MODEL Statement 
The MODEL statement identifies the variable to be used as the failure time variable, the optional censoring variable, and the explanatory effects, including covariates, main effects, and interactions; see the section Specification of Effects of Chapter 41, The GLM Procedure, for more information. A note of caution: specifying the effect T*A in the MODEL statement, where T is the time variable and A is a CLASS variable, does not make the effect timedependent. You must specify exactly one MODEL statement.
The MODEL statement allows one response variable. In the MODEL statement, the failure time variable precedes the equal sign. This can optionally be followed by an asterisk, the name of the censoring variable, and a list of censoring values (separated by blanks or commas if there is more than one) enclosed in parentheses. If the censoring variable takes on one of these values, the corresponding failure time is considered to be censored. The variables following the equal sign are the explanatory variables (sometimes called independent variables or covariates) for the model.
The censoring variable must be numeric. The failure time variable must contain nonnegative values. Any observation with a negative failure time is excluded from the analysis, as is any observation with a missing value for any of the variables listed in the MODEL statement. See Missing Values for details.
Table 89.5 summarizes the options available in the MODEL statement, which can be specified after a slash (/).
Option 
Description 

Specifies for the confidence limits 

Computes confidence limits for regression parameters 

Displays covariance matrix 

Specifies the denominator degrees of freedom 

Displays the Hessian matrix 

Displays the inverse of the Hessian matrix 

Computes confidence limits for the exponentials of the regression parameters 

Specifies tolerance for testing singularity 

Specifies the method of handling ties in failure times 

Specifies a variance adjustment factor 
sets the level of the confidence limits for the estimated regression parameters and the hazard ratios. The value of must be between 0 and 1, and the default is 0.05. A confidence level of produces % confidence limits. The default of ALPHA=0.05 produces 95% confidence limits.
The ALPHA= option has no effect unless the CLPARM or RISKLIMITS option is also specified.
produces confidence limits for regression parameters of Cox proportional hazards models. The confidence coefficient can be specified with the ALPHA= option. Classification main effects that use parameterizations other than REF, EFFECT, or GLM are ignored. See Confidence Intervals for details.
displays the estimated covariance matrix of the parameter estimates.
specifies the denominator degrees of freedom to be infinite. Use this option if you want to compute chisquare tests and normal confidence intervals. This option is applicable to both the Taylor series linearization and the replication methods.
computes the denominator degrees of freedom as the number of clusters (or observations if no CLUSTER statement is specified) minus the number of strata (or one if no STRATA statement is specified) minus the number of nonsingular parameters plus one in the model. This option can be useful if you are fitting a model with many parameters relative to the number of clusters minus the number of strata. See Korn and Graubard (1999, section 5.2) for further details. This option is applicable only for the Taylor series linearization method.
computes the denominator degrees of freedom for replication methods by using the total number of replicate samples. By default, PROC SURVEYPHREG computes the denominator degrees of freedom based on the number of replicate samples used. See Degrees of Freedom for details.
displays the inverse of the Hessian matrix that is evaluated at the estimated regression parameters.
produces confidence limits for hazard ratios and related quantities. See the section Hazard Ratios for details. The confidence coefficient can be specified with the ALPHA= option. Great care needs to be taken with any interpretation of the estimates and their confidence limits if interaction effects are involved in the model or if parameterizations other than REF, EFFECT, or GLM are used.
specifies the singularity criterion for determining linear dependencies in the set of explanatory variables. The default value is .
If there are no ties, both methods result in the same likelihood and yield identical estimates. The default, TIES=BRESLOW, is the most efficient method when there are no ties.
requests degrees of freedom adjustment in the computation of the matrix for the Taylor series linearization variance estimation. By default, VADJUST=DF.
excludes the degrees of freedom adjustment from the computation of the matrix for the Taylor series linearization variance estimation.
use the average sum of squares from all the usable replicate samples for the unusable replicates. This option is applicable only for the jackknife replication method. AVGREPSS multiplies the default jackknife variance estimator by the factor , where is the number of usable replicates and is the total number of replicates. See the section Variance Adjustment Factors for details.