Hi Robert: It appears to me that you have a split-plot structure, so let me see if I have it right.
The 'whole-plot' experiment looks like a replicated randomized block design - the studies are the blocks, the treatments A and B are the whole-plot treatments, each of which is assigned randomly to three subjects...basically the 'between-subjects' part of the design. The within-subject treatment factor is site (let's call them 1 and 2 to avoid confusion with the treatment labels), and it makes sense to me that the two measurements are correlated within animal, but I don't quite see why it makes sense that they should be correlated between animals not treated alike in the same study. I'm not in pharma, so I might learn something by asking. I tend to look at the means model part of a design with ANOVA just because I'm old: Study 9 Treatment 1 Study * treatment 9 Subjects 40 (whole-plot error) Site 1 Site * Study 9 Site * Treatment 1 Site * Study * Trt 9 Residual 40 (split plot error) Study and subjects can reasonably be thought of as random effects. If the same sites are chosen for each subject, they would have to be fixed. The interactions with study and subject are random. I agree with the fixed effects specification so far, but some of the random interactions aren't obvious to me, although I can understand how they arise from the structure of the data. A couple of questions: (1) Are the studies expected to be correlated, and if so, was that the motivation for the replicate subjects per treatment level? I'm rather accustomed to blocks representing independent replications of the experiment, which I would have expected by the use of different subjects in each study. Or is all of this a necessary precaution in the clinical trial? (2) Since sites were the same for each subject, I can see the within-subject correlation and between-subject correlation for subjects in the same study with the same treatment, but I'm curious as to why the Site * Study interaction is relevant. The whole-plot part of this is pretty easy, but I think I (and perhaps others) would need some data to play with to work on getting the random effects and correlation structure specified properly. It also appears you will need to use lme4 rather than nlme for this problem due to the crossed random effects, which lme() can't handle. That may be the source of your trouble :) This is certainly an interesting problem; thanks for sharing it. I might also suggest that this be taken to the r-sig-mixed-models list, where you are likely to have more people who are interested in this type of problem. Cheers, Dennis On Thu, Nov 25, 2010 at 5:00 AM, Robert Kinley <kinley_rob...@lilly.com>wrote: > My small brain is having trouble getting to grips with lme() > > I wonder if anyone can help me correctly set the random = argument > to lme() for this kind of setup with (I think) 9 variance/covariance > components ... > > Study.1 Study.2 ... > Study.10 > Treatment.A: subject: 1 2 3 4 5 6 etc. 28 29 30 > > Treatment.B: subject: 31 32 33 34 35 36 58 59 60 > A variable is measured at 2 fixed sites (A and B) on each subject > > so we have fixed effects :- > > between-Treatments > between-sites (A and B) > Treatment*site interaction > > > and we have random effects :- > > study effects at site A > study effects at site B > correlation between site A and site B study effects > > study*treatment interaction effects at site A > study*treatment interaction effects at site B > correlation between site A and B study*treatment interaction effects > > residual (between-subject) effects at site A > residual (between-subject) effects at site B > correlation between site A and B residuals (between-subject) effects > > My problem is formulating the random = argument to give estimates > of all 9 random components ... > > Hope someone can help ... > > Robert Kinley > > > > > Study: Pos tissue VC, Neg tissue VC, Pos/Neg tissue > correlation > Study*Group: Pos tissue VC, Neg tissue VC, Pos/Neg tissue > correlation > Residual (animal): Pos tissue VC, Neg tissue VC, Pos/Neg tissue > correlation > > > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org 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. > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org 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.
