There are probably 50 threads on how to do that, especially in the Programming forum.  Search and you will find.   SteveDenham ... View more

I am probably answering the wrong question here, but I noticed that the derivative of CELL at hour=0 is, in turn, equal to zero.  That may be leading to the missing values. Looking at the example on fitting systems of ODEs in the PROC MODEL documentation, I see that it is set up to only calculate derivatives if 'time ne 0' and uses a quadratic solution for time=0.  I think that particular approach is designed to avoid the warnings that you are getting with your code.   The other issue is what you copied from the log - it appears that the process is "stuck" at hour=0, but I can't tell.  The repetitive nature makes me think that the same error is generated at each iteration.   Given these you ,might want to reparameterize your system of equations, if at all possible.  The derivatives look unwieldy, so I suspect the partials with respect to the parameters may be even worse.   SteveDenham ... View more

What is the response variable here?  I must have missed that   Anyhow, if there is a date on every record, you can code in a flag based on the cutoff date.  I think you can ignore observation ID in the PROC FREQ analysis.  If you become interested in a more complex/complete approach, you might be able to use PROC GENMOD with a GEE model to test for the time segment equality/homogeneity.   SteveDenham ... View more

I always get curious when someone replies that something is "not working".  How is it not working?  Be specific and provide any errors, warnings or specific NOTEs from the LOG and make sure that those have some context, especially if you are executing a macro.  Also, there may be messages in the output, depending on the PROCs involved.   SteveDenham ... View more

I would look at the data generating process to see what distributions may be appropriate.  Tools for this would include graphical methods (QQ plots and histograms) and PROC TRANSREG.   SteveDenham ... View more

  "The null and alternative hypotheses for this analysis are H0: There is not a linear relationship between exposure and mortality (R=0) H1: There is a linear relationship between exposure and mortality (R≠0)"   This looks more like what you want in the way of hypothesis testing. SteveDenham ... View more

In addition to what @StatDave  said, note that your response variable is binary, and none of your PROC codes take that into account.  The REML/ML methods in MIXED are conditional on the random effects, while the GEE methods in GENMOD are marginal estimates, so I am not surprised that the results are different, and both could be biased (but by different amounts) due to the assumption of normal errors, depending on the sample size.   SteveDenham ... View more

Well, this is probably overly complicated, and you would have to do most, if not all, of it in DATA steps.  Here is my idea:   First, assign all subjects to blocks of 4 or whatever size needed to accommodate all treatments exactly once, and call that N.  This would be as simple as using a mod function.  Second, assign blocks to "super blocks" of some sizes that add to the total, assigning blocks to super blocks based on a uniform random number. Third, assign subjects to treatment group (or sequence) within super block, based on a second uniform random number. Fourth, "refill" the subjects to new blocks of size N in such a way that each block of N contains all treatment groups.  This would be the hardest part to code, as you would do something like sequentially go through the super block, selecting subjects to complete a full balanced block of four. Fifth, assign these new blocks to "final super blocks" of random sizes that are multiples of N.  This partitioning could be done with PROC SURVEYSELECT, or by creating final super blocks based on sampling with replacement possible size values (say N, 2N, 3N) such that you generate a sample of sequences of those 3 numbers.  You would then keep the sequences that summed to the number of available subjects, and select one of those valid sequences using yet another uniform random variable.   You might be able to condense all of this into fewer steps. The key is assigning subjects to treatments randomly and then assigning those subjects to slots in the various sized blocks.  You could rearrange within the various sized blocks based on a separate random value. If you work that correctly, I think you might be able drop steps 4 and 5 from the above.   SteveDenham     ... View more

Ouch.  My brain is having trouble wrapping around this question.  If you require 1:1 allocation, at least one out of the four will be determined once three in the block are allocated to treatment (note that if the first two units are randomly assigned to treatment A, then both of the last two are determined to be treatment B).     I think you might be able to approach this if you assigned all units randomly without blocking (although the 1:1 requirement still will dictate that at least the last position is predetermined), and then sequentially assign four units at a time to a "block".  Unfortunately, this accomplishes little in the way of assigning treatments in a way that additional variation can be removed as a "nuisance" source.   SteveDenham ... View more

I think using the COVB option in the MODEL will give you what you want.   SteveDenham ... View more