PROC SURVEYPHREG Statement 
The PROC SURVEYPHREG statement invokes the procedure and identifies the data set to be analyzed. You can specify the following options in the PROC SURVEYPHREG statement:
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.
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.
suppresses all displayed output. Note that this option temporarily disables the Output Delivery System (ODS); see Chapter 20, Using the Output Delivery System, for more information.
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. The replication methods, which you request with the VARMETHOD=BRR and VARMETHOD=JACKKNIFE options, do not use the NOMCAR option.
specifies the order in which to sort 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. With this option, 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 machinedependent. For more information about sorting order, see the chapter on the SORT procedure in the Base SAS Procedures Guide and the discussion of BYgroup processing in SAS Language Reference: Concepts.
specifies the sampling rate as a nonnegative value, or identifies an input data set that gives the stratum sampling rates in a variable named _RATE_. PROC SURVEYPHREG uses this information to compute a finite population correction for Taylor series variance estimation. The procedure does not use the RATE= option for BRR or jackknife variance estimation, which you request with the VARMETHOD=BRR or VARMETHOD=JACKKNIFE option.
If your sample design has multiple stages, you should specify the firststage 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.
For a nonstratified sample design, or for a stratified sample design with the same sampling rate in all strata, you should specify a nonnegative value for the RATE= option. If your design is stratified with different sampling rates in different strata, then you should name a SAS data set that contains the stratification variables and the stratum sampling rates. See the section Population Totals and Sampling Rates for details.
The sampling rate value must be a nonnegative number. 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%.
If you do not specify the RATE= or TOTAL= option, then the Taylor series 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.
specifies the total number of primary sampling units (PSUs) in the study population as a positive value, or identifies an input data set that gives the stratum population totals in a variable named _TOTAL_. PROC SURVEYPHREG uses this information to compute a finite population correction for Taylor series variance estimation. The procedure does not use the TOTAL= option for BRR or jackknife variance estimation, which you request with the VARMETHOD=BRR or VARMETHOD=JACKKNIFE option.
For a nonstratified sample design, or for a stratified sample design with the same population total in all strata, you should specify a positive value for the TOTAL= option, which refers to the total number of PSUs in each stratum. If your sample design is stratified with different population totals in different strata, then you should name a SAS data set that contains the stratification variables and the stratum totals. See the section Population Totals and Sampling Rates for details.
If you do not specify the TOTAL= or RATE= option, then the Taylor series 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.
specifies the variance estimation method. VARMETHOD=TAYLOR requests the Taylor series method, which is the default if you do not specify the VARMETHOD= option or a REPWEIGHTS statement. VARMETHOD=BRR requests variance estimation by balanced repeated replication (BRR), and VARMETHOD=JACKKNIFE requests variance estimation by the delete1 jackknife method.
For VARMETHOD=BRR and VARMETHOD=JACKKNIFE, you can specify methodoptions in parentheses following the variance method name. Table 89.1 summarizes the available methodoptions.
VARMETHOD= 
Variance Estimation Method 
MethodOptions 

BRR 
Balanced repeated replication 

JACKKNIFE 
Jackknife 

TAYLOR 
Taylor series linearization 
None 
The following values are available for the VARMETHOD= option:
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 methodoptions in parentheses after the VARMETHOD=BRR option:
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.
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= methodoption, 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= methodoption.
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 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 variables, where denotes the number of firststage strata in your design. If the data set contains more than variables, PROC SURVEYPHREG uses only the first variables. Similarly, the HADAMARD= input data set must contain at least observations.
If you do not specify the REPS= methodoption, then the number of replicates is taken to be 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 methodoption to display the Hadamard matrix that the procedure uses to construct replicates for BRR.
names an output SAS data set to store the replicate weights that PROC SURVEYPHREG creates for BRR variance estimation. See the section Balanced Repeated Replication (BRR) Method for information about replicate weights. See the section Replicate Weights Output Data Set for details about the contents of the OUTWEIGHTS= data set.
The OUTWEIGHTS= methodoption is not available when you provide replicate weights with a REPWEIGHTS statement.
displays the Hadamard matrix used to construct replicates for BRR. When you provide the Hadamard matrix in the HADAMARD= methodoption, PROC SURVEYPHREG displays only the rows and columns that are actually used to construct replicates. See the sections Balanced Repeated Replication (BRR) Method and Hadamard Matrix for more information.
The PRINTH methodoption is not available when you provide replicate weights with a REPWEIGHTS statement because the procedure does not use a Hadamard matrix in this case.
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 with the HADAMARD= methodoption, the number of replicates should be greater than the number of strata and should be a multiple of 4. See the section Balanced Repeated Replication (BRR) Method for more information. 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 used to be larger than the REPS= value that you specify.
If you provide a Hadamard matrix with the HADAMARD= methodoption, the value of REPS= must not be less than the number of rows in the Hadamard matrix. If you provide a Hadamard matrix and do not specify the REPS= methodoption, the number of replicates equals the number of rows in the Hadamard matrix.
If you do not specify the REPS= or HADAMARD= methodoption 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= methodoption. With a REPWEIGHTS statement, the number of replicates equals the number of REPWEIGHTS variables.
requests variance estimation by the delete1 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.
You can specify the following methodoptions in parentheses following VARMETHOD=JACKKNIFE:
names an output SAS data set that contains replicate weights. See the section Jackknife Method for information about replicate weights. See the section Replicate Weights Output Data Set for more details about the contents of the OUTWEIGHTS= data set.
The OUTWEIGHTS= methodoption is not available when you provide replicate weights with the REPWEIGHTS statement.
names an output SAS data set that contains jackknife coefficients. See the section Jackknife Coefficients Output Data Set for more details about the contents of the OUTJKCOEFS= data set.
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.