Hi, I am trying to do a repeated measures ANOVA to determine if there is a
significant difference between two sets of timecourse data. Each individual
was given a single treatment and then measured for one variable for 10 days.
Here is made-up example of what my data would look like:

data<-data.frame(subject=rep(c("A1","A2","A3","B1","B2","B3"),10),treatment=rep(c("A","B"),each=3),
day=rep(c(1:10),each=6),response=rnorm(60))

This is the code I run to test for a difference between treatments A and B
over the course of the 10 days:

aov(response~day*treatment+Error(subject), data=data)

I believe this is the correct model to use, though I could definitely be
wrong.

Here is the output I get from my actual data (using summary(aov)):

Error: subject
          Df Sum Sq Mean Sq F value   Pr(>F)
treatment  1 4258.1  4258.1  12.588 0.001344 **
Residuals 29 9810.2   338.3
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Error: Within
               Df Sum Sq Mean Sq F value    Pr(>F)
day             9  98345   10927 150.313 < 2.2e-16 ***
day:treatment   9   6844     760  10.461 8.374e-14 ***
Residuals     261  18974      73
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1


The p-value for "treatment" is the same as what I would get if I lumped the
data from all 10 days together, so I assume this is not what I want here.
However, am I correct in interpreting the p-value for day:treatment as what
I want? Does this tell me that there is a difference between the two groups
over the course of the 10 days (regardless of which days actually are
different) with respect to the fact that I am measuring the same subjects
each day?

Thanks for any help!
-- 
Brooke LaFlamme

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