Only a single MODEL statement can be used with one invocation of the LIFEREG procedure. If multiple MODEL statements are present, only the last is used. The optional label is used to label the model estimates in the output SAS data set and OUTEST= data set.
The first MODEL syntax is appropriate for right censoring. The variable response is possibly right censored. If the response variable can be right censored, then a second variable, denoted censor, must appear after the response variable with a list of parenthesized values, separated by commas or blanks, to indicate censoring. That is, if the censor variable takes on a value given in the list, the response is a rightcensored value; otherwise, it is an observed value.
The second MODEL syntax specifies two variables, lower and upper, that contain values of the endpoints of the censoring interval. If the two values are the same (and not missing), it is assumed that there is no censoring and the actual response value is observed. If the lower value is missing, then the upper value is used as a leftcensored value. If the upper value is missing, then the lower value is taken as a rightcensored value. If both values are present and the lower value is less than the upper value, it is assumed that the values specify a censoring interval. If the lower value is greater than the upper value or both values are missing, then the observation is not used in the analysis, although predicted values can still be obtained if none of the covariates are missing.
The following table summarizes the ways of specifying censoring.
lower 
upper 
Comparison 
Interpretation 


Not missing 
Not missing 
Equal 
No censoring 

Not missing 
Not missing 
Lower < upper 
Censoring interval 

Missing 
Not missing 
Upper used as left 

censoring value 

Not missing 
Missing 
Lower used as right 

censoring value 

Not missing 
Not missing 
Lower > upper 
Observation not used 

Missing 
Missing 
Observation not used 
The third MODEL syntax specifies two variables that contain count data for a binary response. The value of the first variable, events, is the number of successes. The value of the second variable, trials, is the number of tries. The values of both events and (trialsevents) must be nonnegative, and trials must be positive for the response to be valid. The values of the two variables do not need to be integers and are not modified to be integers.
The effects following the equal sign are the covariates in the model. Higherorder effects, such as interactions and nested terms, are allowed in the list, similar to the GLM procedure. Variable names and combinations of variable names representing higherorder terms are allowed to appear in this list. Classification, or CLASS, variables can be used as effects, and indicator variables are generated for the class levels. If you do not specify any covariates following the equal sign, an interceptonly model is fit.
Examples of three valid MODEL statements follow:
a: model time*flag(1,3)=temp; b: model (start, finish)=; c: model r/n=dose;
MODEL statement a
indicates that the response is contained in a variable named time
and that, if the variable flag
takes on the values 1 or 3, the observation is right censored. The explanatory variable is temp
, which could be a CLASS variable. MODEL statement b
indicates that the response is known to be in the interval between the values of the variables start
and finish
and that there are no covariates except for a default intercept term. MODEL statement c
indicates a binary response, with the variable r
containing the number of responses and the variable n
containing the number of trials.
Table 57.9 summarizes the options available in the MODEL statement.
Table 57.9: MODEL Statement Options
Option 
Description 

Model specification 

Sets the significance level 

Specifies the distribution type for failure time 

Requests no log transformation of response 

Specifies initial estimate for intercept term 

Holds the intercept term fixed 

Specifies initial estimates for regression parameters 

Specifies an offset variable 

Initializes the scale parameter 

Holds the scale parameter fixed 

Initializes the first shape parameter 

Holds the first shape parameter fixed 

Model fitting 

Sets the convergence criterion 

Sets the maximum number of iterations 

Sets the tolerance for testing singularity 

Output 

Displays the estimated correlation matrix 

Displays the estimated covariance matrix 

Displays the iteration history, final gradient, and second derivative matrix 
The following options can appear in the MODEL statement.