Douglas That's very helpful! It's just a syntax error in my use of lme (I find the documentation hard to figure!). I'm actually also using the formula
lme(Measurement~Treatment/When etc) as this gives the right contrasts to look at the interactions between each of the treatments and before/after. I'm still working on a model formula that will give me a single p-value for 'is the difference between before and after different for different treatments'. And this all feels much happier than not using a random effects model and simply using patient as a blocking variable (i.e. Measurement ~ Treat/When + Patient) which seems unsatisfactory for independence reasons. (I'm not really a statistician - just the most stats-savvy person in my department!) Thanks, Dov On 31 Jul 2006, at 18:38, Douglas Bates wrote: > On 7/31/06, Dov Stekel <[EMAIL PROTECTED]> wrote: >> Hi >> >> I have been asked by a colleague to perform a statistical analysis >> which uses random effects - but I am struggling to get this to work >> with nlme in R. Help would be very much appreciated! >> >> Essentially, the data consists of: >> >> 10 patients. Each patient has been given three different treatments >> (on >> three separate days). 15 measurements (continuous variable) have been >> taken from each patient both before and after each of the treatments. >> So the data looks like: >> >> Patient When Treat Measurement >> a before A 10.3 >> a before A 11.2 >> ... >> a after A 12.4 >> ... >> a before B 11.6 >> ... >> a after B ... >> >> and the same for treatment C, patients, b,c,d, etc. >> >> My colleague would like to test to see if the treatments are different >> from each other. i.e., is the change (before to after) due to the >> treatments different between the treatments. It would seem to me like >> a >> random effects model in which we are interested in the significance of >> the interaction terms Treat:When, with repeated measures in the >> patients (who are random effects, but crossed with the covariates). >> Unfortunately, the groupedData formula only lets me put a single >> covariate on the LHS - nothing as complicated as this! > > I'm not sure I understand what the LHS of a formula for a groupedData > object has to do with your question. > > You will need to specify the model that you wish to fit by lme and, > for that, you will need to decide which terms should be fixed effects > and which random effects. Do you think that the patients contribute > only an additive shift in the response or do you think that the > patients may have different initial values and different levels of > change in the Before/After responses? > > It seems that you could begin by fitting > > fm1 <- lme(Measurement ~ When*Treat, random = ~ 1 | Patient, data = > ...) > > and > > fm2 <- lme(Measurement ~ When*Treat, random = ~ 1|Patient/When, data = > ...) > > There are many other variations that you could consider but we can > only guess at because we don't know enough of the context of the data. > For example, it is possible that it would be appropriate to eliminate > a main effect for Treat because the Treatment cannot be expected to > influence the measurement before the Treatment is applied. The > fixed-effects term would then be specified as > > fm3 <- lme(Measurement ~ When + When:Treat, random = ...) > >> >> I could, of course, advise her to simply combine all 30 data points >> for >> each treatment in each patient into a single number (representing >> difference between before and after), but is there a way to use all >> the >> data in an LME? >> >> Thanks! >> >> >> Dov >> >> >> >> ************************************************************** >> >> Dr Dov Stekel >> Lecturer in Bioinformatics >> School of Biosciences >> University of Birmingham >> Birmingham B15 2TT >> Tel: +44 121 414 4209 >> Email: [EMAIL PROTECTED] >> >> ______________________________________________ >> R-help@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. >> >> ************************************************************** Dr Dov Stekel Lecturer in Bioinformatics School of Biosciences University of Birmingham Birmingham B15 2TT Tel: +44 121 414 4209 Email: [EMAIL PROTECTED] ______________________________________________ R-help@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.