Dear Frank Harrell, many thanks for your answers!!!
I have downloaded your Harrell_notes.pdf and I think it would be best to have a look in some books you mentioned in the bibliographie. Can you recommend one especially? TO ANYBODY: Has anybody examples for similar data sets (as below) with R code? This would be a great help! (My impression of the example of Frank Harrell at the end of Harrell_notes.pdf is that it is more complicated and therefore more difficult to understand than the simplier data set I want to analyse.) Thanks!!! Karl ----- Ursprüngliche Mail ---- Von: Frank E Harrell Jr <[EMAIL PROTECTED]> An: Karl Knoblick <[EMAIL PROTECTED]> CC: [email protected] Gesendet: Donnerstag, den 17. Mai 2007, 14:29:08 Uhr Betreff: Re: [R] How to analyse simple study: Placebo-controlled (2 groups) repeated measurements (ANOVA, ANCOA???) 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 > 3) Comparing the endpoint t4 with t0 as a covariate between the groups (ANOVA > - how can this model be calculated in R?) 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 __________________________________ Yahoo! Clever: Stellen Sie Fragen und finden Sie Antworten. Teilen Sie Ihr Wissen. www.yahoo.de/clever ______________________________________________ [email protected] 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.
