Hi Teketo.   1. Yes, in theory you could allocate one treatment per block (district) but you would confound treatment effect with district effect more that way. Also, the same unknown, but finite, risk of a district failure to comply and/or report data has a much bigger impact on final design imbalance if you design that way. I would assume that you would have to have some extremely compelling reason to ask about this. Can you share that with us?   2.(a) I inferr from your question that you are only interested in the sample size impact of an effect difference precision of  0.13 between the control and the two experimental treatments and not between the two experimental treatments. Is that correct? (b) When you ask about a specific intraclass correlation, which class do you mean? block (district), experimental unit (clinic) or is it some class you have not yet mentioned, such as physician within (and sometimes between) clinics (and/or districts)? (c) you ask about simulating a particular coefficient of variation, but the appropriate related statistic for binomial and other distributed count data models such as this the index of dispersion. Is that what you mean? (d) When you ask about simulating the impact of a variance of  0.23, I assume you mean a constraint on the total variance of 0.23. With your design, you have to make separate assumptions about the block and treatment variance, so I guess you want to constrain both of those to add to 0.23. However you and possily your colleagues will be the ones that need to think about how to partition that into block and treatment variance, using the method given in the second paragraph of section 4 of Stroup's 2016 paper as I suggested in an earlier post.     3. There is no effective restriction about random seed size in SAS random number functions. I use 09051958 as it is an easy number to remember for me but you could use any number.    4. I do not see the value in answering this until the questions above have been clarified.   Cheers.   Damien     ... View more

** example precision estimation for a random control trial after **; ** Stroup (2016) **; ** see http://support.sas.com/resources/papers/proceedings16/11663-2016.pdf **; data rct; ** this is a SAS data step that creates a SAS data set that is called an 'exemplar' **; ** (or representative) data set in the above article to be used in conjunction with **; ** the glimmix procedure below to simulate the impacts of your assumptions **; ** on the precision (and yes, with more work, power) of your experimental design **; infile cards; input block @@; ** each block is an independent replication of the 3-treatment **; ** (control + 2 new treatments) group experimental design **; ** in your case if you make each block a randomly selected district **; ** then you get to estimate inter-district response variance for 'free' **; ** the double trailling @ in the input statement holds the observation from being output **; do eu=1 to 3; ** iterates over each of the 3 experimental units in each (district?) block **; ** each eu (1-3) is a new experimental unit, which is permuted in this example **; ** the double trailing @ holds the observation from output until the end of dataline **; input trt @@; ** current assumptions for success probabilities of the control (p1) and two **; ** treatments (p2,p3) are set here. These treatments are not as effective as those assumed **; ** in previous simulations. By varying these and the eu size you can see what effect size difference **; **(here I use 10% or 0.1 diff) can be confidently detected at a given level. Do you use 95% or 99%? **; ** I found this treatment effect size difference of 0.075 could just be detected at a 95 % C.L with a **; ** overall sample size of 1625 by changing these probabilites and eu size and re-running the exemplar analysis **; p1=.2;p2=.275;p3=.35; ** p takes on the right value given the newly input treatment type. (trt1=1) =1 if trt =1, else = 0 **; p=(trt=1)*p1+(trt=2)*p2+(trt=3)*p3; ** the ranuni(seed) function generates a uniform random number between 0 and 1 **; ** rounding 10 x this number to an integer and adding to 40 will uniformly randomly **; ** generate (and therefore facilitate simulation of the impact of) experimental unit **; ** sample sizes in the range 40 - 50. Using the same seed reproduces the same **; ** psuedo-random sequence of sample sizes every time. This is my 'lucky' number! **; ** to change the cluster size assumption change the 40 to something else. To change the size variation**; ** change the 10 to something else. To use your own 'lucky' random seed change 09051958 to your **; ** own birthday or any other easy to remember number, or leave it blank for a new seed each time **; n=40+round(10*ranuni(09051958),1); ** mu is the expected number of positive outcomes from each experimental unit**; mu=n*p; ** and the simulated experimental outcome is output to the exemplar data set **; output; ** and this is done 3 times, one for each experimental unit **; end; ** in the datalines below I have simulated 12 districts (block) chosen at random each with **; ** 3 clinics chosen at random for the trial. The treatments are allocated in all possible permutation **; ** orders, twice over the 12 blocks (districts in your case?) **; cards; 1 1 2 3 2 1 3 2 3 2 1 3 4 2 3 1 5 3 1 2 6 3 2 1 7 1 2 3 8 1 3 2 9 2 1 3 10 2 3 1 11 3 1 2 12 3 2 1 run; proc glimmix data=rct; class trt; ** this model statement form model y/n=x automatically invokes /dist=binomial link=logit **; model mu/n=trt ; random intercept trt / subject=block; ** see the reference article by Stroup 2016 on how to make educated assumptions abut the **; ** group and treatment covariances. /hold tells glimmix not to estimate but hold covariance **; ** parameters to the values given below **; parms (.08)(0.06)/hold=1,2; ** this tests for strong evidence of a difference at the precision and sample size simulated **; lsmeans trt/diff cl; run; ... View more

I think this posting is also relevant to your inquiry:   https://communities.sas.com/t5/SAS-Statistical-Procedures/Proc-Mixed/m-p/296000#U296000 ... View more

That's better, but you do realise, dont you, that you've just now twice contradicted your earlier statement about only being interested in study design precision and not power?   It seems like you are you saying that sample size control is not at all possible, once a primary health care facility has been selected? That can't be right, If that were the case, why would you asking about how to determine sample sizes?   I know from my own experience that is is near impossible to manage studies so that treatment and block group sizes come  out equal, but that should not impact on the experimental design stage, only the modelling stage. You should strive to obtain equal group sizes, and then do other things later on to deal with the group unbalance that you end up with, like eat ice cream (just joking) or use the proc glimmix model option ddfm=kr2 (not joking).   To be ethical for all stakeholders, frequent reporting on the current effective cluster sample sizes followed by timely advice to all primary healthcare participant recuiters when the quota is about to be met, so they can stop recruiting, would be best practice, right? Do y.ou plan to do this? This is not clear from your questions to date   Alternatively, do you have some idea of the different cluster sample sizes that will eventuate from the different primary health care cluster sample groups? Maybe an expected range of sizes?   If that is the case you can adapt the code example I gave to include individual sample groups drawn from, say, a uniform distribution over a range.   The code can easily be adaped to extend to more treatments than groups in a block, or more groups than treatments in each blocks, if that is what you are asking about.   Does any of this address your concerns? Do you need any more specific advice?         ... View more

It might help community members help you better if you clarified your design some more. Currently, on the information given, I have these clarifying questions n my own mind:   Does your proposed design have just 3 treatments, one of which is a control, such as a currently used treatment, and the other two are new treatments of interest? Does you proposed design have just 3 cluster samples chosen at random from a larger population, such as samples of patients from 3 primary health care centres chosen from a population of several hundred health care centres? Do you propose that the 3 treatments, including the control, are randomly assigned to the 3 clusters? Is it that simple? If that is the case, I can't see how the variations in response amongst cluster means is not confounded with the variations in response amongst the treatment means, which is a design problem that was addressed by Yates and Fisher in agriculture about 100 years ago. Surely you plan to allocate each treatment to more than one cluster sample group? As a bare minimum, should you not be considering allocating the 3 treatments to a further 3 cluster samples, making 6 groups in total? ... View more

not sure I follow the logic behind link=scan(line,2,'"'); do any or all links on those pages begin at position 2 and finish with double quotes? ... View more

see the paper suggested in this other thread: https://communities.sas.com/t5/SAS-Statistical-Procedures/Sample-size-calculation-for-proportion-repeated-measures/m-p/317603#U317603 ... View more