I'm trying to use the ODS Output dataset ParameterEstimates from the PHREG procedure, and I'm having an issue where it appears that the variable "Parameter" only has a length of 20, so it's truncating any parameter entered into the model with length > 20. Node 126 of 127. The LIFEREG procedure focuses on parametric analysis that uses accelerated failure time models, and it can fit only a proportional hazards model that assumes a Weibull baseline hazard function. At last, we also learn SAS mixe… In the following example of SAS code that uses the above data for the PHREG procedure, Status(0) indicates to SAS that an event of interest has not occurred at that exit time, and that the subject is still at risk for the event(s) of interest at that time. The following statements print out the observations in the data set Pred1for the realization LogBUN=1.00 and HGB=10.0: proc print data=Pred1(where=(logBUN=1 and HGB=10));run; As shown in Output 89.8.2, 32 observations represent the survivor function for the realization LogBUN=1.00 and HGB=10.0. SAS Data Quality Tree level 1. Modeling with Categorical Predictors. Changbin Guo talks about how to use some new features available in the new release of SAS/STAT 14.2 to evaluate survival models for predictive accuracy using the PHREG procedure. Examples: PHREG Procedure Tree level 2. Firth’s Correction for Monotone Likelihood. Copyright © SAS Institute Inc. All rights reserved. Node 6 of 9. proc phreg data=whas500 plots=survival; class gender; model lenfol*fstat(0) = gender age;; run; The following statements use the PHREG procedure to fit the Cox proportional hazards model to these data. This example illustrates how to fit stratified Weibull models by using the STRATA statement. For example, after a bone marrow Example 87.13 and Example 87.14 illustrate Bayesian methodology, and the other examples use the classical method of maximum likelihood. This section contains 14 examples of PROC PHREG applications. PROC BPHREG is an experimental upgrade to PHREG procedure that can be used to fit Bayesian Cox proportional hazards model (SAS Institute, Inc. (2007d)). The "Examples" section includes eight additional examples of useful applications. proc phreg data = dat ; model age* outcome(0) = var_pm25 edu sex center/ rl entry=age0; array pm25 {15} pm25_1999 - pm25_2013 ; do i = 1 to 15; if (age1999+i-1)