similar to: "metafor" package, proportions: single groups wrt to a categorical dependent variable‏

Dear all, I am using R version 2.15.0 and 'metafor' package version 1.6-0. Can this version of the package handle proportions from a categorical dependent variable for single studies?If so how do I set up my dataframe for the raw data from different studies? Also how do I give inputs, specially xi, mi (or ni) to the function escalc()? Thanks,Dushanthi [[alternative HTML

Hello, I experimented the Metafor and Meta packages in the scope of replacing Excel for meta-analysis. I performed the first working example provided in Michael Borenstein's book "Introduction to Meta-Analysis" with Excel, Metafor and Meta. The numbers given by my spreadsheet, which I validated from Borenstein's book, conrespond quite closely to those given by Meta, but

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 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

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

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

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 R-experts! In a meta-analysis (metafor) I would like to assess the effect of two categorical covariates (A & B) whereas they both have 4 levels. Is my understanding correct that this would require to dummy-code (0,1) each level of each covariate (A & B)? However I am interested in the main-effects and the interaction of these two covariates and the dummy-coding would only allow to

Dear R-experts! I am working on some meta-analysis using the metafor package. I would like to extract values of the confidence intervals of the effect sizes of the single studies from an rma object. Those values are printed out when plotting a forest plot using the forest function on the rma object, however I was not able to locate them. Many thanks for your help! Jokel [[alternative HTML

Hi I have tryed to do the meta-regression in metafor package, but I would like to get the standardized coefficients for each variable, however in command:   Ø  res<-rma.uni (yi, vi, method="REML", mods=~cota+DL+uso+gadiente+idade, data= turbidez)   I just have the coefficients no standardized (estimate) of the multiple regression. What I need to do? Thanks Fernanda Melo

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 everyone, I'm using the metafor package to meta-analyze a set of proportions. This is working really well for the raw proportions, but is there a way to back-transform the arcsine transformed proportions in the rma or forest functions with the atransf option? The estimates and CIs for the transformed proportions need to be back-transformed to be the sin of the estimate squared.

Hello all, I am using forest.rma to plot a random effects model meta-analysis. I noticed that refline sets a vertical line indicating the null hypothesis. Is there a way to draw another vertical line, possibly dashed, centered on the summary estimate? Prof. Viechtbauer, if you happen to read this, I'd like to thank you for making an excellent package. I have been using the metafor package

Dear R Users, Do you know of an existing function that allows the production of sensitivity and specificity forest plots? See the following for an example:

Dear Wolfgang Viechtbauer and R users, I have few questions regarding the development of the package 'metafor. As you suggested , I post to the R-help mailing list. I read you're planning an extension of this method to the multivariate case. I think it would be a useful tool. I'm currently performing some analyses with R on multiple outcomes, using the Stata command mvmeta to get

A new package is now available via CRAN, called metafor. The metafor package consists of a collection of functions for conducting meta-analyses in R. Fixed- and random-effects models (with and without moderators) can be fitted via the general linear (mixed-effects) model. For 2x2 table data, the Mantel-Haenszel and Peto's method are also implemented. The package also provides various plot

Hello, Great to see the new metafor package for meta-analysis. I would like to perform a meta-analysis in which I initially calculate the intercept of the model with a nested random-effects structure. In lme, this would be model<- lme(v3~1, random=~1|species/study, weights = varFixed(~Wt), method = "REML") where multiple effects sizes are measured for some studies and more than

Hello,   I would like to do a meta-analysis with the package « metafor ». Ideally I would like to use a mixed model because I’m interested to see the effect of some moderators. But the data set I managed to collect from literature presents two limits.   -         Firstly, for each observation, I have means for a treatment and for a control, but I don’t always have corresponding standard

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),