Ooh, > lme(Measurement~Treatment/When etc) >
and lm(Measurement ~ Treat/When + Patient) give exactly the same results! How interesting! Dov > 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. > ************************************************************** 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.