Programming the statistical procedures from SAS

Proc Mixed - Covary a baseline value

New User
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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: 4,751

Re: Proc Mixed - Covary a baseline value

With option / AT time=0 ?

Posts: 186

Re: Proc Mixed - Covary a baseline value

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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