Dear Angelo, rma(yi=o, sei=se, mods=~s+t-1, method="REML")
is *a* way to run the arm-based pairwise meta-analysis. Whether it is the *correct* way is a question I cannot answer. lme(o~s+t-1, random=~t-1 | s, weights=(~ se^2)) is a different model. First of all, it adds a random effect only to each treatment arm within each study, while the rma model above gives a random effect to each observation. Moreover, the lme model assumes that the sampling variances are only known up to a proportionality constant, while the rma model assumes that they are known exactly. Similarly, lm(formula = o ~ s + t - 1, weights = 1/se.o^2) assumes that the sampling variances are only known up to a proportionality constant, while rma (with method="FE") assumes that they are known exactly. For the same reason will rma(yi=e, sei=se, method="REML") lme(e~1, random=~1 | s, weights=(~ se.e^2)) and rma(yi=e, sei=se.e, method="FE") lm(e~1, weights = 1/se.e^2) not give you the same results. Best, -- Wolfgang Viechtbauer http://www.wvbauer.com/ Department of Methodology and Statistics Tel: +31 (0)43 388-2277 School for Public Health and Primary Care Office Location: Maastricht University, P.O. Box 616 Room B2.01 (second floor) 6200 MD Maastricht, The Netherlands Debyeplein 1 (Randwyck) ----Original Message---- From: Angelo Franchini [mailto:angelo.franch...@bristol.ac.uk] Sent: Wednesday, August 04, 2010 16:26 To: Viechtbauer Wolfgang (STAT) Cc: 'Angelo Franchini'; r-help@r-project.org Subject: RE: [R] metafor and meta-analysis at arm-level > Hello Wolfgang. > > I'd appreciate if you could help me check whether I am doing the proper > thing to do an arm-level meta-analysis with metafor and what differences > there might be in trying to do the same with lme and lm. > > I am following the arm based model described in section 3.2 of the > Salanti's paper that you mentioned in your previous e-mail, namely: > > theta = B*eta + X*mu + W*beta > > where: > theta = vector of parameter for outcomes in treatment arms (theta_ij for > study i, treat. arm j) > eta = vector of parameter for outcomes in control arms (eta_i for > study i) > mu = vector of effects (treat. vs cont.) (mu_ij for study i, treat. > arm j) > beta = vector of random effects (beta_ij for study i, treat. arm j) > > > In my specific case with a pairwise meta-analysis, I had my data arranged > as in columns for the following variables: s t o se > > with > s as study/trial identifier > t as 0/1 for control/treatment arm > o as observed outcome in control or treatment arm > se as standard error of that outcome measure > > I then ran metafor as: > rma(yi=o, sei=se, mods=~s+t-1, method="REML") > > for random effects, and REML replaced by FE for fixed effects. > > Is that the correct way to run the arm-based pairwise meta-analysis? > > Shouldn't I be able to obtain similar results with LME for random-effects > by using the command: lme(o~s+t-1, random=~t-1 | s, weights=(~ se^2)) > > and for fixed-effects with: > lm(formula = o ~ s + t - 1, weights = 1/se.o^2) > > > For the trial-based pairwise meta-analysis I used: > data arranged as: > s e se > > with: > s study > e effect > se standard error > > and commands: > rma(yi=e, sei=se, method="REML") > > or > > lme(e~1, random=~1 | s, weights=(~ se.e^2)) > > for random-effects, while for fixed-effects: > rma(yi=e, sei=se.e, method="FE") > lm(e~1, weights = 1/se.e^2) > > Does that make sense? > > > Many thanks for any comment/advice on this matter. > Best regards, > Angelo ______________________________________________ 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.
