PROC PHREG Statement
You can specify the following options in the PROC PHREG statement.
- PROC PHREG < options > ;
adds the estimated covariance matrix of the parameter estimates
to the OUTEST= data set.
The COVOUT option has no effect unless the OUTEST= option is
names the SAS data set containing the data to be analyzed. If you
omit the DATA= option, the procedure uses the most recently
created SAS data set.
requests that, for each Newton-Raphson iteration, PROC PHREG recompiles
the risk sets corresponding to the event times for the (start,stop)
style of response and recomputes the values of the time-dependent
variables defined by the programming statements for each observation
in the risk sets. If the MULTIPASS option is not specified,
PROC PHREG computes all risk sets and all the variable values
and saves them into a utility file. The MULTIPASS
option decreases required
disk space at the expense of increased execution time; however, for
data, it may actually save time since it is time
consuming to write and read large utility files.
This option has an effect only when the (start,stop) style
of response is used or when there are time-dependent explanatory
suppresses all displayed output. Note that this option
temporarily disables the Output Delivery
System (ODS); see Chapter 15, "Using the Output Delivery System," for more information.
suppresses the display of the event and censored observation
creates an output SAS data set that contains
estimates of the regression coefficients.
If you use the COVOUT option, the data set
also contains the estimated covariance
matrix of the parameter estimates.
The data set includes
- any BY variables specified
- _TIES_, a character variable of length 8 with four
possible values: BRESLOW, DISCRETE, EFRON, and EXACT.
These are the four values
of the TIES= option in the MODEL statement.
- _TYPE_, a character variable of length 8 with two possible
values: PARMS for parameter estimates or COV for covariance estimates
- _STATUS_, a character variable indicating whether the estimates have
- _NAME_, a character variable
containing the name of the TIME variable for the row of parameter estimates
and the name of each
explanatory variable to label the rows of covariance estimates
- one variable for each explanatory variable in the
MODEL statement. In a forward, backward, or stepwise regression
analysis, if an explanatory variable is not included
in the final model, the corresponding parameter estimate and
covariances are set to missing.
- _LNLIKE_, a numeric variable containing the last computed
value of the log likelihood
displays simple descriptive statistics (mean,
standard deviation, minimum, and maximum) for each explanatory
variable in the MODEL statement.
Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.