Prediction of Independent Measurements

[Yughaber]

Thank you for your help!

[SteveDenham]

The only thing I would add here is to first investigate whether the slope is not significantly different for the two postures.  Start with this code:

proc mixed data=Have;
   class Subject Position(ref='Sitting');
   model Valve2 = Valve1 Position  Valve1*Position/  s chisq outpred=MixedOut;
   random intercept / subject=Subject;          /* each subject gets its own intercept */
run;

If Valve1*Position is not significant, you can assume that the slopes aren't different for the two Positions.  If it is significant, then consider this model:

proc mixed data=Have;
   class Subject Position(ref='Sitting');
   model Valve2 = Valve1 Valve1*Position /  s chisq outpred=MixedOut;
   random intercept / subject=Subject;          /* each subject gets its own intercept */
run;

Under this model, if you are interested in LSmeans (which I doubt, as this looks like a pure regression problem), you should do the comparisons at 3 values for Valve1 (high, mean, low).  See the chapter on analysis of covariance in SAS for Mixed Models (any edition).

An alternative way of thinking about this is as a bivariate correlation within posture.  SGPLOT would enable plotting confidence ellipses that provide an excellent graphical presentation.

SteveDenham

[Yughaber]

Thank you for your input.

[Yughaber]

Hi Rick, 

If you have some free time I'll appreciate your help with a follow up question to this analysis. Let's say if I were to add in age and gender to this data and control for them, would I be able to include it in the mixed model somehow?

Thanks!! 

[Rick_SAS]

Add Gender to the CLASS statement and add Gender and Age to the MODEL statement.