# Re: [R] treatment effect at specific time point within mixed effects model

From: Afshartous, David <afshart_at_exchange.sba.miami.edu>
Date: Thu 05 Oct 2006 - 15:07:52 GMT

Hi Spencer,

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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