Hello, there. I found your post while searching for something on the lognormal distribution in the community. I read from your profile that you have not been here for more than four years. In addition, this question was raised more than 10 years ago. I am not sure if your problem has been solved and if you need my answer for the time being. But I think somebody else may need it and am therefore here to offer my viewpoint on your problem. I think ANOVA is a suitable choice in terms of estimating the group means. But forming confidence intervals is not so intuitive under your setting. Why not try the accelerated failure time (AFT) model and put the group indicators as the only independent variables in your model? The AFT model is very inclusive in the sense that the lognormal distribution is only one of the popular distributions that can be modeled. Methods of inferences with respect to the AFT model, including the construction of confidence intervals of the dependent variable, may be more comprehensively studied than the log-transformed version of ANOVA you employed. I think you can have a try. I have also found an article that might solve your problem under another paradigm. You may take a look. Inferences on the means of lognormal distributions using generalized p-values and generalized confidence intervals - ScienceDirect ... View more

Take a look at the STORE statement of the PHREG procedure as well as the PLM procedure that utilizes things generated from the STORE statement. Some statistical graphs can be generated from the PLM procedure, but delicate modifications of them still necessitates the employment of the SG procedures like PROC SGPLOT. ... View more

Two white bars are presented in the second column. That represents variables removed from the second model because they weren't "significant".   SteveDenham ... View more

And for prediction intervals there are two varieties - the first is an approximate interval containing all of k future observations, the second is an interval containing the mean of k future observations. Be sure to check the section for Methods for Computing Statistical Intervals to note the difference between the two methods.   SteveDenham ... View more

@SteveDenham Good advice! ... View more

This article discusses the PARMS statement and gives examples of how to use it in PROC NLMIXED: "Use a grid search to find initial parameter values for regression models in SAS"   ... View more

Hi Steve   Many thanks for the reply. Yes sorry for the length - just in case the answer required knowledge of the data configuration. But no you did not miss the main question about the intuitive appeal or not of truncating the upper bound of the CIs rather than setting to infinity. I suppose I thought since somebody had gone to the effort of truncating (in the macro), and they are clearly knowledgeable about stats,  that there was a rationale that had merit. My data in the early interims will often give rise to these 1-sided CIs, and hence an alternative 2-sided CI would be of interest, but not if this is a risky (inferentially) strategy.       ... View more

SteveDenham, Check this: https://support.sas.com/kb/22/912.html ... View more

Hey everyone - there is a way to use PROC MIXED to fit multiple response variables. A good example can be found in the documentation for the TYPE= option for the REPEATED statement. Look at TYPE=UN@AR(1), where Height and Weight for children measured over time is given. The code suggested is:   proc mixed; class Var Year Child; model Y = Var Year Var*Year; repeated Var Year / type=un@ar(1) subject=Child; run; A similar situation is included in the PROC GLIMMIX documentation, but is much more difficult to implement. Start with the example: Joint Modeling of Binary and Count Data. Note that this approach requires different distribution types for each of the dependent variables, which is not required for the PROC MIXED method.   SteveDenham   ... View more

I am not sure this will work but you might try writing ESTIMATE statements that combine the solutions for the fixed and random effects. All I can suggest is to start simple and work it on up.   SteveDenham ... View more

I think you are, but you should be aware that the variable "assists" probably isn't normally distributed, nor are the residuals of the model you are trying to fit. You will likely need a generalized linear model, and maybe the repeated nature can be modeled as a random repeated effect in PROC GLIMMIX.  Try this:   Proc glimmix data=dsn; class home_away player; model assists= home_away/ solution dist=poisson; random Intercept/subject=player; random home_away/residual subject=player; run; You possibly could use PROC GEE, which might give more interpretable results. I'll call on @StatDave who knows a lot more about generalized linear model analysis than I do. Here would be my attempt:   proc gee data = dsn; class home_away player; model assists= home_away / dist=poisson link=log ; repeated subject = player / type=exch covb corrw; run; SteveDenham       ... View more

This helped a lot as well, thanks for the explanation, it helped cleared some of the questions that I had regarding modeling.  ... View more

This is great! Thank you so much for your advice!!! ... View more

Thanks @FreelanceReinh !   SteveDenham ... View more