The STDDEV= option in the COXREG statement is the assumed standard deviation of the predictor being tested---that is, your best guess of the true standard deviation of this predictor's sampling distribution under the same conditions as expected in the study you're planning. This isn't the same as the standard error of a coefficient estimate, just a simple property of the predictor variable itself as it would be sampled (or perhaps taking on values that you control yourself) in your planned study.   I'd also like to point out that the original question in this thread didn't specify what kind of data analysis is being planned, and it could just as easily pertain to the TWOSAMPLESURVIVAL statement in PROC POWER if you're planning a logrank test rather than a Cox proportional hazards regression. These are two scenarios in which PROC POWER supports the calculation of power for a survival analysis given alpha, hazard ratio, and number of events. ... View more

You can try using the  CONCORDANCE option on the PROC PHREG statement.  I don't know if that's enabled, necessarily, for the competing risks analysis.   ... View more

What sort of measure do you want to use for performance? Machine performance can be obtained from the log, in terms of cpu time and elapsed time. Model performance for PHREG is harder as there isn't a real good equivalent to the RMSE (smaller is better in terms of model performance). I suppose you could look at the relative sizes of the standard errors for various parameters, but that overlooks the danger of overfitting. With only two independent variables that may not be an issue, however.   SteveDenham ... View more

Hi, I review all of the four examples, try to modify them for Fine-Gray model but not successful. Can you give an example for Fine-Gray model with variable selection? Thank you! Hao   ... View more

Thank you! ... View more

Thank you!!! ... View more

It would be nice if you told us how it was "Done".   SteveDenham ... View more

@haoduonge wrote: Hi, Please have a look at page 3:  SAS makes it easy to produce point estimates from weighted datasets. All you have to do is include a WEIGHT statement in your procedure code where you specify the variable holding the weights. However, the weight statement does not affect standard errors, confidence intervals, or other measures of variability. This is where you can get into trouble. Standard errors calculated using methods appropriate to simple random samples will underestimate the standard errors of complex samples. In other words, if you take the variances or confidence intervals at face value, you will be overconfident about the estimates and less aware of the expected range in your data. You will be tempted to judge differences as statistically significant, when they are not. It appears that you did not understand what the simple random sample (SRS) included in the discussion above mentions. Nothing in that says the P-value or OR or limits aren't to be used. It says that you cannot apply the approach typical with a SRS of using a point estimate plus/minus  a test statistic times some values. The test statistic, such as a normal, is using the variance as part of that. However it is going to use a single value of variance if you use the reported summary statistics. Which will generally be smaller than that using the complex sample information. So you use the tools in the survey procedure if the data has a complex sample. Which is why the options are there to provide sample design characteristics and why some options are incompatible with some sample designs. Also the typical introductory statistics class methods assume that your sample is from an infinite population and if your sample design information is done properly it will adjust for finite populations. ... View more

This error: 37 %swdpwr(K = 162, design = ept, family = "binomial", model = "marginal",link = "log", type = "cross-sectional", meanresponse_start = 0.05, _ 180 ERROR 180-322: Statement is not valid or it is used out of proper order. 38 meanresponse_end0 = 0.049, meanresponse_end1 = 0.035, typeIerror = 0.05,alpha0 = 0.0047, alpha1 = 0.0047) 39   Means that the macro named SWDPWR has not been compiled in the current session. That means before you can use the macro call you have to have the code and submit the statements that start: %macro swdpwr ( <parameters>); and ends with %mend; ... View more

This is also extremely handy :   Create ROC curve for any predictive model  Posted 11-14-2018 05:58 AM (485 views) https://communities.sas.com/t5/SAS-Tips-from-the-Community/Create-ROC-curve-for-any-predictive-model/m-p/512899#M193   Koen   ... View more

If you specify the PLOTS(ONLY)=ROC(ID=ID) option in the PROC LOGISTIC statement and include the desired labeling variables in the ID statement, then the displayed ROC plot will label the points on the ROC curve with those variable values. ... View more

You are right. I recoded it and made error from coding. Thanks! ... View more