similar to: refline in forest() {metafor}

Dear Dushanthi, Please keep your e-mails on the R-Help list, where Michael has already given you some excellent advice. As Michael already explained, metafor can handle proportions, but does not have any specific functionality for categorical variables with more than 2 levels (at the moment). So, if it is logical and possible to do so, you could collapse the levels of the categorical outcome to 2

Hello, I am performing a meta-analysis using the metafor package. My data are proportions and I used the Freeman Tukey double arcine (FT) transformation to fit the random effects model. Now I want to create a forest plot with my estimates backtransformed to the original scale of proportions. Can this be done? Regards, Patricia

Hi Dr. Viechtbauer, I'm doing meta-analysis using your package 'metafor'. I used the 'IRFT' to transform the incident rate. But when I tried to back-transform the summary estimates from function rma, I don't know what's the appropriate ti to feed in function transf.iirft. I searched and found your post about using harmonic mean for ni to back-transform the double

Dear all, I am very grateful that Wolfgang Viechtbauer implemented the standardised mean change for dependent groups. I was playing around a bit today, and I am not sure if I understand the "SMCR" procedure correctly. The documentation states that sd1i and sd2i are needed, but it seems to me that SMCR is ignoring sd2i (so Variances are not pooled). Instead, it uses sd1i (pre-test sd),

Hello, Is it possible to have two meta-plots in one graph (not par(mfrow=c(2,1))? But somthing like  library(metafor)  library("igraph")    if (interactive()) {     forest(dat.Treat$RR, ci.lb=dat.Treat$lower, ci.ub=dat.Treat$upper, xlab="Relative Risk",slab=dat.Treat$ID,refline=1)     forest(dat.Control$RR, ci.lb=dat.Control$lower, ci.ub=dat.Control$upper, xlab="Relative

I am using metafor to create forest plots. This code gives me the expected plot (setting x axis tick marks): forest(forest$OR, ci.lb=forest$Low, ci.ub=forest$High, at=log(c(.05, .25, 1, 10)), slab=forest$SNP, atransf=exp) As does this (setting colors): forest(forest$OR, ci.lb=forest$Low, ci.ub=forest$High, col=c(1,2,3), slab=forest$SNP, atransf=exp) But if I try to set both 'at' and

Dear R users, In metafor 1.6-0, the Mantel-Haenszel method is implemented by the rma.mh() function. I have observed that the sum of the weights computed by weights(x) doesn't add to 100% when x is an object of class rma.mh. The consequences of this fact can be clearly seen when a forest diagram is drawn with forest(x), which calls weights(x) (or more precisely, the method weights.rma.mh()

Hi, I am a student using R for my final year project, with the metafor package being particularly helpful. I have been following the steps laid out in the manual 'Conducting Meta-analysis in R with the Metafor package' (Viechtbauer, 2010) and applying it to my own data of infected Anopheles mosquitoes across Africa. In particular, I am attempting to apply a mixed-effect model to my data

Dear users of metafor, I am working on a meta-analysis using the metafor package. I have a excel csv database that I am working with. I am interested in pooling the effect measures for a particular subgroup (European women) in this csv database. I am conducting both sub-group and meta-regression. In subgroup-analyses, I have stratified the database to create a separate csv file just for European

Dear R-experts, The forest.rma() function from the metafor package creates nice forest plots for presenting the results of a meta-analysis. These plots can be annotated for e.g. giving names to the columns. E.g. as in the documentation of the package: data(dat.bcg) ### meta-analysis of the log relative risks using a random-effects model res <- rma(ai=tpos, bi=tneg, ci=cpos, di=cneg,

Dear All, I have been checking the metafor package against R-devel. R CMD check --as-cran metafor yields one note: FOSS licence with BuildVignettes: false Yes, I have 'BuildVignettes: FALSE' in my DESCRIPTION file. I see at http://developer.r-project.org/blosxom.cgi/R-devel/NEWS: Tue, 25 Jun 2013 CHANGES IN R-devel UTILITIES 'R CMD check --as-cran' warns about a false

Dear R-experts, Dear Wolfgang, Weighted model fitting in metafor uses the inverse of the study specific variances as weights. I am wondering if it is possible to specify different weights. In my meta-analysis, there are two types of studies with (intrinsic) differences in their range of sample sizes (which are used to calculate the variances of Fisher's z). I would like to try normalizing

Hi, I would like to do a meta-analysis, i.e., a mixed-effects regression, but I don't seem to get what I want using both the nlme or metafor packages. My question: is there indeed no way to do it? And if so, is there another package I could use? Here are the details: In my meta-analysis I'm comparing different studies that report a measure at time zero and after a certain followup

I'm puzzled by the behaviour of factors in rma models, see example and comments below. I'm sure there's a simple explanation but can't see it... Thanks for any input John Hodgson ------------------------------------- code/selected output ----------------- library(metafor) ## Set up data (from Lenters et al A Meta-analysis of Asbestos and Lung Cancer... ##

Dear sir or madam I want to know how to use "metafor"package do a meta anlysis, forest plot show the results of whole study and results of two subgroups like this the forest plot show in the attachment. looking forword to your reply Regards and Cheers Xudong Liu School of Public Health and Primary Care The Chinese University of Hong Kong Email: liuxudong at cuhk.edu.hk

Hello, I recently started using the metafor package (version 1.6-0) in R (2.15.1, 64-bit Windows 7) and noticed that I was getting slightly different values when I manually calculated the standardized mean difference versus what escalc was giving me. Here''s a very simple example: escalc(measure="SMD", m1i=5,m2i=10,n1i=5,n2i=5,sd1i=1,sd2i=2,vtype="LS") The result

Hi, I have been looking for an R package which allowed to do meta-analysis (both pairwise and network/mixed-treatment) at arm-level rather than at trial-level, the latter being the common way in which meta-analysis is done. By arm-level meta-analysis I mean one that accounts for data provided at the level of the individual arms of each trial and that does not simply derive the difference between

Hello, I am learning to use the metafor package to conduct meta-regression analyses for a systematic review on multidisciplinary care interventions in chronic kidney disease. For the forest plots, I can't figure out how to plot unadjusted and adjusted models on the same plot. From top to bottom, I would like to be able have the unadjusted plot, the multivariate adjusted plot, then each

Hello. A quick question about incorporating variation due to study in the metafor package. I'm working with a particular data set for meta-analysis where some studies have multiple measurements. Others do not. So, let's say the effect I'm looking at is response to two different kinds of drug treatment - let's call their effect sizes T1 and T2. Some studies have multiple

Hello metafor users, I'm using metafor to perform a single-effect summary estimate of the raw proportion of patients experiencing a post-operative complication, and I'm interested in seeing if this proportion differs between the three most commonly used surgical techniques. The software is working as expected, but I would like to double check on the interpretation of my mixed-effect model