Thank you! ... View more

Here is a webpage on power analysis of regression models with interactions that I have found: Power Analyses for Interaction Effects in Cross-Sectional Regressions • InteractionPoweR. By the way, the analysis of power is a rather tricky aspect of regression. Research on this field has generally be confined to very simple cases. For instance, despite the popularity of comparing three and more survival curves in survival analysis, the version of SAS I am using (9.4 M5) only supports calculation of power of the test of comparing of two survival curves. So it would be better if you first searched for the method suitable for your problem on the Internet and then posted them here for questions of the implementation of this method in SAS. Also, be prepared that maybe there isn't a solution to your problem yet. ... View more

I believe that if you check the LOG you will see an explanation for the missing results in the form of a WARNING about the between imputation variance being zero. The message you are receiving can have a number of possible causes.  The first is that there is some problem with the imputation model itself (I have seen where people have left the response variable off the VAR statement in Proc MI as an example).  In general though it occurs when the missing data has no influence on the sampling error of a parameter estimate.  There is no fix or adjustment for that, but it does require some further investigation on your part.  Usually it is an indication of a problem with the imputation model with possible causes ranging from a poor imputation model to no real need to impute due to a very small fraction of missing information     Without fixing the problem, when the between imputation variance is zero then the number of Degrees of Freedom is undefined so you cannot get a confidence interval or p-values.  If you are unable to determine the cause then one suggestion would be that, if there are only minimum differences among these within-imputation variances, then look at the results for that variable for one of the imputations.   ... View more

Hello @iuri_leite ,   I initially thought you were talking about Random Survival Forests scikit  recently released. That was purely technical variation.    The claim paper you provided shows  typically how survival analysis has been done. As you see, there isn't  any special invention on the HPFOREST procedure part to extend the model from logistic approaches. As in all survival analysis, heavy duty is in data preparation. If you have analysis data ready for logistic regression, it is ready for HPfOREST.    Jia ... View more

Hello, I am not an expert in frailty models (for sure not in practice), but here's some info. The PHREG procedure in SAS implements the penalized likelihood method for the gamma and log-normal frailty models. SAS/STAT 15.3 User's Guide The PHREG Procedure The Frailty Model https://go.documentation.sas.com/doc/en/pgmsascdc/9.4_3.5/statug/statug_phreg_details16.htm Paper 1492-2014 Introduction to Frailty Models John Amrhein, McDougall Scientific Ltd. https://support.sas.com/resources/papers/proceedings14/1492-2014.pdf If you encounter recurrent disease episodes in patients, then this paper may be of interest: Analysis of Correlated Recurrent and Terminal Events Data in SAS® Li Lu(1), Chenwei Liu(2) 1 The EMMES Corporation, Rockville, MD 2 Core Genotyping Facility, Division of Cancer Epidemiology and Genetics, Advanced Technology Program, SAIC Frederick, Inc., NCI-Frederick, Frederick, MD https://www.lexjansen.com/nesug/nesug08/sa/sa16.pdf BR, Koen ... View more

Create and compare ROC curves for any predictive model By Rick Wicklin on The DO Loop November 14, 2018 https://blogs.sas.com/content/iml/2018/11/14/compare-roc-curves-sas.html   Usage Note 52973: Plot and compare ROC curves from a fitted model used to score validation or other data https://support.sas.com/kb/52/973.html   Usage Note 45339: Plot and compare ROC curves from logistic models fit to independent samples https://support.sas.com/kb/45/339.html   Koen ... View more

Dear Mkeintz, it seems to be a good approach. Regards, Iuri ... View more

Dear Dave, I had tried this before. It was your suggestion. Many thanks. Regards, Iuri ... View more

Rather than suggest a text, I suggest that you examine and try to understand an interaction plot. Here is an example (scroll down): https://documentation.sas.com/doc/en/statug/15.2/statug_glm_examples03.htm   Interactions exist when the colored lines are not parallel to each other. Note that the red line (disease 2) is highest for drugs 1 and 2 and lowest for drugs 3 and 4. That is an interaction between drugs and diseases. Had the red line not been present and drug 4 removed so only three drugs and 2 diseases, the green and blue lines are (approximately) parallel to each other, drugs 1 and 3 (approximately) do not interact with disease. The difference is the main effect of drug.   How much “non-parallel-ness” is allowed? That would be determined by the actual F-test for the interaction. ... View more

I don't see one model as being preferable over the other but rather as the two models answering different questions. If your interest is in modeling the time to the event, then use the survival model and your censored data. If the interest is in modeling the probability of the event occurring within six months, then use the logistic model and those that don't experience the event are simply nonevent observations to be used along with the event observations. ... View more

Dear Dave, many thanks. regards ... View more

Dear Dave,   many thanks. Regards,   Iuri ... View more

Dear Rob,   thanks a lot.   regards,   Iuri ... View more