# [R-meta] Implementation of the Inverse variance heterogeneity model

Viechtbauer Wolfgang (SP) wolfgang.viechtbauer at maastrichtuniversity.nl
Wed Nov 29 20:42:27 CET 2017

```Dear Dirk,

What Doi et al. describe are RE models with different weights than the default ones.

"AMhet" uses unit weights. The possibility to fit this model was implemented in metafor since its first release in 2009. "IVhet" uses inverse sampling variance weights. The possibility to fit this model was implemented in version 1.9-3 in 2014.

Using the example from Doi et al. (2015):

##############################

library(metafor)

dat <- get(data(dat.li2007))
dat <- dat[order(dat\$study),]
rownames(dat) <- 1:nrow(dat)
dat <- escalc(measure="OR", ai=ai, n1i=n1i, ci=ci, n2i=n2i, data=dat, subset=-c(19,21,22))

### standard RE model
res <- rma(yi, vi, data=dat, method="DL")
predict(res, transf=exp, digits=2)

### RE model with 1/vi weights ("IVhet")
res <- rma(yi, vi, data=dat, method="DL", weights=1/vi)
predict(res, transf=exp, digits=2)

### RE model with unit weights ("AMhet")
res <- rma(yi, vi, data=dat, method="DL", weights=1)
predict(res, transf=exp, digits=2)

##############################

The results are exactly those reported on 135: "When the meta-analytic estimates were computed using the three methods, they were most extreme with the AMhet estimator (OR 0.44; 95% CI 0.29-0.66), less extreme with the RE estimator (OR 0.71; 95% CI 0.57-0.89) and most conservative with the IVhet estimator (OR 1.01; 95% CI 0.71-1.46)."

The idea to fit a RE model with inverse sampling variance weights was actually already described in:

Henmi, M., & Copas, J. B. (2010). Confidence intervals for random effects meta-analysis and robustness to publication bias. Statistics in Medicine, 29(29), 2969-2983.

Best,
Wolfgang

-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-project.org] On Behalf Of dirk.richter at upd.unibe.ch
Sent: Wednesday, 29 November, 2017 17:22
To: r-sig-meta-analysis at r-project.org
Subject: [R-meta] Implementation of the Inverse variance heterogeneity model

Dear R meta-analysis group,

I was wondering whether there are any plans to implement the Inverse variance heterogeneity model (by Doi et al., see reference below) into R MA packages or whether this has been done recently (although I couldn't find anything on the Web). While the authors of this model have provided with MetaXL a free software that allows to run such an analysis, I would be happy to have it connected to or implemented into R to have the chance to run meta-regressions based on this approach. Currently, there is a only a connection to Stata for meta-regressions.

Reference

SA Doi et al. Advances in the meta-analysis of heterogeneous clinical trials I: The inverse variance heterogeneity model. Contemp Clin Trials. 2015 Nov;45(Pt A):130-8. doi: 10.1016/j.cct.2015.05.009

Thanks,
Dirk Richter

UNIVERSITÄRE PSYCHIATRISCHE DIENSTE BERN (UPD) AG
DIREKTION PSYCHIATRISCHE REHABILITATION

Dirk Richter, Dr. phil. habil.
Leiter Forschung und Entwicklung
Murtenstrasse 46
CH-3008 Bern
Tel. +41 31 632 4707
Mobil + 41 76 717 5220
E-Mail: dirk.richter at upd.unibe.ch
https://www.upd.ch/forschung/psychiatrische-rehabilitation/

University of Bern Psychiatric Services
Center for Psychiatric Rehabilitation
Dirk Richter, Dr. phil., PhD
Head of Research and Development
Murtenstrasse 46
CH-3008 Bern
Switzerland
Phone +41 31 632 4707
Mobile Phone +41 76 717 5220

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