[R] treatment effect at specific time point within mixedeffects model

[Doran, Harold]

Hi David:

In looking at your original post it is a bit difficult to ascertain
exactly what your null hypothesis was. That is, you want to assess
whether there is a treatment effect at time 3, but compared to what. I
think your second post clears this up. You should refer to pages 224-
225 of Pinhiero and Bates for your answer. This shows how to specify
contrasts.

> -----Original Message-----
> From: r-help-bounces at stat.math.ethz.ch 
> [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of 
> Afshartous, David
> Sent: Thursday, October 05, 2006 11:08 AM
> To: Spencer Graves
> Cc: r-help at stat.math.ethz.ch
> Subject: Re: [R] treatment effect at specific time point 
> within mixedeffects model
> 
> Hi Spencer,
> 
> Thanks for your reply.
> I don't think this answers my question.
> 
> If I understand correctly, your model simply removes the 
> intercept and thus the intercept in fm1 is the same as the 
> first time factor in fm1a ... but am I confused as to why the 
> other coefficient estimates are now different for the time 
> factor if this is just a re-naming.  
> The coefficient estimates for the interactions are the same 
> for fm1 and fm1a, as expected.
> 
> But my question relates to the signifcance of drug at a 
> specific time point, e.g., time = 3.  The coeffecieint for 
> say "factor(time)3:drugP" measures the interaction of the 
> effect of drug=P and time=3, which is not testing what I want 
> to test.  Based on the info below, I want to compare 3) versus 4).
> 
> 1) time=1, Drug=I : Intercept
> 2) time=1, Drug=P : Intercept + DrugP
> 3) time=3, Drug=I : Intercept + factor(time)3
> 4) time=3, Drug=P : Intercept + factor(time)3 + DrugP + 
> factor(time)3:drugP
> 
> I'm surprised this isn't simple or maybe I'm missing 
> something competely.
> 
> thanks
> dave
> 
> 
> 
> 
> 
> -----Original Message-----
> From: Spencer Graves [mailto:spencer.graves at pdf.com]
> Sent: Wednesday, October 04, 2006 7:11 PM
> To: Afshartous, David
> Cc: r-help at stat.math.ethz.ch
> Subject: Re: [R] treatment effect at specific time point 
> within mixed effects model
> 
>       Consider the following modification of your example: 
> 
> fm1a = lme(z ~ (factor(Time)-1)*drug, data = data.grp, random 
> = list(Patient = ~ 1) )
> 
> summary(fm1a)
> <snip>
>                          Value Std.Error DF    t-value p-value
> factor(Time)1       -0.6238472 0.7170161 10 -0.8700602  0.4047
> factor(Time)2       -1.0155283 0.7170161 10 -1.4163256  0.1871
> factor(Time)3        0.1446512 0.7170161 10  0.2017405  0.8442
> factor(Time)4        0.7751736 0.7170161 10  1.0811105  0.3050
> factor(Time)5        0.1566588 0.7170161 10  0.2184871  0.8314
> factor(Time)6        0.0616839 0.7170161 10  0.0860286  0.9331
> drugP                1.2781723 1.0140139  3  1.2605077  0.2966
> factor(Time)2:drugP  0.4034690 1.4340322 10  0.2813528  
> 0.7842 factor(Time)3:drugP -0.6754441 1.4340322 10 -0.4710104 
>  0.6477 factor(Time)4:drugP -1.8149720 1.4340322 10 
> -1.2656424  0.2343 factor(Time)5:drugP -0.6416580 1.4340322 
> 10 -0.4474502  0.6641 factor(Time)6:drugP -2.1396105 
> 1.4340322 10 -1.4920240  0.1666
> 
>       Does this answer your question? 
>       Hope this helps. 
>       Spencer Graves
> 
> Afshartous, David wrote:
> >  
> > All,
> >
> > The code below is for a pseudo dataset of repeated measures on 
> > patients where there is also a treatment factor called 
> "drug".  Time 
> > is treated as categorical.
> >
> > What code is necessary to test for a treatment effect at a 
> single time
> 
> > point,
> > e.g., time = 3?   Does the answer matter if the design is a 
> crossover
> > design,
> > i.e, each patient received drug and placebo?
> >
> > Finally, what would be a good response to someone that 
> suggests to do 
> > a simple t-test (paired in crossover case) instead of the 
> test above 
> > within a mixed model?
> >
> > thanks!
> > dave
> >
> >
> >
> > z = rnorm(24, mean=0, sd=1)
> > time = rep(1:6, 4)
> > Patient = rep(1:4, each = 6)
> > drug = factor(rep(c("I", "P"), each = 6, times = 2)) ## P = 
> placebo, I
> 
> > = Ibuprofen dat.new = data.frame(time, drug, z, Patient) data.grp = 
> > groupedData(z ~ time | Patient, data = dat.new)
> > fm1 = lme(z ~ factor(time) + drug + factor(time):drug, data = 
> > data.grp, random = list(Patient = ~ 1) )
> >
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> >
> 
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