[R-meta] Clarifications about pooling of continous outcomes when geometric means are present

-----Original Message-----
From: R-sig-meta-analysis <r-sig-meta-analysis-bounces at r-project.org> On Behalf
Of Alberto Enrico Maraolo via R-sig-meta-analysis
Sent: Tuesday, December 12, 2023 13:19
To: r-sig-meta-analysis at r-project.org
Cc: Alberto Enrico Maraolo <albertomaraolo at mail.com>
Subject: [R-meta] Clarifications about pooling of continous outcomes when
geometric means are present

Dear all,

I would like to have a help if possible on the pooling of continuous data,
specifically Pk/Pd outcomes in the presence of means, medians, and geometric
means.
The goal is to have a (standardized) mean difference between patients with two
different conditions.
Medians under certain assumption can be converted to means and the conv.fivenum
functions serves the purpose.

The problem is the pooling of means with geometric means, since the latter
cannot be re-converted to arithmetic means without raw data.

So, as suggested by the Cochrane Handbook, ?a meta-analysis may be then
performed on the scale of the log-transformed data?; the Handbook describes how
to derive the natural logs from the geometric means and how to compute standard
deviations of the log-transformed data from confidence intervals.
Since ?log-transformed and untransformed data cannot be mixed in a meta-
analysis?, the issue is to go from raw to transformed data for standard means,
and the suggestion is to follow the formulas from the paper of Higgins 2008.

My question is: can I follow the chunks suggested in this page:

https://wviechtb.github.io/metafor/reference/escalc.html?

I mean the section 3a (?Measures for Quantitative Variables?), by resorting to
"CVLN" for the log transformed coefficient of variation and to "SDLN" for the
log transformed standard deviation in each group, in order to have log-
transformed data to be pooled with the natural logs from the geometric means.

If there is some line of code also for faster handling of geometric means in
order to derive natural logs it would be nice as well.

Warm regards,
Alberto

Alberto Enrico Maraolo, MD, MSc (Antimicrobial Stewardship, Evidence Synthesis),
FESCMID

Infectious Diseases Specialist, Member of the Steering Committee of SIMIT (ID
Italian Society)

Cotugno Hospital, AORN dei Colli, Naples, Italy
mail: mailto:albertomaraolo at mail.com

Alberto Enrico Maraolo, MD, MSc (Antimicrobial Stewardship, Evidence Synthesis),
FESCMID

Specialista in Malattie Infettive, Consigliere Nazionale Direttivo SIMIT
(Societ? Italiana di Malattie Infettive e Tropicali)

Dirigente Medico, AORN dei Colli - Ospedale Cotugno, Napoli