The SURVEYPHREG Procedure
PROC SURVEYPHREG Statement
PROC SURVEYPHREG <options>;
The PROC SURVEYPHREG statement invokes the SURVEYPHREG procedure. It also identifies the data set to be analyzed. Table 1 summarizes the options available in the PROC SURVEYPHREG statement.
Table 1: PROC SURVEYPHREG Statement Options
| Option | Description |
|---|---|
| ATRISK | Displays a table that contains the sum of weights for the number of units and the sum of weights for the corresponding number of events in the risk sets |
| DATA= | Names the input SAS data set |
| MISSING | Treats missing values as a valid category |
| NAMELEN= | Specifies the length of effect names |
| NOMCAR | Uses missing observations specified as not missing completely at random |
| NOPRINT | Suppresses all displayed output |
| ORDER= | Specifies the sort order of CLASS variables |
| RATE= | Specifies the sampling rate |
| TOTAL= | Specifies the total number of primary sampling units |
| VARMETHOD= | Specifies the variance estimation method |
You can specify the following options in the PROC SURVEYPHREG statement:
- ATRISK
-
displays a table that contains the sum of weights for the number of units at risk at each distinct event time and the sum of weights for the corresponding number of events in the risk sets. For example, the risk set information in Figure 3 is displayed if the ATRISK option is specified in the example in the section Getting Started: SURVEYPHREG Procedure.
Figure 3: Risk Set Information
The SURVEYPHREG ProcedureRisk Set Sum of Weights lenBorrow At Risk Event 1 11616.79 5440.11 2 6176.68 1177.71 3 4998.97 926.55 4 4072.42 1411.07 5 2661.35 461.89 6 2199.46 565.01 7 1634.45 236.58 8 1397.87 230.3
- DATA=SAS-data-set
names the SAS data set that contains the data to be analyzed. If you omit the DATA= option, the procedure uses the most recently created SAS data set.
- MISSING
treats missing values as a valid (nonmissing) category for all categorical variables, which include CLASS, STRATA, CLUSTER, and DOMAIN variables. By default, if you do not specify the MISSING option, an observation is excluded from the analysis if it has a missing value for any of these categorical variables. For more information, see the section Missing Values.
- NAMELEN=n
specifies the length of effect names in tables and output data sets to be n characters, where n is a value between 20 and 200, inclusive. By default, NAMELEN=20.
- NOMCAR
-
includes observations with missing values of the analysis variables that are specified in the MODEL statement as not missing completely at random (NOMCAR) for Taylor series variance estimation. When you specify the NOMCAR option, PROC SURVEYPHREG computes variance estimates by analyzing the nonmissing values as a domain (subpopulation), where the entire population includes both nonmissing and missing domains. See the section Missing Values for details.
By default, PROC SURVEYPHREG excludes an observation from analyses (and the corresponding variance computations) if that observation has a missing value for any of the variables in the MODEL statement. Note that if you specify the MISSING option for classification variables, then the procedure treats the missing values as a valid nonmissing level.
The NOMCAR option applies only to Taylor series variance estimation. Other replication methods do not use the NOMCAR option.
- NOPRINT
suppresses all displayed output. Note that this option temporarily disables the Output Delivery System (ODS); see Chapter 23, Using the Output Delivery System, for more information.
- ORDER=DATA | FORMATTED | FREQ | INTERNAL
-
specifies the sort order for the levels of the classification variables (which are specified in the CLASS statement).
This option applies to the levels for all classification variables, except when you use the (default) ORDER=FORMATTED option with numeric classification variables that have no explicit format. In that case, the levels of such variables are ordered by their internal value.
The ORDER= option can take the following values:
Value of ORDER= Levels Sorted By DATA Order of appearance in the input data set FORMATTED External formatted value, except for numeric variables with no explicit format, which are sorted by their unformatted (internal) value FREQ Descending frequency count; levels with the most observations come first in the order INTERNAL Unformatted value By default, ORDER=FORMATTED. For ORDER=FORMATTED and ORDER=INTERNAL, the sort order is machine-dependent.
For more information about sort order, see the chapter on the SORT procedure in the Base SAS Procedures Guide and the discussion of BY-group processing in the "Grouping Data" section of SAS Programmers Guide: Essentials.
-
RATE=value | SAS-data-set
R=value | SAS-data-set -
specifies the sampling rate, which PROC SURVEYPHREG uses to compute a finite population correction for Taylor series or bootstrap variance estimation. This option is ignored for BRR and jackknife variance estimation.
If your sample design has multiple stages, you should specify the first-stage sampling rate, which is the ratio of the number of primary sampling units (PSUs) that are selected to the total number of PSUs in the population.
You can specify the sampling rate in either of the following ways:
- value
specifies a nonnegative number to use for a nonstratified design or for a stratified design that has the same sampling rate in each stratum.
- SAS-data-set
specifies a SAS-data-set that contains the stratification variables and the sampling rates for a stratified design that has different sampling rates in the strata. You must provide the sampling rates in the data set variable named
_RATE_.
The sampling rates must be nonnegative numbers. You can specify value as a number between 0 and 1. Or you can specify value in percentage form as a number between 1 and 100, and PROC SURVEYPHREG converts that number to a proportion. The procedure treats the value 1 as 100% instead of 1%.
For more information, see the section Population Totals and Sampling Rates.
If you do not specify the RATE= or TOTAL= option, then the Taylor series or bootstrap variance estimation does not include a finite population correction. You cannot specify both the TOTAL= option and the RATE= option in the same PROC SURVEYPHREG statement.
-
TOTAL=value | SAS-data-set
N=value | SAS-data-set -
specifies the total number of primary sampling units (PSUs) in the population. PROC SURVEYPHREG uses the value to compute a finite population correction for Taylor series or bootstrap variance estimation. This option is ignored for BRR and jackknife variance estimation.
You can specify the total number of PSUs in either of the following ways:
- value
specifies a positive number to use for a nonstratified design or for a stratified design that has the same population total in each stratum.
- SAS-data-set
specifies a SAS-data-set that contains the stratification variables and the population totals for a stratified design that has different population totals in the strata. You must provide the stratum totals in the data set variable named
_TOTAL_.
The stratum totals must be positive numbers.
For more information, see the section Population Totals and Sampling Rates.
If you do not specify the TOTAL= or RATE= option, then the Taylor series or bootstrap variance estimation does not include a finite population correction. You cannot specify both the TOTAL= option and the RATE= option in the same PROC SURVEYPHREG statement.
- VARMETHOD=method <(method-options)>
-
specifies the variance estimation method. PROC SURVEYPHREG provides the Taylor series method and balanced repeated replication (BRR), jackknife, and bootstrap replication (resampling) methods.
Table 2 summarizes the available methods and method-options.
Table 2: Variance Estimation Options
method Variance Estimation Method method-options BOOTSTRAP Bootstrap CENTER=FULLSAMPLE | REPLICATES DETAILS MH=number | SAS-data-set OUTWEIGHTS=SAS-data-set REPS=number SEED=number BRR Balanced repeated replication CENTER=FULLSAMPLE | REPLICATES DETAILS FAY <=value> HADAMARD=SAS-data-set OUTWEIGHTS=SAS-data-set PRINTH REPS=number JACKKNIFE Jackknife CENTER=FULLSAMPLE | REPLICATES DETAILS OUTJKCOEFS=SAS-data-set OUTWEIGHTS=SAS-data-set TAYLOR Taylor series linearization None
By default, VARMETHOD=JACKKNIFE if you also specify a REPWEIGHTS statement; otherwise, VARMETHOD=TAYLOR by default.
You can specify the following methods:
- BOOTSTRAP < (method-options) >
-
requests variance estimation by the bootstrap method. The bootstrap method requires at least two primary sampling units (PSUs) in each stratum for stratified designs unless you provide replicate weights by using a REPWEIGHTS statement. For more information, see the section Bootstrap Method.
You can specify the following method-options:
- CENTER=FULLSAMPLE | REPLICATES
-
defines how to compute the deviations for the bootstrap method. You can specify the following values:
- FULLSAMPLE
computes the deviations of the replicate estimates from the full sample estimate.
- REPLICATES
computes the deviations of the replicate estimates from the average of the replicate estimates.
For more information, see the section Bootstrap Method. By default, CENTER=FULLSAMPLE.
- DETAILS
displays the maximum likelihood estimates of model parameters for replicate samples when the replicate parameter estimates are available. A replicate sample might not provide useful parameter estimates (replicate estimates) for reasons such as nonconvergence of the optimization or inestimability of some parameters in that replicate sample.
- MH=value | (values) | SAS-data-set
-
specifies the number of PSUs to select for the bootstrap replicate samples. You can provide bootstrap stratum sample sizes
by specifying a list of values or a SAS-data-set. Alternatively, you can provide a single bootstrap sample size value to use for all strata or for a nonstratified design. For more information, see the section Bootstrap Method. Each bootstrap sample size
must be a positive integer and must be less than 
, which is the total number of PSUs in stratum h. By default, = 
for a stratified design. For a nonstratified design, the bootstrap sample size value must be less than n (the total number of PSUs in the sample). By default, m = n – 1 for a nonstratified design. You can provide the bootstrap sample size by specifying one of the following forms:
- OUTWEIGHTS=SAS-data-set
-
names a SAS-data-set in which to store the replicate weights that PROC SURVEYPHREG creates for bootstrap variance estimation. For information about replicate weights, see the section Bootstrap Method. For information about the contents of the OUTWEIGHTS= data set, see the section Replicate Weights Output Data Set.
This method-option is not available when you provide replicate weights in a REPWEIGHTS statement.
- REPS=number
specifies the number of replicates for bootstrap variance estimation, where number must be an integer greater than 1. Increasing the number of replicates improves the estimation precision but also increases the computation time. By default, REPS=250.
- SEED=number
-
specifies the initial seed for random number generation, where number must be a positive integer.
If you do not specify this option or if you specify a number that is negative or 0, PROC SURVEYPHREG uses the time of day from the computer’s clock to obtain an initial seed.
The seed that is used is displayed in the "Variance Estimation" table.
To reproduce the same bootstrap replicate weights and the same analysis in a subsequent execution of PROC SURVEYPHREG, you can specify the same initial seed that was used in the original analysis.
- BRR < (method-options) >
-
requests variance estimation by balanced repeated replication (BRR). The BRR method requires a stratified sample design with two primary sampling units (PSUs) in each stratum. If you specify the VARMETHOD=BRR option, you must also specify a STRATA statement unless you provide replicate weights with a REPWEIGHTS statement. See the section Balanced Repeated Replication (BRR) Method for details.
You can specify the following method-options in parentheses after the VARMETHOD=BRR option:
- CENTER=FULLSAMPLE | REPLICATES
defines how to compute the deviations for the BRR method. CENTER=FULLSAMPLE is the default, which computes the deviations of the replicate estimates from the full sample estimate. Alternatively, you can specify CENTER=REPLICATES to compute the deviations of the replicate estimates from the average of the replicate estimates. See the section Balanced Repeated Replication (BRR) Method for details.
- DETAILS
displays the maximum likelihood estimates of model parameters for replicate samples when the replicate parameter estimates are available. A replicate sample might not provide useful parameter estimates (replicate estimates), for reasons such as nonconvergence of the optimization or inestimability of some parameters in that replicate sample.
- FAY <=value>
-
requests Fay’s method, which is a modification of the BRR method. See the section Fay’s BRR Method for details.
You can specify the value of the Fay coefficient, which is used in converting the original sampling weights to replicate weights. The Fay coefficient must be a nonnegative number less than 1. By default, the value of the Fay coefficient equals 0.5.
-
HADAMARD=SAS-data-set
H=SAS-data-set -
names a SAS data set that contains the Hadamard matrix for BRR replicate construction. If you do not provide a Hadamard matrix with the HADAMARD= method-option, PROC SURVEYPHREG generates an appropriate Hadamard matrix for replicate construction. See the sections Balanced Repeated Replication (BRR) Method and Hadamard Matrix for details.
If a Hadamard matrix of a given dimension exists, it is not necessarily unique. Therefore, if you want to use a specific Hadamard matrix, you must provide the matrix as a SAS data set in the HADAMARD= method-option.
In the HADAMARD= input data set, each variable corresponds to a column of the Hadamard matrix, and each observation corresponds to a row of the matrix. You can use any variable names in the HADAMARD= data set. All values in the data set must equal either 1 or –1. You must ensure that the matrix you provide is indeed a Hadamard matrix—that is,
, where 
is the Hadamard matrix of dimension R and 
is an identity matrix. PROC SURVEYPHREG does not check the validity of the Hadamard matrix that you provide. The HADAMARD= input data set must contain at least H variables, where H denotes the number of first-stage strata in your design. If the data set contains more than H variables, PROC SURVEYPHREG uses only the first H variables. Similarly, the HADAMARD= input data set must contain at least H observations.
If you do not specify the REPS= method-option, then the number of replicates is equal to the number of observations in the HADAMARD= input data set. If you specify the number of replicates—for example, REPS=nreps—then the first nreps observations in the HADAMARD= data set are used to construct the replicates.
You can specify the PRINTH method-option to display the Hadamard matrix that PROC SURVEYPHREG uses to construct replicates for BRR variance estimation.
- OUTWEIGHTS=SAS-data-set
-
names an output SAS data set to store the replicate weights that PROC SURVEYPHREG creates for BRR variance estimation. For more information about replicate weights, see the section Balanced Repeated Replication (BRR) Method. For more information about the contents of the OUTWEIGHTS= data set, see the section Replicate Weights Output Data Set.
The OUTWEIGHTS= method-option is not available when you provide replicate weights by using a REPWEIGHTS statement.
- PRINTH
-
displays the Hadamard matrix that is used to construct replicates for BRR variance estimation. When you provide the Hadamard matrix in the HADAMARD= method-option, PROC SURVEYPHREG displays only the rows and columns that are actually used to construct replicates. For more information, see the sections Balanced Repeated Replication (BRR) Method and Hadamard Matrix.
The PRINTH method-option is not available when you provide replicate weights by using a REPWEIGHTS statement, because PROC SURVEYPHREG does not use a Hadamard matrix in this case.
- REPS=number
-
specifies the number of replicates for BRR variance estimation. The value of number must be an integer greater than 1.
If you do not provide a Hadamard matrix by using the HADAMARD= method-option, the number of replicates should be greater than the number of strata and should be a multiple of 4. For more information, see the section Balanced Repeated Replication (BRR) Method. If a Hadamard matrix cannot be constructed for the REPS= value that you specify, the value is increased until a Hadamard matrix of that dimension can be constructed. Therefore, it is possible for the actual number of replicates to be larger than the REPS= value that you specify.
If you provide a Hadamard matrix by using the HADAMARD= method-option, the value of REPS= must not be greater than the number of rows in the Hadamard matrix. If you provide a Hadamard matrix and do not specify the REPS= method-option, the number of replicates equals the number of rows in the Hadamard matrix.
If you do not specify the REPS= or HADAMARD= method-option and do not include a REPWEIGHTS statement, the number of replicates equals the smallest multiple of 4 that is greater than the number of strata.
If you provide replicate weights with a REPWEIGHTS statement, the procedure does not use the REPS= method-option. With a REPWEIGHTS statement, the number of replicates equals the number of REPWEIGHTS variables.
- JACKKNIFE | JK <(method-options)>
-
requests variance estimation by the delete-1 jackknife method. See the section Jackknife Method for details. If you provide replicate weights with a REPWEIGHTS statement, VARMETHOD=JACKKNIFE is the default variance estimation method. The JACKKNIFE method requires at least two primary sampling units (PSUs) in each stratum for stratified designs unless you provide replicate weights with a REPWEIGHTS statement.
You can specify the following method-options in parentheses following VARMETHOD=JACKKNIFE:
- TAYLOR
requests Taylor series variance estimation. This is the default method if you do not specify the VARMETHOD= option or a REPWEIGHTS statement. See the section Taylor Series Linearization for more information.