[R-meta] OR using metafor: Standard group with many zeros

Dear Nelson,

1) You mean that there are studies where there were 0 patients (not just 0
events, but actually no participants) in one of the two groups? If so, I
don't see how such studies could be included in a meta-analysis that aims
to estimate ORs. Those are essentially one-arm studies, so they don't
provide any information about group differences.

2) help(rma.glmm) explains what nAGQ represents. When using glmer() for
fitting the 'UM.RS' model, one has to use the Laplacian approximation
(i.e., nAGQ=1). You could try using:

control=list(package="GLMMadaptive")

which then uses the GLMMadaptive package, which does allow for using
proper quadrature when fitting the model.

As for the convergence warning - that comes from glmer(). It is quite
quick to flag potential non-convergence, but this may be a false positive.
Again, you could compare results when using GLMMadaptive.

Best,
Wolfgang

-----Original Message-----
From: R-sig-meta-analysis [mailto:

r-sig-meta-analysis-bounces at r-project.org]

On Behalf Of Nelson Ndegwa
Sent: Saturday, 18 April, 2020 17:09
To: r-sig-meta-analysis at r-project.org
Cc: wvb at wvbauer.com
Subject: [R-meta] OR using metafor: Standard group with many zeros

Hi,

I am interested in calculating an OR for a developing some outcome after
new-treatment compared to standard treatment. I have proportions data,

with

19 studies.

I ran a Random-Effects Model (code below, data attached) with the model
type: Unconditional Model with Random Study Effects as follows, then
exponentiated the coefficient for an OR:

Two questions:

1. I noticed there were quite a few zeros in the standard group (about

8/19

studies) . This is sometimes because there were no standard patients in

that

study. 0 patients and 0 events. Should it be a zero or a . for missing
instead? what is the best way to handle this situation? I tested running
above code by using both TRUE or FALSE in the drop00 option, but the

results

didn't change.
2. I got results but also this message which am not sure what it means for
the results I have got, are they still valid? "Currently not possible to

fit

RE/ME model='UM.RS' with nAGQ > 1. nAGQ automatically set to 1.Model

failed

to converge with max|grad| = 0.00207718 (tol = 0.001, component 1)"
I shall greatly appreciate your comments/advice.

=========
CODE RUN
=========
res1 <- rma.glmm(measure="OR", ai=treatment_events, n1i=treatment_total,
ci=control_events, n2i=control_total, data=repdat, model="UM.RS")

print(res1, digits=3)

predict(res1, transf=exp, digits=2)