Hello!   I have found a significant interaction effect of moderator A on the association between X and Y. I now want to test and plot at which values of A (-1 SD, 0, +1 SD) the relationship between X and Y significant. I am examining both within and between subject associations using glimmix for drinking frequency (drinks/no drinks) and nlmixed for drinking quantity (count data), however, I only found a significant interaction effect at the within-person level. Any help with this would be much appreciated. ... View more

Hi,   I am using SAS version 9.4 to examine the association between physical activity (PA) and alcohol consumption (AC) at between- and within-person levels over 21 days. The study design is multilevel with repeated measures, with days nested in individuals (253 individuals - 21 days/measurements). My time structure is days (21 days - 0,....,21). My outcome variable is AC (total drinks per day per individual - count data) and it follows a Poisson distribution (there is evidence of overdispersion, so I am trying to fit a negative binomial distribution). Predictors are average PA (person-specific average measures of PA centered at the grand mean) and daily PA (person-centered).   Issue 1: The random intercept model converged fine but the model didn't fit the data very well. Issue 2: When I added average PA, the model failed to converge.   I have tried different types of covariance structures, but none seemed to work. I am wondering if the issue could be zero-inflation in the outcome variable, as 70% of my AC observations are 0? I found nothing on GLIMMIX dealing with zero-inflated negbin though, so I am wondering whether I need to use another procedure? Also, my PA variables are significantly positively skewed, so I am wondering if that could be a part of the issue and whether I need to transform the predictors? I'd very much appreciate any input, thanks!   Below is the code I used:   *random intercept model that converged but didn't fit the data very well*; proc glimmix data=SHB.Days_final lognote plots=all; class IDnum Days_c; model AC = / solution dist=negbin; random Days_c / subject=IDnum type=ar(1) residual; nloptions maxiter=500; run; *model with predictor that did not converge*; proc glimmix data=SHB.Days_final lognote plots=all; class IDnum Days_c; model AC = averagePA / solution dist=negbin; random Days_c / subject=IDnum type=ar(1) residual; nloptions maxiter=500; run;     ... View more