search for: escalc

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 is: yi vi 1 -2.854599 0.8074367 However, if I calculate this manually using the pooled standard deviation formula given i...

...ase. 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 women from the original database and conducted the following: women_west<-read.csv("women_west.csv") print(women_west) dat<-escalc(measure="ZCOR",ri=Pearson,ni=N,data=women_west,append=TRUE) res<-rma(yi,vi,data=dat) is.factor(dat$year) forest(res,transf=transf.ztor) In meta-regression, I used the original database, but used categorical moderators for sex (=women), and ethnicity (=european) to find the effect spec...

...arms and do the meta-analysis on that. I am not sure metafor can do that, but hopefully someone more experienced on it can clarify that to me. I can see that it can take data in both forms, arm and trial level, but it looks as if the arm-level information would be converted into trial one through escalc and the latter then used for the meta-analysis. Is that right? Many thanks. Angelo -- NIHR Research Methods Training Fellow, Department of Community Based Medicine University of Bristol 25 Belgrave Road Bristol BS8 2AA Tel. 0779 265-6552

...;PR", xi=xi, ni=ni) res forest(res) just to give you an idea. There are also various transformations that can be applied to proportions and depending on your data, it may be more sensible to work with one of these transformations. See "Proportions and Transformations Thereof" under ?escalc. Of course, by doing so, you lose some information. More advanced would be to use an ordinal regression or a multinomial model. Also, regarding your analysis plan -- while it is fine in principle, I hope you are not planning to use the information obtained in this way to draw any conclusions abou...

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:

...es that would bring p-value to the alpha, to be exact)was different than that I got in Comprehensive Meta-Analysis Version 2.0. I wonder why R and CMA got different results. *Below is the R code:* dat=read.table("Your working directory\\Example.csv",header=T,sep=",") transf.ies=escalc(xi=cases,ni=total,measure="PLO",data=dat) #I transform the data using the logit transformation first. In CMA, it also uses the logit transformation. transf.pes=rma(yi,vi,data=transf.ies,method="DL",weighted=TRUE) #Pooling individual effect sizes in the logit scale. ranktest(tran...

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

...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 version deleted]]

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

...to the alpha, to be exact)was different than that I >got >in Comprehensive Meta-Analysis Version 2.0. I wonder why R and CMA got >different results. > >*Below is the R code:* >dat=read.table("Your working directory\\Example.csv",header=T,sep=",") >transf.ies=escalc(xi=cases,ni=total,measure="PLO",data=dat) #I transform >the data using the logit transformation first. In CMA, it also uses the >logit transformation. >transf.pes=rma(yi,vi,data=transf.ies,method="DL",weighted=TRUE) #Pooling >individual effect sizes in the logit scal...

...o numerical issues, but for the random effect, the numbers are considerably different. Unfortunately, I could not find where I made it wrong. I would be grateful if someone would have a look at my calculations. Here are the meta-analysis commands: ### USING METAFOR library(metafor) ( dat<-escalc(m1i=m1i, sd1i=sd1i, n1i=n1i, m2i=m2i, sd2i=sd2i, n2i=n2i, measure="SMD", data=metaData, append=T) ) # COMPUTE EFFECT SIZE ( res<-rma.uni(yi,vi,data=dat,method="HE", level=95) ) ### RANDOM EFFECT ( res<-rma.uni(yi,vi,data=dat,method="FE", level=95) ) ### FIXED...

...Cano et al. 2006 2 42 237 148 629 2 Kerah - Hinzoumbe et al. 2009 10 103 7260 3 215 3 Konate et al. 1994 13 83 5714 48 3609 4 Mwanzia et al. 2011 4 2 8971 0 100 > dat<-escalc(measure="RR", ai = mosq1pos, bi = mosq1neg, ci = mosq2pos, di = mosq2neg, data = mixed, append = TRUE) > res<-rma(yi, vi, mods = ablat, data=dat) > predict(res,transf = exp, addx = TRUE) > preds<-predict(res, transf = exp) > wi<-1/sqrt(dat$vi) > size<-0.5 + 3 *(...

Dear Sir or Madam, I am trying to explore the citation bias by perfroming meta-analysis. I need to plot a forest plot on some other proportions other than the usual effect size OR,RR, RD. I still do not have any idea after searching google and reading relevant books. Can anyone kindly help? Thank you in advance. Best wishes weiwei [[alternative HTML version deleted]]

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

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),

...es that would bring p-value to the alpha, to be exact)was different than that I got in Comprehensive Meta-Analysis Version 2.0. I wonder why R and CMA got different results. *Below is the R code:* dat=read.table("Your working directory\\Example.csv",header=T,sep=",") transf.ies=escalc(xi=cases,ni=total,measure="PLO",data=dat) #I transform the data using the logit transformation first. In CMA, it also uses the logit transformation. transf.pes=rma(yi,vi,data=transf.ies,method="DL",weighted=TRUE) #Pooling individual effect sizes in the logit scale. ranktest(tran...

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

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

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

I am tryin to perform an arcsine transformation on my data containig percentages as the dep. variable. Does anyone have a code that I could use to do that? I am relatively new to R. Thanks for your help! -- View this message in context: http://r.789695.n4.nabble.com/Arcsine-transformation-tp4676706.html Sent from the R help mailing list archive at Nabble.com.