[R] treatment effect at specific time point within mixedeffects model
Afshartous, David
afshart at exchange.sba.miami.edu
Thu Oct 5 18:48:15 CEST 2006
Hi Harold,
Thanks for your response.
I'll check out p.224 in P&B, thanks.
The null hypothesis is that there is no difference between say
A=[time=3, drug=I]
and B=[time=3, drug=P], or mu_A = mu_B. If the study is a crossover
design, i.e.,
each patient receives drug=I and drug=P, I assume that a simple paried
t-test could
also be employed at time=3.
However, I'd like to test this within a mixed effects model; With
respect to 3) and 4) below,
it seems somewhat difficult to express this specific hypothesis in terms
of the model paramaters. Ways in which this null are violated under
the mixed effects models could be:
1) there is no interaction between time and Drug, i.e., there is a drug
effect but
it is the same at all time points. (the specific interaction in 3) below
represents
the shift of the effect of drug=P from time=1 to time=3 ... so the lack
of significance of
the paramater "factor(time)3:drugP" doesn't capture what I want)
2) there is neither interaction nor drug effect (variable Drug not
significant).
But both these violations are more general than my null;
I think testing fixed effects 3) versus 4) below is what I want, but
this
also seems strange since possibly the drug effect and drug:time
interaction as defined in the model
are signicant (with time=1 as the reference baseline).
Regardless, I assume I would need to employ coef() and vcov() to obtain
the needed
info ... but I notice that coef() produces 4 values for the intercept of
fm1
below, does anyone know why this occurs?
I apologize if my explanation above is confusing, I've tried to make it
as clear as possible.
thanks again,
dave
-----Original Message-----
From: Doran, Harold [mailto:HDoran at air.org]
Sent: Thursday, October 05, 2006 11:40 AM
To: Afshartous, David; Spencer Graves
Cc: r-help at stat.math.ethz.ch
Subject: RE: [R] treatment effect at specific time point within
mixedeffects model
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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> > and provide commented, minimal, self-contained, reproducible code.
> >
>
> ______________________________________________
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