# [R] metafor - code for analysing geometric means

Mon Dec 8 07:25:47 CET 2014

```Dear All

I have tried very hard to work out what to do with putting logged data into metafor; the paper says..
'geometric mean antibody concentrations (GMCs) or opsonophagocytic activity titres (geometric mean titres [GMT]) were calculated with 95% CIs by taking the antilog of the mean of the log concentration or titre transformations.'

Does this look right if I take the reported mean, upper and lower bound of the CI, and the number?

m<-log(mean)
ub<-log(upper bound)
lb<-log(lower bound)
diff<-ub-lb
SE<-diff/3.92
SD<-SE*(sqrt(n))

Then put m, SD and n for each group into metafor as normal.  Or is there a better way?  I am afraid I didn't understand how to do it on a log scale.

Thank you

Edward
----------------------------
Senior Lecturer

Florence Nightingale Faculty of Nursing and Midwifery
King's College London
James Clerk Maxwell Building
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Mobile 07782 374217

________________________________________
From: Viechtbauer Wolfgang (STAT) <wolfgang.viechtbauer at maastrichtuniversity.nl>
Sent: 14 November 2014 10:40
To: Michael Dewey; Purssell, Ed; r-help at r-project.org
Subject: RE: [R] metafor - code for analysing geometric means

With "geometric mean 1 CI /3.92", I assume you mean "(upper bound - lower bound) / 3.92". Two things:

1) That will give you the SE of the mean, not the SD of the observations (which is what you need as input).

2) Probably the CI for the geometric mean was calculated on the log-scale (as Michael hinted at). Check if log(upper bound) and log(lower bound) is (within rounding error) symmetric around log(geometric mean). Then (log(upper bound) - log(lower bound)) / 3.96 * sqrt(n) will give you the SD of the log of the values used to compute the geometric mean. Then you could use log(geometric mean) and that SD as input. But this would give you the difference of the log-transformed geometric means. Not sure if this is what you want to analyze.

Two more articles that may be helpful here:

Friedrich, J. O., Adhikari, N. K., & Beyene, J. (2012). Ratio of geometric means to analyze continuous outcomes in meta-analysis: Comparison to mean differences and ratio of arithmetic means using empiric data and simulation. Statistics in Medicine, 31(17), 1857-1886.

Souverein, O. W., Dullemeijer, C., van 't Veer, P., & van der Voet, H. (2012). Transformations of summary statistics as input in meta-analysis for linear dose-response models on a logarithmic scale: A methodology developed within EURRECA. BMC Medical Research Methodology, 12(57).

Best,
Wolfgang

> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
> On Behalf Of Michael Dewey
> Sent: Thursday, November 13, 2014 12:36
> To: Purssell, Ed; r-help at r-project.org
> Subject: Re: [R] metafor - code for analysing geometric means
>
> On 13/11/2014 11:00, Purssell, Ed wrote:
> > ?Dear All
> >
> > I have some data expressed in geometric means and 95% confidence
> intervals.  Can I code them in metafor as:
> >
> > rma(m1i=geometric mean 1, m2i=geometric mean 2, sd1i=geometric mean 1
> CI /3.92, sd2i=geometric mean 2 CI/3.92.......etc, measure="MD")
>
> Would it not be better to work on the log scale?
>
> > All of the studies use geometric means.
> >
> > Thanks!
> >
> > Edward
>
> --
> Michael
> http://www.dewey.myzen.co.uk

```