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

Viechtbauer, Wolfgang (NP) wo||g@ng@v|echtb@uer @end|ng |rom m@@@tr|chtun|ver@|ty@n|
Thu Nov 2 08:53:29 CET 2023 

If you use summary() on an 'escalc' object, you get the CIs.

Best,
Wolfgang

> -----Original Message-----
> From: Andrzej Andrzej <xaf3111.developers using gmail.com>
> Sent: Wednesday, November 1, 2023 19:39
> To: Michael Dewey <lists using dewey.myzen.co.uk>
> Cc: R Special Interest Group for Meta-Analysis <r-sig-meta-analysis using r-
> project.org>; Viechtbauer, Wolfgang (NP)
> <wolfgang.viechtbauer using maastrichtuniversity.nl>
> Subject: Re: [R-meta] "Favours experimental/vaccinated", "Favours control" -
> Metafor
>
> Hi,
> Is there a way using escalc function to get CI for log RR as well, apart from
> log RR itself ?
> I would like to get RR with CI too. Is it a way to do it and append it all to
> bcg data ?
> bets,
> Andrzej
>
> śr., 1 lis 2023 o 14:50 Michael Dewey <mailto:lists using dewey.myzen.co.uk>
> napisał(a):
> 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 paź 2023 o 19:06 Viechtbauer, Wolfgang (NP) <
> > mailto: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 <mailto:xaf3111.developers using gmail.com>
> >>> Sent: Monday, October 30, 2023 18:58
> >>> 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
> >>>
> >>> 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: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:mailto:xaf3111.developers using gmail.com>
> >>>> Sent: Monday, October 30, 2023 17:09
> >>>> To: Viechtbauer, Wolfgang (NP)
> >>> <mailto: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: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:mailto
> >> :r-sig-meta-analysis-bounces using r-
> >>> http://project.org>
> >>>> On Behalf
> >>>>> Of Andrzej Andrzej via R-sig-meta-analysis
> >>>>> Sent: Sunday, October 29, 2023 18:25
> >>>>> To: Michael Dewey <mailto:mailto:mailto:mailto:lists using dewey.myzen.co.uk>
> >>>>> Cc: Andrzej Andrzej
> <mailto:mailto:mailto:mailto:xaf3111.developers using gmail.com>; R
> >> Special
> >>> Interest
> >>>> Group for
> >>>>> Meta-Analysis <mailto:mailto: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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