Thank you for your attention. I replaced the real variable names with 'var' for example only. So the first line is to generate a binary variable and the second is to generate a categorical variable with 6 levels. I hope that makes sense? ... View more

Thank you for your questions. It is multivariate data (combination of binary, categorical, and continuous baseline variables and time to follow-up data (# of days until event) for incidence of outcome defined both as binary and cumulative). Below are some examples of the codes I used to simulate the baseline variables (replaced the variables with "var" - I am at a loss for what to do to simulate the number of days to event - the distributions are different by event status and treatment arm. Please let me know what additional information I could provide that would be helpful %Let N = 1; %Let mu_var = 72.8; %Let sigma_var = 10.5; %Let norm_var = rand('normal',&mu_var,&sigma_var); do x=1 to &N; array a [6] (0.02 0.146 0.066 0.0033 0.02 0.745); varnew=&norm_var; If rand("uniform") < 0.666 then var = 0; else var = 1; var = rand("Table", of a[*]); I am using SAS 9.4. I am not familiar with SAS/IML or SAS/ETS, but if you think these platforms are necessary, I can inquire if my institute has access to them. Thank you. ... View more

Thank you.  I have downloaded Chapter 2 available online for free.  This is a one time task so I was hoping to avoid having to purchase any books.  Thanks for the quick reply. ... View more

@PGStats. Thank you for your help with proc surveyselect. I am wondering, in what situations is proc survey select most often used? One of the other community members responded that this procedure does not in fact 'simulate' data - I now understand what was meant by that. I also better understand that my goal is to make sure the new simulated data set cannot be recognized as the original data set (I hope that makes sense?). However, I have read many discussions on this forum that it is difficult to simulate data for a large number of variables. Thanks, MichelleR0 ... View more

I think I misunderstood the use of proc surveyselect. My goal was to create a fake data set that would have similar aggregated distributions of independent and dependent variables to use for analyses. Based on previous posts I thought I could use proc surveyselect to do that instead of a data step to make the process easier. From my understanding of the link you shared, bootstrapping is used when you want to see the possible range of estimates for a specific measure (e.g. relative risk) for the empirical data. ... View more

Using the following code: proc surveyselect data = one method = urs seed = 35 out = one_sim reps=1000 n = 300 OUTHITS; strata trt; run; I get the desired 300 unique observations per exposure group. However, how do I resample and output a data set so that my new simulated data set has the same moments, but does not have the identical observations as the original data set? ... View more

My original data has the following moments for age: N Mean Std Dev Minimum Maximum 600 75.6240017 11.7483702 36.6307132 112.3859522 I used the following SAS code to simulate data, wanting a sample size of n=600. proc surveyselect data=one method=urs reps=10000 n=600 seed=40070 out=mi;run; proc means data = mi;var age;where replicate = 2;run; The output is as follows: Selection Method Unrestricted Random Sampling Input Data Set ONE Random Number Seed 40070 Sample Size 600 Expected Number of Hits 1 Sampling Weight 1 Number of Replicates 10000 Total Sample Size 6000000 Output Data Set MI N Mean Std Dev Minimum Maximum 375 75.4457998 12.0702549 36.6307132 112.3859522 Can you explain why N=375 and not 600? Thank you. ... View more

Thank you. That worked. ... View more

Let me know if you need additional information. Thank you. proc surveyselect data = one method = urs seed = 3579 out = mi1 reps=1000 n=100; strata trt; run; ... View more

The original design was to select 100 exposed and 100 unexposed. Using method=urs, I tried using the strata option for the variable that assigns exposure status. It returns a sample with an equal amount of subjects in each group, but less than n=100. ... View more

Thank you for your reply and for providing additional details in response to my questions. I was informed this morning that at a future date, I will have access to the original data. With that, could you please provide clarity on the following: 1. Can you explain further what you meant by, 'You then simulate from a DISTRIBUTION that has those moments.'? 2. Given that I will have access to the original data, I understand that I could use the bootstrap method in Proc Surveyselect. You can confirm my understanding that I 1) simulate data (i.e. create a new data set with fake data) based on the moments from the original data set and then 2) run Proc Surveyselect with method=bootstrap on the simulated/fake data? Do I understand correctly that the original data is only used to inform simulation process? 3) If the simulated data has values that are beyond the original data (and inclusion criteria), is there a way to restrict this? For example, if the original data included patients >18 and <70 years of age with a normally distributed mean age of 55, and the simulated data included patents <18 or >70 years of age, is it appropriate to place these restrictions on the simulated data? 4) Also, my understanding is that I should have the same number of patients in the exposed and unexposed groups as the original data set. When I simulate the data and the use Proc Surveyselect to draw a sample of 200 (out of 20,000 in simulated data set), I no longer get the same number of patients in the exposed and unexposed groups. This is causing large variations in the variable distributions when stratifying by exposure group. ... View more

Thank you, PG. Per your suggestion, is there a way to keep the exposure fixed to 100 exposed and 100 unexposed? When I run proc surveyselect, it is assigning the appropriate moments to the independent variables, however, the proportion of exposed versus unexposed has changed so the moments are not the same when stratifying by exposure. ... View more