[R-meta] Options for generating a forest plot for multilevel subgroup meta-analysis

Mon, Aug 17, 2020 12:16 AM

Dear Mikayla,

Given the data structure you describe, the two variance components in the multilevel model will be hardy distinguishable. I would be cautious in using a multilevel model then. I would personally do one of the following:

1) Select one of the two estimates (based on some a priori rule). Then there is no dependency and you can use standard methods for pooling the estimates.

2) Run the analysis twice, each time including only one of the two. As above.

3) Impute the covariance between the two estimates based on r*SE1*SE2, where r is some assumed correlation and SE1 and SE2 are the standard errors of the (transformed) prevalence estimates. I would then use this covariance in my V matrix and use rma.mv(), but without a multilevel structure (just random effects for the individual estimates). Then one could vary r within a reasonable range and check the sensitivity of the results to the assumed correlation.

You wrote that you used the "arcsine transformation" for your data, but then also "transf.ipft", which is for the Freeman-Tukey double arcsine transformation. The "arcsine transformation" (or more precisely, the arcsine square root transformation) is not the same as the latter. But I guess you indeed used the Freeman-Tukey double arcsine transformation.

However, before using the back-transformation, I would suggest to read:

https://onlinelibrary.wiley.com/doi/abs/10.1002/jrsm.1348

As for showing subgroups with the forest() function from metafor, you will have to do more manual work. See, for example:

http://www.metafor-project.org/doku.php/plots:forest_plot_with_subgroups

Best,
Wolfgang

-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-project.org]
On Behalf Of Mikayla Plishka
Sent: Monday, 17 August, 2020 0:49
To: r-sig-meta-analysis at r-project.org
Subject: [R-meta] Options for generating a forest plot for multilevel
subgroup meta-analysis

Hi there,

I am a masters student in epidemiology at the University of Guelph's
Department of Population Medicine working on my thesis. I am conducting a
meta-analysis of proportions, where prevalence is the outcome of interest. I
am conducting multiple separate meta-analyses, however, each meta-analysis
is relatively small and has less than 15 studies. Some of my meta-analyses
have two prevalence estimates for one study. For instance, one of my
datasets has 10 prevalence estimates for 9 studies (1 study reports 2
prevalence estimates). As a result, I need to do a multi-level meta-analysis
to account for this clustering. I also have subgroups that I would like to
group by. However, I do not need to do the multilevel analysis for the
subgroups as I do not have multiple estimates per study in each of the
subgroups. I mainly need the multilevel analysis for the overall combined
prevalence estimate.

Using the rma.mv function from the metafor package, I was able to calculate
an overall combined prevalence estimate using a multilevel meta-analysis.
However, I am having difficulty finding a good function to generate a forest
plot for the multilevel meta-analysis. My data is transformed via the
arcsine transformation and then back transformed for ease of interpretation.

Previously, in my meta-analyses that were not multilevel, I used forest.meta
from the meta package. I used the results of the metaprop function as the
first argument in the forest.meta function. The metaprop function was great
as I could calculate the appropriate effect estimate, I2 value and
confidence intervals for my data. It also worked very well for the subgroup
analyses. However, to the best of my knowledge the metaprop function does
not work for multilevel meta-analyses.

Since I am therefore unable to use metaprop and forest.meta, I tried to use
the result of the rma.mv function with forest.rma, and back-transform it
within the function.

For example, I used the following code: forest(data, transf=transf.ipft,
targs=list(ni=data$ni)).

The above code gave me the following error "Error in xy.coords(x, y, setLab
= FALSE) : 'x' and 'y' lengths differ". I also tried the same forest
function with transf=transf.ipft.hm instead. This worked and it generated a
forest plot. However, I do not want the harmonic mean, I want the arithmetic
mean.

I did additional research to find a solution. The author of the package
answered someone's question (https://stat.ethz.ch/pipermail/r-help/2010-
December/263286.html) and stated that transf.ipft does not work with that
function and, instead, I have to calculate the back-transformed confidence
intervals manually and use these with the forest.default function as
follows:

forest(dat$pi, ci.lb=ci.lb, ci.ub=ci.ub, ylim=c(-0.5,8), refline=NA,
xlim=c(-.5,1.8), alim=c(0,1), digits=3, xlab="Proportion")

I did this and it worked, however, I was unable to figure out how to show
the subgroups with forest.default.

I also tried the forestplot function from the forestplot package. This
function allows me to enter in all of the data manually, however, I also
could not figure out how to show the subgroups.

I also tried the viz_forest function from the metaviz package, but this
function only takes in the standard error and I could not figure out how to
give it my own confidence intervals that I calculated. I tried adding in
annotate_CI = TRUE to the function, but these confidence intervals are not
correct for my purposes and the standard error bars are still printed on the
plot (instead of the confidence interval bars).

In addition, I would like the heterogeneity values to be printed at the
bottom. The forest.meta function using metaprop gives this to me, but I
cannot figure out how to generate this automatically in any of the other
forest plot functions.

My primary question is, what is the best option to generate a forest plot
for a multilevel meta-analysis with subgroups? Is there a way to use
transf=transf.ipft in forest.rma (as this seems to be the ideal option)?

Any help would be greatly appreciated.

Best,

Mikayla Plishka, Hons. BA.
MSc Candidate Epidemiology
Department of Population Medicine
Rm 2505, Stewart Building (office)
Ontario Veterinary College
Email: plishkam at uoguelph.ca

[R-meta] Options for generating a forest plot for multilevel subgroup meta-analysis

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