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

From: Doran, Harold <HDoran_at_air.org>
Date: Thu 05 Oct 2006 - 15:39:31 GMT


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@stat.math.ethz.ch
> [mailto:r-help-bounces@stat.math.ethz.ch] On Behalf Of
> Afshartous, David
> Sent: Thursday, October 05, 2006 11:08 AM
> To: Spencer Graves
> Cc: r-help@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@pdf.com]
> Sent: Wednesday, October 04, 2006 7:11 PM
> To: Afshartous, David
> Cc: r-help@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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> > http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
> >
>
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https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Fri Oct 06 01:48:14 2006

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