I am going to pull a Ksharp trick here and ask someone else who knows quite a bit about PHREG.  So calling @FreelanceReinh    SteveDenham ... View more

I tried a variety of splines (penalized B, thin-plate and loess) to see if there was any indication of something that might look like a plateau, without using a polynomial fit.  The penalized B gave something that looked like this: The other methods showed a smooth decrease.  Removing the last date from the penalized B gives something that turns back up (no plateau), while the other two methods give; Loess: Thin plate spline:   These give some support to a plateau being reached some time in November 2019.  That last point has a tremendous amount of leverage, and really shifts the resulting plots.  Code for these follows:   proc sgplot data=have; scatter x=date y=duration; pbspline x=date y=duration; run; ods graphics on; proc tpspline data=have plots(only)=(criterionplot fitplot(clm)); model duration = (date) /alpha = 0.1; output out = result pred uclm lclm; run; proc loess data=have; model duration=date; run; /* Delete the last point */ data have2; set have; if surgeryno=25 then delete; run; proc sgplot data=have2; scatter x=date y=duration; pbspline x=date y=duration; run; proc tpspline data=have2 plots(only)=(criterionplot fitplot(clm)); model duration = (date) /alpha = 0.1; output out = result pred uclm lclm; run; proc loess data=have2; model duration=date; run; SteveDenham       ... View more

Could you consider "block" as a spatial co-ordinate based on the centroid of the area?  Then a spatial analysis with boat as the subject could be explored.   It would probably mean using a mixed model approach with sample weighting because this is a survey rather than a designed experiment, and a two-dimensional spatial covariance type would be needed, so you may be in some rough waters (no pun intended there).   You may want to look at some of the ecology literature on analysis of this type.  I know that field has a strong R preference, but similar models can be constructed in SAS.   SteveDenham ... View more

What procedure was used to analyze the imputed datasets?  It will make a difference in how to standardize.  My gut feeling would be to standardize the imputed data at that stage, and allow MIANALYZE to composite the results of the standardized models.  Looking at the response from Dale in that thread, it appears that was his recommendation as well (although in more detail), and I have a great deal of respect for his answers.   SteveDenham ... View more

Starting at the top: The solution vector is what is used to create the least squares means.  I find it useful if I need an estimate and standard error of a continuous covariate.  Otherwise, the latter two are more useful. The Type3 F tests are testing to see if at least one mean is different from all the others in that effect (main or interaction). This is the primary test of "significance' for an effect. The LSM (least squares means) tells you what the expected values are for each of the levels of the effects.  Using the diffs option allows you to test if one particular mean is "significantly' different from another.   I don't know what you mean by more powerful.  Do you mean which had a greater effect on the mean?  That is generally what Cohen's D is all about.  However, mixed models don't really lend themselves to calculating effect sizes. If you really want to look at something like it, add the /diff option to the lsmeans statement. The results table should present the t values for each comparison.  This is a ratio of the difference to the standard error of the difference.  Cohen's D is a ratio of the difference to the standard deviation of the reference group, so they should be analogous in direction.   SteveDenham ... View more