The EFFECTPLOT statement below is included to visualize the effect of interest. The data are available in the SAS/STAT® Sample Library in example programs for the GENMOD procedure. Stepwise Regression Tree level 3. Consider the following data from Kalbfleisch and Prentice (1980). Best Subset Selection. Note that if the response contains any negative values, those observations are omitted by PROC PHREG. Sashelp Data Sets Tree level 1. ODS Graphics must be enabled before plots can be requested. This section contains 14 examples of PROC PHREG applications. The variable LogBUN has the largest chi-square value (8.5164), and it is significant (p=0.0035) at the SLENTRY=0.25 level. Both linear and quadratic effects of AGE are included in the model and the BaseDeficit spline is allowed to interact with both AGE effects. The SCORE statement produces predicted values for these two points. Again, the amount(s) of change in the continuous variable can be specified using the UNITS= option. The EFFECTPLOT statement below produces a plot of the predicted response against x4 with x3 fixed at its mean. Following are the coefficients produced by the HAZARDRATIO statement. ALPHA= number specifies the alpha level of the interval estimates for the hazard ratios. PROC PHREG is a semi-parametric procedure that fits the Cox proportional hazards model (SAS Institute, Inc. (2007c)). rights reserved. Release is the software release in which the problem is planned to be The results from PROC ORTHOREG include tables (from the E option, not shown) that verify that the coefficients from PHREG were properly used and tables of estimates. The following statements use PROC PHREG to produce a stepwise regression analyis. But when using modeling procedures other than LOGISTIC or PHREG, such as when using the GLM, GENMOD, GLIMMIX, or other procedure to model a count or continuous response, nothing like the ODDSRATIO or HAZARDRATIO statement is available as an alternative to the ESTIMATE and CONTRAST statements for assessing a continuous variable involved in interactions. Node 5 of 7. Node 127 of 127 . The fitted model is also saved by the STORE statement in an item store named RegMod. The estimated effect of increasing BaseDeficit by one unit at -10 when AGE=10 is about 0.009. Here is an example, where the datastep after PHREG do the integration: data mydata; do i=1 to 10000; predictor=mod(i,2); time=rand('gamma',5*exp(log(2)*predictor)); censurtime=rand('gamma',10); event=(time<=censurtime); … The variables thought to be related to survival are LogBUN (log(BUN) at diagnosis), HGB (hemoglobin at diagnosis), Platelet (platelets at diagnosis: 0=abnormal, 1=normal), Age (age at diagnosis, in years), LogWBC (log(WBC) at diagnosis), Frac (fractures at diagnosis: 0=none, 1=present), LogPBM (log percentage of plasma cells in bone marrow), Protein (proteinuria at diagnosis), and SCalc (serum calcium at diagnosis). The results from the ESTIMATE and EFFECTPLOT statements are shown below. The ESTIMATE statement results show that the effect of increasing x4 by one unit with x3 at its mean is 61.8. You can elect to output the predicted survival curves in a SAS data set by optionally specifying the OUT= option in the BASELINE statement. data test; set dat; array pm25 {15} pm25_1999 - pm25_2013 ; do i = 1 to 15; if (age1999+i-1)5 then t=5+rand('exponential',1/(baseline*rateratio2**(covariate1=1)));; entry=0; event=1; output; end; end; drop i; run; proc phreg data=simulation nosummary; class covariate1/param=glm ; model (entry t)*event(0)=covariate1; run; proc phreg … Modeling with Categorical Predictors. So, it is equivalent to this MODEL statement: It is of interest to assess the effect on the response of increasing x4 by one unit when x3 is set at its mean. Statements using those coefficients are shown below variable with the at option p=0.0035 at! 14 examples of PROC PHREG data CHK and predicted values for any nonmissing values in the BASELINE...., like in the SAS/ETS® Sample Library in example programs for the COUNTREG procedure explanatory variables is first into! 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