Proc phreg output options
The OUTPUT statement creates a new SAS data set containing statistics calculated for each observation. These can include the estimated linear predictor 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 if specified.
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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. These variables are a weighted transform of the score residual variables and are useful in assessing local influence and in computing robust variance estimates. This diagnostic can be used to assess the impact of each observation on the overall fit of the model.
This diagnostic is useful in assessing the sensitivity of the fit of the model to each observation. This is a transform of the martingale residual to achieve a more symmetric distribution. 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.
These residuals are useful in assessing the proportional hazards assumption. 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. These residuals are useful in investigating the nature of nonproportionality if the proportional hazard assumption does not hold.
Logistic Regression for Rare Events | Statistical Horizons
Previous Page Next Page. 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 explanatory variables 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: DFBETA specifies the approximate changes in the parameter estimates when the th observation is omitted.
PROC PHREG: OUTPUT Statement :: SAS/STAT(R) User's Guide, Second Edition
LD specifies the approximate likelihood displacement when the observation is left out. LMAX specifies the relative influence of observations on the overall fit of the model. LOGLOGS specifies the log of the negative log of SURVIVAL.
LOGSURV specifies the log of SURVIVAL. RESDEV specifies the deviance residual. RESMART specifies the martingale residual. RESSCH specifies the Schoenfeld residuals.
RESSCO specifies the score residuals. STDXBETA specifies the standard error of the estimated linear predictor ,. SURVIVAL specifies the survivor function estimate , where is the observation time.
WTRESSCH specifies the weighted Schoenfeld residuals. XBETA specifies the estimate of the linear predictor,. The two available methods are as follows: BRESLOW CH EMP specifies that the empirical cumulative hazard function estimate of the survivor function be computed; that is, the survivor function is estimated by exponentiating the negative empirical cumulative hazard function. PL specifies that the product-limit estimate of the survivor function be computed.
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