Dear R-users,
In a randomized placebo-controlled within-subject design, subjects recieved
a psycho-active drug and placebo. Subjects filled out a questionnaire
containing 15 scales on four different time points after drug
administration. In order to detect drug effects on each time point, I
compared scale values between placebo and drug for all time conditions and
scales, which sums up to 4*15=60 comparisons.
I have summarized the results in a data.frame with columns for t test
results including confidence intervals and mean-differences:
df1<-data.frame(trt=gl(2,35),matrix(rnorm(4200),70,60))
df2<-as.data.frame(matrix(NA,60,6))
names(df2)<-c('t','df','p','lower','upper','mean.diff')
for (i in 1:60) {df2[i,1:6]<-as.numeric(
unlist(t.test(df1[,i+1]~df1$trt,paired=T))[1:6])}
Now, I want to adjust the confidence intervals for multiple comparisons.
For a Bonferroni-adjustment, I did the following:
df2$std.error.of.diff<-df2$mean.diff/df2$t
ci<-qt(p=1-(0.05/nrow(df2)),df=df2$df)*df2$std.error.of.diff
ci.bonf<-data.frame(lower=df2$mean.diff-ci,upper=df2$mean.diff+ci)
I hope this is the correct method. However, I think, the
Bonferroni-adjustment would be much too conservative. I need a less
conservative approach, perhaps, something like Holm's method, which I can
easily apply to the p-value with p.adjust(df2$p,method='holm'). Is there
package, which can do this for the confidence-interval or could someone
provide a simple script to calculate this?
Thanks a lot!
Erich
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