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

[Viechtbauer, Wolfgang (NP)]

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 <xaf3111.developers using gmail.com>
> Sent: Monday, October 30, 2023 17:09
> 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
>
> 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: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: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:lists using dewey.myzen.co.uk>
> > Cc: Andrzej Andrzej <mailto:xaf3111.developers using gmail.com>; R Special Interest
> Group for
> > Meta-Analysis <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