From: Afshartous, David <afshart_at_exchange.sba.miami.edu>

Date: Thu 05 Oct 2006 - 15:07:52 GMT

R-help@stat.math.ethz.ch mailing list

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 01:14:01 2006

Date: Thu 05 Oct 2006 - 15:07:52 GMT

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).

- time=1, Drug=I : Intercept
- time=1, Drug=P : Intercept + DrugP
- time=3, Drug=I : Intercept + factor(time)3
- 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 0.2343factor(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) )
**>
**> ______________________________________________
**> R-help@stat.math.ethz.ch mailing list
**> 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.
**>
*

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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 01:14:01 2006

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