@StatDave could give you a more definitive answer, but here is what I am thinking: First off, I see var1 repeated in both the class and model statements. I am guessing this is a typo, and the second instance should be var2. Next, you have two continuous covariates - is there any possibility that these are partially confounded with the levels of time or of the tertiles for HEI? That could lead to a Simpson's paradox situation. Additionally, you might have an issue with your Change variable - it is only defined at time=2, so there really isn't a reason to include time in the model. My final comment would be about changing from wide to long. I often have trouble using PROC TRANSPOSE to do wide to long, and so have usually resorted to a DATA step approach. In this case though, I don't see a need to change to long format, as you are not modeling raw BP values over time, but rather ChangeBP with the various covariates listed. If the independent variables have different values over time, I have a different concern - how do you associate those with the change from baseline? Do the values at baseline have the same effect as the values at first follow-up? Should you be fitting two separate variables - for example, var1_base and var1_follow, var2_base and var2_follow, etc.?   SteveDenham       ... View more

There are some PROCs that will enable you to fit a truncated normal distribution. I will leave the searching through the documentation to find some examples to you. Some starting places would be PROC QLIM in SAS/ETS and PROC FMM (and HPFMM) in SAS/STAT.   SteveDenham ... View more

Lack of convergence means that the g'H{inverse}g/abs(f) is not less than the tolerance set, which seems to be 1e-4 in this case. There are a lot of issues that can cause this - model misspecification relative to the data at hand, getting stuck in a local extremum (minimum or maximum) or even running up against a default number of iterations. I can't really tell from that particular output table, so a bit more info would help - the code for the PROC you are using and the LOG file, if there are any ERRORs, WARNINGs or NOTEs that relate to this.   SteveDenham ... View more

How was it resolved? Kind of went from an interesting problem to "nothing to see here."   SteveDenham ... View more

I have read through all of the answers and it occurs to me - why do you want to remove extreme values? Unless you can identify a data quality issue, extreme values in animal growth experiments identify critical subjects, whose history should be investigated. For growth, it may be a food or water availability issue, for example. In any case, removing so-called outliers is going to increase your ability to detect differences, as the new dataset will almost certainly have a smaller mean squared error.  But at what cost? Would you lose the ability to find an important effect of your treatment, such as increasing animal to animal variability?   Do the outliers identify a particular subpopulation with a different distribution? I would have a difficult time accepting a conclusion of increased growth rate, as an example, if you removed extreme low values from a treated group or extreme high values from a control. Since publications do not ordinarily include raw data, cases of exclusion like these could not be identified, and perhaps a conclusion of effectiveness will not be repeatable.     And all of this also applies to high-leverage points in a regression analysis.   So, if you exclude outliers, you should, in my opinion, disclose which points were excluded and why it was excluded.   SteveDenham ... View more

First question, is the effect of any of your repeated predictors linked solely to the current response (this will have an easy response), or does the predictor at a previous timepoint affect the response as well (this gets into timeseries analysis)?   If it is the first case, then get your data in "long" format with each record having an entry for the subject_id, the timepoint, the response variable value at that timepoint, and each predictor variable value at that timepoint.   From there, questions like "Is this predictor continuous or categorical?" and "Is there a possibility of predictors interacting that could affect my interpretation?" and "What do I do if all my predictors are continuous, but have very different ranges of values?" become important.   In any case any of the linear modeling PROCs are capable of handling data of this sort.   SteveDenham ... View more

Save your code and data. then restart SAS, or disconnect and reconnect SAS Studio. At some point, it looks like you have an unmatched single or double quote.   From what you posted, it also looks to me like you called a macro named logistic_aic_sbc_score. The unbalanced quote may be in that macro..   SteveDenham ... View more

Question (actually a trick question) - how do you know that the distribution for residuals is not normal? Did you do some sort of test? There are well-known issues with almost every hypothesis test for normality (overpowered with N greater than about 40, underpowered for N less than about 15), and the linear mixed model is remarkably robust to the assumption of normality of the residuals, so long as the empirical distribution is mono-modal, not truncated, and lacks extremely large absolute values. The mono-modal basically boils down to sex differences.   So here are some ways to attack the issue, from simple to complex: bin your responses to four or five categories and consider using Cochran-Mantel-Haenszel methods where you stratify by sex. Plot your data and see what the shape looks like. From that, use a generalized linear model, assuming the distribution you have a picture of. If you have what might be considered random effects, use a generalized linear mixed model. Bootstrap your data. Simulate a lot of datasets that could possibly occur based on your current data. Use a Bayesian analysis with noninformative priors. This does a lot better job of simulating the data needed to construct credible intervals as you can include correlations over time or clusters. I don't think you have any random effects, so a good start on this can be found by looking through the documentation for PROC BGLIMM. Given what you have done so far, I would recommend #4. You can use most of your PROC MIXED code, and you can examine each distribution/link to see which best fits your data.   SteveDenham ... View more

Two part answer here. First a reply to @Ksharp : After fitting a model, the residuals may or may not be normal (Gaussian). For example, if you fit binomial data without accounting for the distribution with a link function, the residuals will not look Gaussian (it might take a lot of data). Second a reply to @SAS-questioner : If you only have 6 data points, why are you bothering to fit a model? The mixed model or GEE model parameters will have such large standard errors you probably won't be able to correctly infer from them.    SteveDenham ... View more

Just be sure that you use the monotonically decreasing part of these curves to estimate the half-life if you implement the method in @Rick_SAS 's blog. Including the increasing phase will lead to a best fit line that has a much, much smaller slope than the part of the curve dominated by elimination from the pool sampled. As a result, the half life will be greatly over-estimated.   SteveDenham    PS I am working through the paper by Kurada & Chen to use compartment modeling on this data. Here is a link: https://support.sas.com/resources/papers/proceedings18/1883-2018.pdf . Then on to  the paper by Hannion and Boulanger https://www.lexjansen.com/phuse/2020/as/PRE_AS06.pdf . From these, all of the data can be used, not just the monotonically decreasing data. ... View more

So I started to work on this with NLMIXED when I noticed something critical - these data do not describe a single pool decaying process of the type  c = c0 * exp (-k * time). It appears that at time=0 the response value is at or near zero, then increases to about time = 43, followed by a decrease. There are two logical models that fit this - a time independent two-pool model with sampling from the second pool, so that there is an entry rate constant, or a time-dependent model with sampling from a single pool.     Neither of those models fit the linearized (log transform) approach you have. One thing that might help would be to estimate the removal coefficient and estimate t1/2 from that. That would be easiest by removing time points less than 43 from the analysis and rescaling the time axis. However, that estimate of t1/2 will be biased by not having an accurate initial estimate of the maximum concentration and when it occurs.  However, in many pharmacokinetic studies, this is the standard approach, and it gives a good estimate of the removal coefficient and the resulting half-life in the pool.  Here is code to accomplish that:   proc nlmixed data=have2(where=(atptn>40)) maxiter=5000 ; parms c0 = 1.5790 d1 = 0 k0 = -0.5291 d2 = 0 s2 = 0.2069 ; pred = c0*trt1*exp(k0*trt1*(ATPTN-43)) + (c0 + d1)*trt2*exp((k0 + d2)*trt2*(ATPTN-43)); model aval ~ normal(pred,s2); predict pred out=pred; estimate 'initial for trt1' c0 ; estimate 'removal coefficient for trt1' k0; estimate 'initial for trt2' c0 + d1; estimate 'removal coefficient for trt2' k0 + d2; estimate 'half-life for trt1' -log(2)/k0; estimate 'half-life for trt 2' -log(2)/(k0 + d2); ods output parameterestimates=parms additionalestimates=addl; run; I think the use of the CMPTMODEL statement would enable fitting all of the data, but I will have to learn more about it before I post anything.   SteveDenham   ... View more