[R-meta] OR using metafor: Standard group with many zeros
Viechtbauer, Wolfgang (SP)
wo||g@ng@v|echtb@uer @end|ng |rom m@@@tr|chtun|ver@|ty@n|
Sat Apr 18 18:36:42 CEST 2020
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:
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.
>From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-project.org]
>On Behalf Of Nelson Ndegwa
>Sent: Saturday, 18 April, 2020 17:09
>To: r-sig-meta-analysis using r-project.org
>Cc: wvb using wvbauer.com
>Subject: [R-meta] OR using metafor: Standard group with many zeros
>I am interested in calculating an OR for a developing some outcome after
>new-treatment compared to standard treatment. I have proportions data, with
>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:
>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
>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.
>res1 <- rma.glmm(measure="OR", ai=treatment_events, n1i=treatment_total,
>ci=control_events, n2i=control_total, data=repdat, model="UM.RS")
>predict(res1, transf=exp, digits=2)
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