[R-meta] Difference in Proportions Meta-Analysis

[ne gic]

Dear Wolfgang,

Thank you, I greatly appreciate it.

Sincerely,
nelly

On Tue, Jul 7, 2020 at 10:39 PM Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:

> Dear Nelly,
>
> Looks right. And yes, for an analysis with log odds ratios, just change
> measure="RR" to measure="OR". The back-transformation is still exp.
>
> Best,
> Wolfgang
>
> >-----Original Message-----
> >From: ne gic [mailto:negic4 using gmail.com]
> >Sent: Monday, 06 July, 2020 17:08
> >To: Viechtbauer, Wolfgang (SP)
> >Cc: r-sig-meta-analysis using r-project.org
> >Subject: Re: [R-meta] Difference in Proportions Meta-Analysis
> >
> >Dear Wolfgang & list,
> >
> >This is what I have done, so I cross check with you again.
> >
> >Following your suggestion, I computed the number events for both groups
> with
> >p*n and called the resulting event counts ai and ci. The n1i and n2i were
> >already available from the risk tables.
> >
> >To get a RR as the measure I did this: Is this correct?
> >
> >mydat <- escalc(measure="RR", ai = ai, n1i = n1i, ci = ci, n2i = n2i,
> data =
> >bmi_gc, slab=paste(author))
> >
> >res <- rma(yi, vi, data=mydat, method="REML")
> >
> >forest(res, digits = 2, atransf = exp )
> >
> >### add text with Q-value, dfs, p-value, and I^2 statistic
> >text(-1, -1, pos=4, cex=0.8, bquote(paste(" (Q = ",
> >                                            .(formatC(res$QE, digits=2,
> >format="f")), ", df = ", .(res$k - res$p),
> >                                            ", p = ", .(formatC(res$QEp,
> >digits=2, format="f")), "; ", I^2, " = ",
> >                                            .(formatC(res$I2, digits=1,
> >format="f")), "%)")))
> >
> >To get an OR as the measure, does it suffice to just change the RR to OR
> or
> >do I need to change e.g. atransf to something else downstream of the code?
> >especially the back-transformation.
> >
> >Apologies if this is trivial or repetitive, but I really haven't got the
> >hang of this yet.
> >
> >Sincerely,
> >nelly
> >
> >On Wed, Jun 10, 2020 at 4:13 PM Viechtbauer, Wolfgang (SP)
> ><wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
> >Hi Nelly,
> >
> >Don't you have the sample size on which the proportions are based? If not,
> >the standard error of a proportion is SE[p] = sqrt[p(1-p)/n], so given p
> and
> >SE[p], one can easily recover the sample size with n = p(1-p) / SE^2. Do
> >this for both groups. Call the resulting sample sizes n1i and n2i. Also
> >compute the number events with p*n. Do this for both groups. Call the
> >resulting event counts ai and ci. Now you have everything to use
> >escalc(measure="OR", ai, n1i, ci, n2i).
> >
> >Best,
> >Wolfgang
> >
> >>-----Original Message-----
> >>From: ne gic [mailto:negic4 using gmail.com]
> >>Sent: Wednesday, 10 June, 2020 13:13
> >>To: Viechtbauer, Wolfgang (SP)
> >>Cc: r-sig-meta-analysis using r-project.org
> >>Subject: Re: [R-meta] Difference in Proportions Meta-Analysis
> >>
> >>Dear Wolfgang,
> >>
> >>I have taken time to read the entire escalc documentation
> >>(https://wviechtb.github.io/metafor/reference/escalc.html ) and I am
> sorry
> >>to say I don't understand how I can do that in a single step with
> >>escalc(measure="OR", ...) as you previously mentioned. I would like to do
> >>this correctly following your suggestion so as to minimize any errors as
> I
> >>redo steps that can be done in fewer steps.
> >>
> >>For each of my two groups I have proportion and standard error. Could you
> >>kindly show me how I can do this using one of the available datasets
> >please?
> >>
> >>Sincerely,
> >>nelly
> >>
> >>On Mon, Jun 8, 2020 at 6:48 PM Viechtbauer, Wolfgang (SP)
> >><wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
> >>No, this is not correct. You should not apply the logit-transformation to
> >>the difference and the SE is not correct either. You should apply the
> >logit-
> >>transformation to the two proportions individually, compute the correct
> SE
> >>of the logit-transformed proportions, take the difference between the two
> >>logit-transformed proportions, and then you can use the equation to get
> the
> >>SE of this difference. But all of this can just be done in a single step
> >>with escalc(measure="OR", ...). As I mentioned, the difference between
> two
> >>logit-transformed proportions is the log odds ratio.
> >>
> >>Best,
> >>Wolfgang
>

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