[R-meta] "Favours experimental/vaccinated", "Favours control" - Metafor

Andrzej Andrzej x@|3111@deve|oper@ @end|ng |rom gm@||@com
Wed Nov 1 08:55:29 CET 2023 

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 paź 2023 o 19:06 Viechtbauer, Wolfgang (NP) <
wolfgang.viechtbauer using maastrichtuniversity.nl> napisał(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 with
> 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 using gmail.com>
> > Sent: Monday, October 30, 2023 18:58
> > To: Viechtbauer, Wolfgang (NP) <
> wolfgang.viechtbauer using maastrichtuniversity.nl>
> > Cc: R Special Interest Group for Meta-Analysis <r-sig-meta-analysis using r-
> > project.org>
> > Subject: Re: [R-meta] "Favours experimental/vaccinated", "Favours
> control" -
> > Metafor
> >
> > Here is the code for it:
> > log_rr <- log((a/b)/(c/d))
> > kind regards,
> > Andrzej
> >
> > pon., 30 paź 2023 o 18:26 Viechtbauer, Wolfgang (NP)
> > <mailto:wolfgang.viechtbauer using maastrichtuniversity.nl> napisał(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 using gmail.com>
> > > Sent: Monday, October 30, 2023 17:09
> > > To: Viechtbauer, Wolfgang (NP)
> > <mailto:wolfgang.viechtbauer using maastrichtuniversity.nl>
> > > Cc: R Special Interest Group for Meta-Analysis <r-sig-meta-analysis using r-
> > > http://project.org>
> > > Subject: Re: [R-meta] "Favours experimental/vaccinated", "Favours
> control" -
> > > 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
> ratio
> > > therefore indicates that the results of a study favor the vaccinated
> group."
> > > 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
> results
> > > favor the vaccinated group."
> > > Whether log(RR) is negative or positive, the vaccinated group is
> favoured
> > anyway
> > > ?
> > > I do not understand this, please clarify.
> > > best,
> > > Andrzej
> > >
> > > pon., 30 paź 2023 o 15:46 Viechtbauer, Wolfgang (NP)
> > > <mailto:mailto:wolfgang.viechtbauer using maastrichtuniversity.nl>
> napisał(a):
> > > Dear Andrzej,
> > >
> > > In this example, the 2x2 table is of the form as shown here:
> > >
> > > https://wviechtb.github.io/metadat/reference/dat.bcg.html#details-1
> > >
> > > Since a low 'TB positive' count is desirable, a negative log risk ratio
> > > therefore indicates that the results of a study favor the vaccinated
> group.
> > >
> > > 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
> results favor
> > > the vaccinated group.
> > >
> > > So, one really has to understand what is being computed and whether
> positive
> > or
> > > negative values indicate which group is being favored.
> > >
> > > Best,
> > > Wolfgang
> > >
> > > > -----Original Message-----
> > > > From: R-sig-meta-analysis <mailto:mailto
> :r-sig-meta-analysis-bounces using r-
> > project.org>
> > > On Behalf
> > > > Of Andrzej Andrzej via R-sig-meta-analysis
> > > > Sent: Sunday, October 29, 2023 18:25
> > > > To: Michael Dewey <mailto:mailto:lists using dewey.myzen.co.uk>
> > > > Cc: Andrzej Andrzej <mailto:mailto:xaf3111.developers using gmail.com>; R
> Special
> > Interest
> > > Group for
> > > > Meta-Analysis <mailto:mailto:r-sig-meta-analysis using r-project.org>
> > > > Subject: Re: [R-meta] "Favours experimental/vaccinated", "Favours
> control" -
> > > > 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
> experimental")
> > > > 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
> knew
> > > > whetheh high values favoured experimental and so would be on the
> right
> > > > of the plot (the first option) or vice versa"
> > > > Could you please guide me with explanation based on this code and WV
> data,
> > > > 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),
> shade="zebra",
> > > >        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$k+2, c("TB+", "TB-", "TB+", "TB-"),
> cex=0.75,
> > > > font=2)
> > > > text(c(-8.75,-5.25),     res$k+3, c("Vaccinated", "Control"),
> cex=0.75,
> > > > font=2)
> > > >
> > > > 3. "whetheh high values favoured experimental and so would be on the
> right
> > > > of the plot (the first option) or vice versa"
> > > > Could you please explain using that forest plot and bcg.data, where
> are
> > > > those higher values (in which group) so how should I label Log Risk
> Ratio X
> > > > 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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