The OUTPUT statement creates a new SAS data set containing statistics
calculated for each observation. These can include the estimated
() and its standard error,
survival distribution estimates, residuals, and influence statistics.
In addition, this data set includes the time variable, the
explanatory variables listed in the MODEL statement, the censoring
variable (if specified), and the BY, STRATA, FREQ, and ID variables
- OUTPUT <OUT= SAS-data-set >
< keyword=name ... keyword=name > <
/options > ;
For observations with missing values in the time variable or any
explanatory variables, the output statistics are set to missing.
However, for observations with missing values only in the
censoring variable or the FREQ variable, survival estimates are
still computed. Therefore, by adding observations with missing
values in the FREQ variable or the censoring variable, you can
compute the survivor function estimates for new observations or for
settings of explanatory variables not present in the data without
affecting the model fit.
No OUTPUT data set is created if the model contains a time-dependent
variable defined by means of programming statements. Survival
distribution estimates are set to missing for the counting process
style of input.
The following list explains specifications in the OUTPUT statement.
names the output data set. If you omit the OUT= option, the
OUTPUT data set is created and given a default name using the
specifies the statistics included in the OUTPUT data set and
names the new variables that contain the statistics. Specify a
keyword for each desired statistic (see the following list of
keywords), an equal sign, and either a variable or a list of
variables to contain the statistic. The keywords that accept a
list of variables
are DFBETA, RESSCH, RESSCO, and WTRESSCH. For these keywords,
you can specify as many names in name as the number of
specified in the MODEL statement. If you specify k names
and k is less than
the total number of explanatory variables, only the changes for
the first k parameter estimates are output.
The keywords and the corresponding statistics are as follows:
- approximate changes in the
parameter estimates when the jth observation is omitted. These variables
are a weighted transform of the score residual variables and
are useful in assessing local influence and in computing robust
- approximate likelihood displacement
when the observation is left out. This diagnostic can be
used to assess
the impact of each observation on the overall fit of the model.
- relative influence of observations on the overall fit of the
This diagnostic is useful in assessing the
sensitivity of the fit of the model to each observation.
- log of the negative log of SURVIVAL
- log of SURVIVAL
- number of subjects at risk at the observation time (or at the right endpoint of the at risk interval when a
counting process MODEL specification is used)
- deviance residual . This
is a transform of the martingale residual to achieve
a more symmetric distribution.
- martingale residual . The residual at the observation time can be interpreted as the difference
over in the observed number of events
minus the expected number of events
given by the model.
- Schoenfeld residuals.
These residuals are useful in assessing the proportional
- score residuals.
These residuals are a decomposition of the first partial derivative
of the log likelihood. They can be used to assess the leverage
exerted by each subject in the parameter estimation. They are
also useful in constructing robust sandwich variance estimators.
of the estimated linear predictor,
- survivor function estimate
, where is the observation time
- weighted Schoenfeld residuals.
These residuals are useful in investigating the nature of
nonproportionality if the proportional hazard assumption does not hold.
- estimate of the linear predictor,
The following options can appear in the OUTPUT statement after
a slash (/).
specifies the order of the observations in the OUTPUT
data set. Available values for sort_order are
- requests that the output observations
the same as the input data set.
- requests that the output observations be sorted by strata
and descending order of the time variable within each stratum.
The default is ORDER=DATA.
specifies the method used to compute the survivor function
estimates. The two available methods are
- CH | EMP
- specifies that the empirical cumulative hazard function
estimate of the survivor function is to be computed; that
is, the survivor function is estimated by exponentiating
the negative empirical cumulative hazard function.
- specifies that the product-limit estimate of the survivor
function is to be computed. The default is METHOD=PL.
Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.