Programming the statistical procedures from SAS

Proc Mixed - Covary a baseline value

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Proc Mixed - Covary a baseline value

I'm hoping someone might be able to help me adjust the lsmeans for a baseline value.  That is, DV was measured 8 times: 0 min, 15 min, 30 min, 45 min, 60 min, 120 min, and 90 min, and we'd like to adjust the lsmeans for the baseline value (0 min).  Is this possible? 

Respected Advisor
Posts: 5,053

Re: Proc Mixed - Covary a baseline value

Posted in reply to eileenwright

With option / AT time=0 ?

Posts: 275

Re: Proc Mixed - Covary a baseline value

Posted in reply to eileenwright

An analysis of covariance model might work, if what you want is to estimate the means at 15, 30, 45, 60, 90, and 120 minutes for a common value of baseline. (The baseline time=0 value would be the covariate.)


Because (presumably) of the repeated measures on a subject at 0, ..., 120 min, the model would be more complicated than your standard ANCOVA (which would have data for time=0 and time= a second value). In particular, the relationship between data at time 15 and time 0 might be stronger than the relationship between data at time 120 and time 0 because noise intrudes as time passes--in other words, the slope of the regression of the response on baseline (time=0) might decrease with later times.


This is speculative and untested, but I would consider

proc glimmix data=have;
class time subjectID;
model response = time baseline time*baseline;
random time / subject=subjectID type=<some covariance structure, maybe ar(1)> residual;
lsmeans time / at mean; /* or some other value of baseline */

Issues to consider are

-- the nature of the relationship between response and baseline at each time (e.g., linear)

-- an appropriate covariance structure for the repeated measures within a subject

-- normality and homogeneity of variance (assuming normal distribution)

-- what a sensible "common" value for baseline might be, in context


Alternatively, you could compute a variable that represents deviance from baseline, either absolute (i.e., subtract the baseline value) or relative (e.g., divide by the baseline value). Or maybe a random coefficients model. 

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