[R-meta] Calculating standard errors from confidence interval and weights assigned

This aside, the syntax rma(yi, sei, data) is wrong, since the second
argument to rma() is 'vi', which is for the *sampling variances* (i.e., the
squared standard errors). So, either do rma(yi, sei^2, data) or use the
appropriate argument for passing the standard errors, that is, rma(yi,
sei=sei, data).

In recent versions of metafor, rma() actually tries to check for this
(based on the variable name specified for the 'vi' argument), so syntax
like rma(yi, sei) should yield a warning about this.

Best,
Wolfgang

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

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

Behalf Of Michael Dewey via R-sig-meta-analysis
Sent: Wednesday, 12 July, 2023 16:09
To: R Special Interest Group for Meta-Analysis
Cc: Michael Dewey
Subject: Re: [R-meta] Calculating standard errors from confidence

interval and

weights assigned

Dear Win

If you fit a random-effects model then the weights become more nearly
equal tending to equality as tau^2 increases. I suspect that may be the
issue here.

Michael

On 12/07/2023 05:36, Win Thu via R-sig-meta-analysis wrote:

Hello,

I would like to seek your opinions on calculating standard error (sei)
from confidence intervals, and weights assigned to each study in a
random effects model.
To include in rma function*rma(yi, sei, data)*, I used the following
formula to calculate sei

*sei <- [log(CI upper limit) - log(CI lower limit)] / 3.92 *
For yi, it will be log(relative risk).

Then, I have the following weights for each study, which I am not very
confident as the studies with larger sample sizes (cohort studies) were
given reduced weights compared to smaller studies (case-control).
I need to combine both studies as there are limited studies on the

topic.

I am grateful for your feedback.

Untitled.png
Thank you,
Win