From: Frank E Harrell Jr <f.harrell_at_vanderbilt.edu>

Date: Thu, 17 May 2007 07:29:08 -0500

Date: Thu, 17 May 2007 07:29:08 -0500

Karl Knoblick wrote:

> Hallo!

*>
**> I have two groups (placebo/verum), every subject is measured at 5 times, the first time t0 is the baseline measurement, t1 to t4 are the measurements after applying the medication (placebo or verum). The question is, if there is a significant difference in the two groups and how large the differnce is (95% confidence intervals).
**>
**> Let me give sample data
**> # Data
**> ID<-factor(rep(1:50,each=5)) # 50 subjects
**> GROUP<-factor(c(rep("Verum", 115), rep("Placebo", 135)))
**> TIME<-factor(rep(paste("t",0:4,sep=""), 50))
**> set.seed(1234)
**> Y<-rnorm(250)
**> # to have an effect:
**> Y[GROUP=="Verum" & TIME=="t1"]<-Y[GROUP=="Verum" & TIME=="t1"] + 0.6
**> Y[GROUP=="Verum" & TIME=="t2"]<-Y[GROUP=="Verum" & TIME=="t2"] + 0.3
**> Y[GROUP=="Verum" & TIME=="t3"]<-Y[GROUP=="Verum" & TIME=="t3"] + 0.9
**> Y[GROUP=="Verum" & TIME=="t4"]<-Y[GROUP=="Verum" & TIME=="t4"] + 0.9
**> DF<-data.frame(Y, ID, GROUP, TIME)
**>
**> I have heard of different ways to analyse the data
**> 1) Comparing the endpoint t4 between the groups (t-test), ignoring baseline
*

Don't even consider this

> 2) Comparing the difference t4 minus t0 between the two groups (t-test)

This is not optimal

Using t0 as a covariate is the way to go. A question is whether to just use t4. Generally this is not optimum.

> 4) Taking a summary score (im not sure but this may be a suggestion of Altman) istead of t4

*> 5) ANOVA (repeated measurements) times t0 to t5, group placebo/verum), subject as random factor - interested in interaction times*groups (How to do this in R?)
**> 6) as 5) but times t1 to t5, ignoring baseline (How to do this in R?)
**> 7) as 6) but additional covariate baseline t0 (How to do this in R?)
**>
**> What will be best? - (Advantages / disadvantages?)
**> How to analyse these models in R with nested and random effects and possible covariate(ID, group - at least I think so) and random parameter ID)? Or is there a more simple possibility?
*

It's not obvious that random effects are needed if you take the correlation into account in a good way. Generalized least squares using for example an AR1 correlation structure (and there are many others) is something I often prefer. A detailed case study with R code (similar to your situation) is in http://biostat.mc.vanderbilt.edu/FrankHarrellGLS . This includes details about why t0 is best to consider as a covariate. One reason is that the t0 effect may not be linear.

If you want to focus on t4 it is easy to specify a contrast (after fitting is completed) that tests t4. If time is continuous this contrast would involve predicted values at the 4th time, otherwise testing single parameters.

Frank Harrell

*>
*

> Perhaps somebody can recommend a book or weblink where these different strategies of analysing are discussed - preferable with examples with raw data which I can recalculate. And if there is the R syntax includede - this would be best!

*>
**> Any help will be appreciate!
**>
**> Thanks!
**> Karl
*

-- Frank E Harrell Jr Professor and Chair School of Medicine Department of Biostatistics Vanderbilt University ______________________________________________ R-help_at_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 Thu 17 May 2007 - 12:38:50 GMT

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