[R-meta] rma.glmm and relative risk

[David Hajage]

Dear Wolfgang,

Thank you very much for this (so complete) answer.

My request is explained by:
- the desire to compare the results obtained with the one-step and two-step
methods
- the need to provide RR to a collaborator who finds the OR misleading

I fully understand the problems of convergence. What would you recommend in
this situation?
- provide RR after verifying that there is no convergence issues? would
this be acceptable, or subject to questioning at the time of publication?
- explain the possible convergence issues and convince the investigator to
"accept" OR estimations?

Thank you again,
Best,
David


On Wed, Sep 4, 2019 at 11:59 PM Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:

> Hi David,
>
> measure="RR" is not (currently) available in rma.glmm() because that would
> require using a log instead of the logit link and you are (much?) more
> likely to run into convergence problems when doing logistic mixed-effects
> models with a log link.
>
> Just for the fun of it, I just pushed an update to the 'devel' version of
> metafor on GitHub (which you can install as described here:
> https://wviechtb.github.io/metafor/#installation) that adds an
> (undocumented) 'family' argument to rma.glmm() that allows you to change
> the link function (and in principle even the family, but that would be
> really asking for trouble). You still have to use measure="OR" -- which is
> a bit of a misnomer when switching to a log link -- but I didn't want to
> make extensive changes to rma.glmm() right now.
>
> Let's try an example (where the outcome is not rare, because for rare
> outcomes, you won't see much of a difference between RRs and ORs). I'll
> start with the analysis using ORs:
>
> ### copy data into 'dat'
> dat <- dat.linde2005
>
> ### remove studies with no response information and study with no
> responses in either group
> dat <- dat[-c(5,6,26),]
>
> res <- rma(measure="OR", ai=ai, ci=ci, n1i=n1i, n2i=n2i, data=dat)
> res
> predict(res, transf=exp)
>
> res <- rma.glmm(measure="OR", ai=ai, ci=ci, n1i=n1i, n2i=n2i, data=dat,
> model="UM.RS")
> res
> predict(res, transf=exp)
>
> So, using a 'normal-normal' (two-step) model and using a logistic
> random-effects model gives very similar results here. Okay, now let's use a
> 'normal-normal' model with RRs:
>
> res <- rma(measure="RR", ai=ai, ci=ci, n1i=n1i, n2i=n2i, data=dat)
> res
> predict(res, transf=exp)
>
> And now you can do:
>
> res <- rma.glmm(measure="OR", ai=ai, ci=ci, n1i=n1i, n2i=n2i, data=dat,
> model="UM.RS", family=binomial(link="log"))
> res
> predict(res, transf=exp)
>
> Two things to note: The results are indeed close again to those from the
> normal-normal model. But you also get a convergence warning (it does
> converge, but with some troubles).
>
> So, feel free to play around with that. I don't know if I will leave that
> option in (and I'll have to go through all functions that operate on
> 'rma.glmm' model objects to check that nothing else breaks as a result).
> Use at your own risk.
>
> In principle, one could now even move to a risk difference scale, by using
> an identity link:
>
> rma(measure="RD", ai=ai, ci=ci, n1i=n1i, n2i=n2i, data=dat)
>
> But this is even more difficult to fit and fails spectacularly here:
>
> rma.glmm(measure="OR", ai=ai, ci=ci, n1i=n1i, n2i=n2i, data=dat, model="
> UM.RS", family=binomial(link="identity"))
>
> For our beloved BCG dataset, this does work, but I had to switch to the
> UM.FS model:
>
> dat <- dat.bcg
> rma(measure="RD", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat)
> rma.glmm(measure="OR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat,
> model="UM.FS", family=binomial(link="identity"))
>
> Best,
> Wolfgang
>
> -----Original Message-----
> From: R-sig-meta-analysis [mailto:
> r-sig-meta-analysis-bounces using r-project.org] On Behalf Of David Hajage
> Sent: Wednesday, 04 September, 2019 18:29
> To: r-sig-meta-analysis using r-project.org
> Subject: [R-meta] rma.glmm and relative risk
>
> Dear all,
>
> First, I would like to thank Mr Viechtbauer and all the contributors of the
> metafor package: it is very useful and convenient piece of software.
>
> I have a little question regarding the function ‘rma.glmm’. I have a binary
> outcome, and applied the following one-step model:
> > rma.glmm(measure = "OR",
> +        ai = exp_event, bi = exp_noevent, ci = con_event, di =
> con_noevent,
> +        data = ma, model="UM.RS")
>
> I would prefer to express the results in terms of relative risk. This seems
> possible when applying a two-step approach (because measure = “RR” is an
> option of the function ‘escalc’), but it doesn’t seem possible with
> rma.glmm.
> Is there a reason for that? And is there a workaround within your package?
> (I know that I can directly use glmer, but I would really prefer staying in
> the workflow of metafor package).
>
> Thank you very much for you help,
>
> Best wishes,
> --
> David
>

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