search for: sd1i

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), as suggested by Becker 1988. Is that correct? Thank you all very much for your time and help, Markus [[alternative HTML version deleted]]

...age (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 in Cooper et al (2009)*, I get an SMD of -2.85625 and a variance of 0.870205. The formula given in Cooper et al fo...

...ues, 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 EFFECT ### US...

?Dear All I have some data expressed in geometric means and 95% confidence intervals. Can I code them in metafor as: rma(m1i=geometric mean 1, m2i=geometric mean 2, sd1i=geometric mean 1 CI /3.92, sd2i=geometric mean 2 CI/3.92.......etc, measure="MD") All of the studies use geometric means. Thanks! Edward ---------------------------- [[alternative HTML version deleted]]

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

I have data that looks like this: > df1 group id 1 red A 2 red B 3 red C 4 blue D 5 blue E 6 blue F I want a list of the groups containing vectors with the ids. I am avoiding subset(), as it is only recommended for interactive use. Here's what I have so far: df1 <- data.frame(group=c("red", "red", "red", "blue",