Dear Andrzej
I am afraid your post and the calculation shows that you have not
understood what meta-analysis is trying to do. Aggregating the values in
each column and computing log(RR) is not what meta-analysis does.
I think you need to go back to the tutorial material you used when
learning about meta-analysis and re-read it as otherwise you are in
danger of mis-leading yourself again.
Michael
On 01/11/2023 07:55, Andrzej Andrzej via R-sig-meta-analysis wrote:
Hi,
Why when I calculated log(RR) by hand (still using bcg data):
library(metafor)
# Define the data
tpos <- sum(dat.bcg$tpos)
tneg <- sum(dat.bcg$tneg)
cpos <- sum(dat.bcg$cpos)
cneg <- sum(dat.bcg$cneg)
# Calculate RR
RR <- (tpos / (tpos + tneg)) / (cpos / (cpos + cneg))
# Calculate log RR
log_RR <- log(RR)
log((1065 / (1065 + 189999)) / (1510 / (1510 + 164773))) equals to
-0.4880521,
but doing everything like in your tutorial (escalc, rma, forest) ), it
gives me value of -0.71, that is displayed under forest plot in RE Model
row ?
Why is the difference ? What am I missing ?
https://wviechtb.github.io/metafor/reference/forest.rma.html
best regards,
Andrzej
pon., 30 pa? 2023 o 19:06 Viechtbauer, Wolfgang (NP) <
wolfgang.viechtbauer at maastrichtuniversity.nl> napisa?(a):
That would give you the log odds ratio, not risk ratio.
But to shortcut the next 4-5 messages going back and forth:
Once you have figured out the correct equation for the log risk ratio,
then start replacing Group 1 and Group 2 and Outcome 1 and Outcome 2
the appropriate values from the BCG dataset (or whatever dataset you are
working with).
Then think about what a positive value for log(RR) would imply about the
probability of Outcome 1 in Group 1 relative to that of Group 2.
Best,
Wolfgang
-----Original Message-----
From: Andrzej Andrzej <xaf3111.developers at gmail.com>
Sent: Monday, October 30, 2023 18:58
To: Viechtbauer, Wolfgang (NP) <
wolfgang.viechtbauer at maastrichtuniversity.nl>
Cc: R Special Interest Group for Meta-Analysis <r-sig-meta-analysis at r-
project.org>
Subject: Re: [R-meta] "Favours experimental/vaccinated", "Favours
Metafor
Here is the code for it:
log_rr <- log((a/b)/(c/d))
kind regards,
Andrzej
pon., 30 pa? 2023 o 18:26 Viechtbauer, Wolfgang (NP)
<mailto:wolfgang.viechtbauer at maastrichtuniversity.nl> napisa?(a):
At this point, I would like to turn around the question:
How do you think a log risk ratio is computed in a table of the form:
Outcome 1 Outcome 2
Group 1 a b
Group 2 c d
where a, b, c, and d are the counts for the respective cells?
Best,
Wolfgang
-----Original Message-----
From: Andrzej Andrzej <mailto:xaf3111.developers at gmail.com>
Sent: Monday, October 30, 2023 17:09
To: Viechtbauer, Wolfgang (NP)
<mailto:wolfgang.viechtbauer at maastrichtuniversity.nl>
Cc: R Special Interest Group for Meta-Analysis <r-sig-meta-analysis at r-
http://project.org>
Subject: Re: [R-meta] "Favours experimental/vaccinated", "Favours
Metafor
Dear Wolfgang,
Thank you for your kind reply.
How is that ?
1. "Since a low 'TB positive' count is desirable, a negative log risk
therefore indicates that the results of a study favor the vaccinated
This is perfectly clear to me, but this next one, I quote:
2. "In this case, a positive log risk ratio would indicate that the
favor the vaccinated group."
Whether log(RR) is negative or positive, the vaccinated group is
?
I do not understand this, please clarify.
best,
Andrzej
pon., 30 pa? 2023 o 15:46 Viechtbauer, Wolfgang (NP)
<mailto:mailto:wolfgang.viechtbauer at maastrichtuniversity.nl>
therefore indicates that the results of a study favor the vaccinated
But one could just as well have computed the log risk ratios with:
dat <- escalc(measure="RR", ai=cpos, bi=cneg,
ci=tpos, ti=tneg, data=dat)
In this case, a positive log risk ratio would indicate that the
the vaccinated group.
So, one really has to understand what is being computed and whether
negative values indicate which group is being favored.
Best,
Wolfgang
-----Original Message-----
From: R-sig-meta-analysis <mailto:mailto
:r-sig-meta-analysis-bounces at r-
Of Andrzej Andrzej via R-sig-meta-analysis
Sent: Sunday, October 29, 2023 18:25
To: Michael Dewey <mailto:mailto:lists at dewey.myzen.co.uk>
Cc: Andrzej Andrzej <mailto:mailto:xaf3111.developers at gmail.com>; R
Meta-Analysis <mailto:mailto:r-sig-meta-analysis at r-project.org>
Subject: Re: [R-meta] "Favours experimental/vaccinated", "Favours
Metafor
Thank you Michael,
Yes, I do not know how to quote here, but I try:
1. "Do you mean whether to type c("Favors control","Favors
or c("Favors experimental", "Favors control")?"
Yes, exactly I do mean that.
2. "If so the answer is that when you computed the effect size you
whetheh high values favoured experimental and so would be on the
of the plot (the first option) or vice versa"
Could you please guide me with explanation based on this code and WV
please:
https://wviechtb.github.io/metadat/reference/dat.bcg.html
library(metafor)
data(dat.bcg)
dat <- dat.bcg
### calculate log risk ratios and corresponding sampling variances
dat <- escalc(measure="RR", ai=tpos, bi=tneg,
ci=cpos, di=cneg, data=dat,
slab=paste0(author, ", ", year))
### random-effects model
res <- rma(yi, vi, data=dat)
forest(res, addpred=TRUE, xlim=c(-16,7), at=seq(-3,2,by=1),
ilab=cbind(tpos, tneg, cpos, cneg),
ilab.xpos=c(-9.5,-8,-6,-4.5),
cex=0.75, header="Author(s) and Year")
text(c(-9.5,-8,-6,-4.5), res$k+2, c("TB+", "TB-", "TB+", "TB-"),
font=2)
text(c(-8.75,-5.25), res$k+3, c("Vaccinated", "Control"),
font=2)
3. "whetheh high values favoured experimental and so would be on the
of the plot (the first option) or vice versa"
Could you please explain using that forest plot and bcg.data, where
those higher values (in which group) so how should I label Log Risk
axis according to RevMan 5 style with " Favours control" and "Favours
vaccinated" ? I want to understand which way is correct, please.
best regards,
Andrzej
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