values are not integers, the FlemingHarrington estimate cannot be computed.
The KaplanMeier (productlimit) estimate of the survivor function at is the cumulative product
Notice that all the estimators are defined to be right continuous; that is, the events at are included in the estimate of . The corresponding estimate of the standard error is computed using Greenwood’s formula (Kalbfleisch and Prentice 1980) as
The first quartile (or the 25th percentile) of the survival time is the time beyond which 75% of the subjects in the population under study are expected to survive. It is estimated by
If is exactly equal to 0.75 from to , the first quartile is taken to be . If it happens that is greater than 0.75 for all values of t, the first quartile cannot be estimated and is represented by a missing value in the printed output.
The general formula for estimating the 100pth percentile point is
The second quartile (the median) and the third quartile of survival times correspond to p = 0.5 and p = 0.75, respectively.
Brookmeyer and Crowley (1982) have constructed the confidence interval for the median survival time based on the confidence interval for the . The methodology is generalized to construct the confidence interval for the 100pth percentile based on a gtransformed confidence interval for (Klein and Moeschberger 1997). You can use the CONFTYPE= option to specify the gtransformation. The % confidence interval for the first quantile survival time is the set of all points t that satisfy
where is the first derivative of and is the th percentile of the standard normal distribution.
Consider the bone marrow transplant data described in Example 70.2. The following table illustrates the construction of the confidence limits for the first quartile in the ALL group. Values of that lie between = 1.965 are highlighted.
Constructing 95% Confidence Limits for the 25th Percentile 




t 


LINEAR 
LOGLOG 
LOG 
ASINSQRT 
LOGIT 
1 
0.97368 
0.025967 
8.6141 
2.37831 
9.7871 
4.44648 
2.47903 
55 
0.94737 
0.036224 
5.4486 
2.36375 
6.1098 
3.60151 
2.46635 
74 
0.92105 
0.043744 
3.9103 
2.16833 
4.3257 
2.94398 
2.25757 
86 
0.89474 
0.049784 
2.9073 
1.89961 
3.1713 
2.38164 
1.97023 
104 
0.86842 
0.054836 
2.1595 
1.59196 
2.3217 
1.87884 
1.64297 
107 
0.84211 
0.059153 
1.5571 
1.26050 
1.6490 
1.41733 
1.29331 
109 
0.81579 
0.062886 
1.0462 
0.91307 
1.0908 
0.98624 
0.93069 
110 
0.78947 
0.066135 
0.5969 
0.55415 
0.6123 
0.57846 
0.56079 
122 
0.73684 
0.071434 
–0.1842 
–0.18808 
–0.1826 
–0.18573 
–0.18728 
129 
0.71053 
0.073570 
–0.5365 
–0.56842 
–0.5222 
–0.54859 
–0.56101 
172 
0.68421 
0.075405 
–0.8725 
–0.95372 
–0.8330 
–0.90178 
–0.93247 
192 
0.65789 
0.076960 
–1.1968 
–1.34341 
–1.1201 
–1.24712 
–1.30048 
194 
0.63158 
0.078252 
–1.5133 
–1.73709 
–1.3870 
–1.58613 
–1.66406 
230 
0.60412 
0.079522 
–1.8345 
–2.14672 
–1.6432 
–1.92995 
–2.03291 
276 
0.57666 
0.080509 
–2.1531 
–2.55898 
–1.8825 
–2.26871 
–2.39408 
332 
0.54920 
0.081223 
–2.4722 
–2.97389 
–2.1070 
–2.60380 
–2.74691 
383 
0.52174 
0.081672 
–2.7948 
–3.39146 
–2.3183 
–2.93646 
–3.09068 
418 
0.49428 
0.081860 
–3.1239 
–3.81166 
–2.5177 
–3.26782 
–3.42460 
466 
0.46682 
0.081788 
–3.4624 
–4.23445 
–2.7062 
–3.59898 
–3.74781 
487 
0.43936 
0.081457 
–3.8136 
–4.65971 
–2.8844 
–3.93103 
–4.05931 
526 
0.41190 
0.080862 
–4.1812 
–5.08726 
–3.0527 
–4.26507 
–4.35795 
609 
0.38248 
0.080260 
–4.5791 
–5.52446 
–3.2091 
–4.60719 
–4.64271 
662 
0.35306 
0.079296 
–5.0059 
–5.96222 
–3.3546 
–4.95358 
–4.90900 
Consider the LINEAR transformation where . The event times that satisfy include 107, 109, 110, 122, 129, 172, 192, 194, and 230. The confidence of the interval [107, 230] is less than 95%. Brookmeyer and Crowley (1982) suggest extending the confidence interval to but not including the next event time. As such the 95% confidence interval for the first quartile based on the linear transform is [107, 276). The following table lists the confidence intervals for the various transforms.
95% CI’s for the 25th Percentile 


CONFTYPE 
[Lower 
Upper) 
LINEAR 
107 
276 
LOGLOG 
86 
230 
LOG 
107 
332 
ASINSQRT 
104 
276 
LOGIT 
104 
230 
Sometimes, the confidence limits for the quartiles cannot be estimated. For convenience of explanation, consider the linear transform . If the curve that represents the upper confidence limits for the survivor function lies above 0.75, the upper confidence limit for first quartile cannot be estimated. On the other hand, if the curve that represents the lower confidence limits for the survivor function lies above 0.75, the lower confidence limit for the quartile cannot be estimated.
The estimated mean survival time is
where is defined to be zero. When the largest observed time is censored, this sum underestimates the mean. The standard error of is estimated as
where
If the largest observed time is not an event, you can use the TIMELIM= option to specify a time limit L and estimate the mean survival time limited to the time L and its standard error by replacing k by k + 1 with .
The NelsonAalen cumulative hazard estimator, defined up to the largest observed time on study, is
and its estimated variance is
PROC LIFETEST computes the adjusted KaplanMeier estimate (AKME) of the survivor function if you specify both METHOD=KM and the WEIGHT statement. Let ( denote an independent sample of rightcensored survival data, where is the possibly rightcensored time, is the censoring indicator ( if is censored and if is an event time), and is the weight (from the WEIGHT statement). Let be the D distinct event times in the sample. At time , there are events out of subjects. The weighted number of events and the weighted number at risk are and , respectively. The AKME (Xie and Liu 2005) is
The estimated variance of is
where