Dear R-helpers:

My question is how do I efficient and valid correct for multiple tests in a repeated measurement design: 
Suppose we measure at two distinct visits with repeated subjects a treatment difference on the same variable. 
The treatment differences are assessed with a mixed model and adjusted by two methods for multiple tests:

# 1. Method: Adjustment with library(multcomp)

library(nlme)
library(multcomp)

n <- 30 # number of subjects
sd1 <- 0.5 # Standard deviation of the random intercept
sd2 <- 0.8 # Standard deviation of the residuals
id <- rep(1:n,times=2); v <- rep(0:1, each=n); trt <- rep(sample(rep(0:1, each=n/2), n), times=2)
df <- data.frame(id, v, trt, 
y=2 + rep(rnorm(10,0,sd1), times=2) + 0.5*v + 0.7*trt + 0.2*v*trt + rnorm(2*n, 0, sd2))
m1 <- lme(y ~ v + trt + v*trt, data=df, random= ~ 1|id)
summary(m1)
par4 <- m1$coef$fixed
cov4 <- vcov(m1)
cm4 <- matrix(c(0, 0, 1, 0, 0, 0, 1, 1), nrow = 2, ncol=4, byrow=TRUE, 
	dimnames = list(c("diff/v=0", "diff/v=1"), c("C.1", "C.2", "C.3", "C.4")))
v4 <- csimint(estpar=par4, df=n-6, # I'm not sure whether I found 
     # the correct degrees of freedom
	covm=cov4,
	cmatrix=cm4, conf.level=0.95)
sv4 <- summary(v4)

# 2. Method: I found in Handbook of Statistics Vol 13, p.616,
# same can be found in http://home.clara.net/sisa/bonhlp.htm
# Bonferroni on correlated outcomes:

raw.p <- sv4$p.value.raw
co4 <- cor(df$y[df$v==0],df$y[df$v==1])
rho <- mean(c(1,co4,co4,1))
pai <- 1-(1-raw.p)^2^(1-rho) 

# The results of two methods are presented in the following lines:
out <- cbind(raw.p, sv4$p.value.bon, sv4$p.value.adj, pai)
colnames(out) <- c("raw.p", "bon.p", "multcomp.p", "bon.cor.p")
out

As you can see there are quite big differences 
between the two ways adjusting for multiple tests on repeated measurements. 
I guess that the multcomp library is not appropriate for this kind of hypotheses. 
However I could not find an explanation in the help files. 
May be one of the experts can point me in the right direction?

Kind regards,

Dominik

platform i386-pc-mingw32
arch     i386           
os       mingw32        
system   i386, mingw32  
status                  
major    2              
minor    2.1            
year     2005           
month    12             
day      20             
svn rev  36812          
language R    

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