STRATA Statement 
In the preceding syntax, variable is a variable whose values determine the stratum levels, and list is a list of endpoints for a numeric variable. The values for variable can be formatted or unformatted. If variable is a character variable, or if variable is numeric and no list appears, then the strata are defined by the unique values of the STRATA variable. More than one variable can be specified in the STRATA statement, and each numeric variable can be followed by a list. Each interval contains its lower endpoint but not its upper endpoint. The corresponding strata are formed by the combination of levels. If a variable is numeric and is followed by a list, then the levels for that variable correspond to the intervals defined by the list. The initial interval is assumed to start at , and the final interval is assumed to end at .
The specification of a STRATA variable can have any of the following forms:
A list separated by blanks 
Age(5 10 20 30) 

A list separated by commas 
Age(5,10,20,30) 

x to y 
Age(5 to 10) 

x to y by z 
Age(5 to 30 by 10) 

A combination of the above 
Age(5,10 to 50 by 10) 
For example, the specification
strata Age(5,20 to 50 by 10) Sex;
indicates the following levels for the Age variable:
This statement also specifies that the Age strata be further subdivided by values of the variable Sex. In this example, there are six age groups by two sex groups, forming a total of 12 strata.
The specification of several STRATA variables, such as
strata A B C;
is equivalent to the A*B*C syntax of the TABLES statement in the FREQ procedure. The number of strata levels usually grows very rapidly with the number of STRATA variables, so you must be cautious when specifying the list of STRATA variables.
When comparing more than two survival curves, a ksample test tells you whether the curves are significantly different from each other, but it does not identify which pairs of curves are different. A multiplecomparison adjustment of the pvalues for the paired comparisons retains the same overall false positives as the ksample test. Two types of paired comparisons can be made: comparisons between all pairs of curves and comparisons between a control curve and all other curves. You use the DIFF= option to specify the comparison type, and you use the ADJUST= option to select a method of multiplecomparison adjustments.
Table 51.3 summaries the options available in the STRATA statement.
Option 
Description 

Homogeneity Tests 

Specifies the group variable for stratified tests 

Suppresses printing the test statistic and covariance matrix 

Suppresses any tests 

Specifies tests corresponding to various weight functions 

Requests a trend test 

Multiple Comparisons 

Requests a multiplecomparison adjustment 

Specifies the type of differences to consider 

Missing Strata Value 

Allows missing values as valid stratum values 
You can specify options in the STRATA statement after a slash ("/"). The following list describes these options.
performs Dunnett’s twotailed comparisons of the control group with all other groups. PROC LIFETEST uses the factoranalytic covariance approximation described in Hsu (1992) and identifies the adjustment in the results as "DunnettHsu." Note that ADJUST=DUNNETT is incompatible with DIFF=ALL.
performs the paired comparisons based on the studentized maximum modulus test.
performs the paired comparisons based on Tukey’s studentized range test. PROC LIFETEST uses the approximation described in Kramer (1956) and identifies the adjustment as "TukeyKramer" in the results. Note that ADJUST=TUKEY is incompatible with DIFF=CONTROL.
computes the adjusted pvalues from the simulated distribution of the maximum or maximum absolute value of a multivariate normal random vector. The simulation estimates , the true th quantile, where is the value of the ALPHA= simulateoption.
The number of samples for the SIMULATE adjustment is set so that the tail area for the simulated is within a certain accuracy radius of with an accuracy confidence of %. In equation form,
where is the simulated and is the true distribution function of the maximum; see Edwards and Berry (1987) for details. By default, = 0.005 and = 0.01 so that the tail area of is within 0.005 of 0.95 with 99% confidence.
The simulateoptions include the following:
specifies the target accuracy radius of a % confidence interval for the true probability content of the estimated th quantile. The default value is ACC=0.005.
specifies the value for estimating the th quantile. The default value is the ALPHA= value in the PROC LIFETEST statement, or 0.05 if that option is not specified.
specifies the value for a % confidence interval for the true probability content of the estimated th quantile. The default value for the accuracy confidence is 99%, corresponding to EPS=0.01.
specifies the sample size for the simulation. By default, n is set based on the values of the target accuracy radius and accuracy confidence % for an interval for the true probability content of the estimated th quantile. With the default values for , , and (0.005, 0.01, and 0.05, respectively), NSAMP=12604 by default.
specifies that a report on the simulation should be displayed, including a listing of the parameters, such as , , and , in addition to an analysis of various methods for estimating or approximating the quantile.
specifies an integer used to start the pseudorandom number generator for the simulation. If you do not specify a seed, or if you specify a value less than or equal to zero, the seed is generated by default from reading the time of day from the computer’s clock.
requests all paired comparisons
requests comparisons of the control curve with all other curves. To specify the control curve, you specify the quotes strings of formatted values that represent the curve in parentheses. For example, if Cell=’large’ identifies the control group, you specify
DIFF=CONTROL('large')
If more than one variable is used to identify the curves (for example, if Cell=’large’ and Sex=’F’ represent the control), you specify
DIFF=CONTROL('large' 'F')
The order of the quoted strings should correspond to the order of the stratum variables. If no specific curve is specified as the control, the first stratum or group value is used.
specifies the variable whose formatted values identify the various samples whose underlying survival curves are to be compared. The tests are stratified on the levels of the STRATA variables. For example, in a multicenter trial in which two forms of therapy are to be compared, you specify the variable that identifies therapies as the GROUP= variable and the variable that identifies centers as the STRATA variable, in order to perform a stratified test to compare the therapies while controlling the effect of the centers.
allows missing values to be a stratum level or a valid value of the GROUP= variable.
suppresses the display of the rank statistics and the corresponding covariance matrices for various strata. If the TREND option is specified, the display of the scores for computing the trend tests is suppressed.
suppresses the ksample tests, stratified tests, and trend tests.
computes the trend tests for testing the null hypothesis that the population hazards rate are the same versus an ordered alternatives. If there is only one STRATA variable and the variable is numeric, the unformatted values of the variable are used as the scores; otherwise, the scores are in the given order of the strata.
controls the tests produced. Each test corresponds to a different weight function (see the section Nonparametric Tests for the weight functions). The testrequests include the following:
specifies all the nonparametric tests with =1 and =0 for the Fleming and Harrington test—FLEMING(1,0).
specifies the family of tests in Harrington and Fleming (1982), where and are nonnegative numbers. FLEMING(,) reduces to the FlemingHarrington family (Fleming and Harrington; 1981) when =0, which you can specify as FLEMING() with one argument. When =0, the test becomes the logrank test. When =1, the test should be very close to the PetoPeto test.
suppresses all comparison tests. Specifying TEST=NONE is equivalent to specify NOTEST.
specifies the likelihood ratio test based on the exponential model.
specifies the PetoPeto test. The test is also referred to as the PetoPetoPrentice test.
specifies the Wilcoxon test. The test is also referred to as the Gehan test or the Breslow test.
By default, TEST=(LOGRANK WILCOXON LR) for the ksample tests, and TEST=(LOGRANK WILCOXON) for stratified and trend tests.