Hi al, I have a dataset (see attached), which basically involves 4 treatments for a chemotherapy drug. Samples were taken from 2 biopsy locations, and biopsy were taken at 2 time points. So each subject has 4 data points (from 2 biopsy locations and 2 time points). The objective is to study treatment difference.? I used lme to fit a mixed model that uses "biopsy.site nested within pid" as a random term, and used corAR1() as the correlation structure for between the 2 time points: library(nlme) test<-read.table("test.txt",sep='\t',header=T,row.names=1) fit<-lme(y~age + time * trt, random=~1|pid/biopsy.site, data = test, correlation=corAR1()) First, by above model specification, corAR1() is used for the correlation between the 2 time points; what is the correlation structure implicitly used for between biopsy locations? How do I specify a particular correlation structure for between biopsy locations in this situation? Second, does anyone know how to write the above mixed model in SAS? One of my colleagues wrote the following, but it gave me different results: proc mixed data=test; class time trt pid biopsysite; model y=age time trt time*trt; random biopsysite repeated pid / type=ar(1) run; Is there anyone familiar with SAS and know if the above SAS code does what the R code does? Many thanks John -------------- next part -------------- An embedded and charset-unspecified text was scrubbed... Name: test.txt URL: <https://stat.ethz.ch/pipermail/r-help/attachments/20110807/f9e1ff72/attachment.txt>

Dear Thierry, Thanks a lot for pointing me to the right direction! I still have some questions, really appreciate if you could provide any help: Is there a relationship between the nested mixed model that I used vs. the model that you gave (using biop location as random slope of pid), i.e. lme(y~age + time * trt, random=~1|pid/biopsy.site, data = test, correlation=corAR1()) vs. lme(y~age + time * trt, random=~0? + biopsy.site|pid, data = test,? correlation=corAR1()) In my nested mixed model, a variance of pid and a variance of biopsy.site within pid will be estimated. In your mixed model, there is no variance estimated at the pid level , instead variance for each biopsy.site is given. I thought the statistical model was always the same for both mixed models: y_ijk=fixed_effect + b_i + b_ij + e_ijk where b_i is the random effect for pid, and v_ij is the random effect for biopsy.site within pid. I thought that the difference is in my nested mixed model, b_ij is independent of each other and has the same variance, whereas in your mixed model, b_ij is modeled by the pdClasses() chosen. If my thought was correct, then my model should be the same as lme(y~age + time * trt, random=list(pid=pdIdent(~0? + biopsy.site)), data = test,? correlation=corAR1()) But they are not the same. What did I misunderstand here? Many thanks John ----- Original Message ----- From: "ONKELINX, Thierry" <Thierry.ONKELINX at inbo.be> To: array chip <arrayprofile at yahoo.com>; "r-sig-mixed-models at r-project.org" <r-sig-mixed-models at r-project.org> Cc: Sent: Monday, August 8, 2011 1:19 AM Subject: RE: [R] mixed model fitting between R and SAS Please don't cross-post. - corAR1() models the correlation between the residuals of the two time points. - if you want a specific correlation structure for biopsy locations the you must use on of the pdClasses() and use biopsy location as random slope of pid rather than random effect nested in pid. #basic structure = positive definitive symmetrical variance/covariance matrix lme(y~age + time * trt, random=~0? + biopsy.site|pid, data = test,? correlation=corAR1(~time)) #no correlation between biopsy location and different variance lme(y~age + time * trt, random=list(pid? = pdDiag(~0? + biopsy.site), data = test,? correlation=corAR1(~time)) #no correlation between biopsy location and equal variance lme(y~age + time * trt, random=list(pid? = pdIdent(~0? + biopsy.site), data = test,? correlation=corAR1(~time)) Note that since you have only two biopsy locations there will be no difference between pdSymm (the default) and pdCompSymm Best regards, Thierry ---------------------------------------------------------------------------- ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek team Biometrie & Kwaliteitszorg Gaverstraat 4 9500 Geraardsbergen Belgium Research Institute for Nature and Forest team Biometrics & Quality Assurance Gaverstraat 4 9500 Geraardsbergen Belgium tel. + 32 54/436 185 Thierry.Onkelinx at inbo.be www.inbo.be To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher The plural of anecdote is not data. ~ Roger Brinner The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. ~ John Tukey> -----Oorspronkelijk bericht----- > Van: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] > Namens array chip > Verzonden: maandag 8 augustus 2011 8:48 > Aan: r-sig-mixed-models at r-project.org > CC: r-help > Onderwerp: [R] mixed model fitting between R and SAS > > Hi al, > > I have a dataset (see attached), which basically involves 4 treatments for a > chemotherapy drug. Samples were taken from 2 biopsy locations, and biopsy > were taken at 2 time points. So each subject has 4 data points (from 2 biopsy > locations and 2 time points). The objective is to study treatment difference. > > I used lme to fit a mixed model that uses "biopsy.site nested within pid" as a > random term, and used corAR1() as the correlation structure for between the 2 > time points: > > > library(nlme) > > test<-read.table("test.txt",sep='\t',header=T,row.names=1) > fit<-lme(y~age + time * trt, random=~1|pid/biopsy.site, data = test, > correlation=corAR1()) > > First, by above model specification, corAR1() is used for the correlation between > the 2 time points; what is the correlation structure implicitly used for between > biopsy locations? How do I specify a particular correlation structure for between > biopsy locations in this situation? > > Second, does anyone know how to write the above mixed model in SAS? One of > my colleagues wrote the following, but it gave me different results: > > proc mixed data=test; > > class time trt pid biopsysite; > model y=age time trt time*trt; > random biopsysite > repeated pid / type=ar(1) > run; > > Is there anyone familiar with SAS and know if the above SAS code does what the > R code does? > > Many thanks > > John