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

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 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 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

control" -

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

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 pa? 2023 o 15:46 Viechtbauer, Wolfgang (NP)
<mailto:mailto:wolfgang.viechtbauer at maastrichtuniversity.nl>

napisa?(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 at 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 at dewey.myzen.co.uk>
Cc: Andrzej Andrzej <mailto:mailto:xaf3111.developers at gmail.com>; R

Special

Interest

Group for

Meta-Analysis <mailto:mailto:r-sig-meta-analysis at 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

	[[alternative HTML version deleted]]

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

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 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

control" -

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

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 pa? 2023 o 15:46 Viechtbauer, Wolfgang (NP)
<mailto:mailto:wolfgang.viechtbauer at maastrichtuniversity.nl>

napisa?(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 at 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 at dewey.myzen.co.uk>
Cc: Andrzej Andrzej <mailto:mailto:xaf3111.developers at gmail.com>; R

Special

Interest

Group for

Meta-Analysis <mailto:mailto:r-sig-meta-analysis at 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

      [[alternative HTML version deleted]]

--
Michael

-----Original Message-----
From: Andrzej Andrzej <xaf3111.developers at gmail.com>
Sent: Wednesday, November 1, 2023 19:39
To: Michael Dewey <lists at dewey.myzen.co.uk>
Cc: R Special Interest Group for Meta-Analysis <r-sig-meta-analysis at r-
project.org>; Viechtbauer, Wolfgang (NP)
<wolfgang.viechtbauer at 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

?r., 1 lis 2023 o 14:50 Michael Dewey <mailto:lists at dewey.myzen.co.uk>
napisa?(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 pa? 2023 o 19:06 Viechtbauer, Wolfgang (NP) <
mailto: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 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 at gmail.com>
Sent: Monday, October 30, 2023 18:58
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

control" -

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: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:mailto:xaf3111.developers at gmail.com>
Sent: Monday, October 30, 2023 17:09
To: Viechtbauer, Wolfgang (NP)

<mailto: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

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 pa? 2023 o 15:46 Viechtbauer, Wolfgang (NP)
<mailto:mailto:mailto:mailto:wolfgang.viechtbauer at maastrichtuniversity.nl>

napisa?(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 at 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 at dewey.myzen.co.uk>
Cc: Andrzej Andrzej

<mailto:mailto:mailto:mailto:xaf3111.developers at gmail.com>; R

Special

Interest

Group for

Meta-Analysis <mailto:mailto:mailto:mailto:r-sig-meta-analysis at 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