Message-ID: <5b41205030294a3dbc34f53e8c9ddec1@upd.unibe.ch>
Date: 2021-05-28T17:21:58Z
From: simeo@@zuercher m@iii@g oii upd@u@ibe@ch
Subject: [R-meta] Question regarding three-level metaanalysis of proportions
In-Reply-To: <7ea5514f-6452-5850-ef0f-06a1088a7569@dewey.myzen.co.uk>
Dear Michael,
thank you very much for the quick reply and help. In this case I will apply this approach.
To answer your question: this was just toy data.
Kind regards
Simeon
________________________________
Von: Michael Dewey <lists at dewey.myzen.co.uk>
Gesendet: Freitag, 28. Mai 2021 14:28
An: Z?rcher, Simeon (UPD); r-sig-meta-analysis at r-project.org
Betreff: Re: [R-meta] Question regarding three-level metaanalysis of proportions
Dear Simeon
In your example you have 100 for ni for all studies. Does that mean the
xi are in fact percentages or is it just a toy example? If the former
then I think that is not correct.
For your substantive problem you could always model the raw proportions
adding 1/2 or some other constant to the zeroes. This would give you
directly interpretable moderator coefficients at the cost of possibly
being a less defensible model.
Michael
On 28/05/2021 10:45, simeon.zuercher at upd.unibe.ch wrote:
> Dear all,
> I?m currently working on a three-level meta-analysis of proportions where we look at neurological complications after some infectious diseases. Effect sizes are dependent since several studies report different effect sizes. It?s a large dataset with over 150 effect sizes that are nested within 60 studies. Some prevalence rates are quite extreme with some reaching the limit (e.g. 100% complications). Based on literature I decided to use a double arcsine transformation.
>
> Example Data:
> study_id <- c(1,1,1,1,1,2,2,3,3,3,4)
> effect_id <- c(1,2,3,4,5,6,7,8,9,10,11)
> xi <- c(2,3,8,10,15,60,80,45,100,100,98)
> ni <- rep(100, 11)
> mod_age <- c(22.5,22.5,22.5,22.5,22.5, 30.5, 30.5,45,45,45,60)
> published <- c(0,0,0,0,0,1,1,1,1,1,0)
>
> code:
> ies <- escalc(xi=xi, ni= ni, data = data, measure = "PFT", add = 0)
> result <- rma.mv(yi, vi, random = ~ 1 | study_id/effect_id, tdist = TRUE,
> data = ies, method = "REML")
> result_pred <- predict(result, transf=transf.ipft.hm, targ=list(ni=data$ni))
> print(result_pred)
>
> While a get plausible results for the pooled effect (which is hopefully correct and quite different from effects by using log or even no transformation), I have some problems with the moderator analysis. After transformation I would like to back-transform to proportions in order to allow a simple interpretation (e.g. percentage point differences between subgroups).
>
> code:
> result <- rma.mv(yi,
> vi,
> random = ~ 1 | study_id/effect_id,
> tdist = TRUE,
> data = ies,
> method = "REML", mods = ~ published)
>
> result_pred <- predict(result, transf=transf.ipft.hm, targ=list(ni=data$ni))
> print(result_pred)
>
> Apparently, back-transformation of the coefficients in moderator analysis is not straight forward and not recommended. I wonder how I can solve this issue. What would be a good way of doing a three-level meta-analysis with proportions (that include extreme values like zero and one)?
>
> I am very grateful for some help with this issue
> Many thanks
> Simeon
>
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>
> UNIVERSIT?RE PSYCHIATRISCHE DIENSTE BERN (UPD) AG
> ZENTRUM PSYCHIATRISCHE REHABILITATION
>
>
> Simeon Z?rcher, Dr. sc. nat., RN
> Wissenschaftlicher Mitarbeiter
> Forschung & Entwicklung
> Murtenstrasse 46
> 3008 Bern
>
> Mailto: simeon.zuercher at upd.unibe.ch
> Tel. +41 (0)79 889 73 54
>
> www.upd.ch
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--
Michael
http://www.dewey.myzen.co.uk/home.html
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