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. [image: Untitled.png] Thank you, Win -------------- next part -------------- An HTML attachment was scrubbed... URL: <https://stat.ethz.ch/pipermail/r-sig-meta-analysis/attachments/20230712/de959cc5/attachment.html> -------------- next part -------------- A non-text attachment was scrubbed... Name: Untitled.png Type: image/png Size: 8207 bytes Desc: not available URL: <https://stat.ethz.ch/pipermail/r-sig-meta-analysis/attachments/20230712/de959cc5/attachment.png>
[R-meta] Calculating standard errors from confidence interval and weights assigned
4 messages · Michael Dewey, Wolfgang Viechtbauer, Win Thu
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 <http://www.avg.com/email-signature?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=emailclient> Virus-free.www.avg.com <http://www.avg.com/email-signature?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=emailclient> <#DAB4FAD8-2DD7-40BB-A1B8-4E2AA1F9FDF2>
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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
Dear Michal - thank you for the feedback. Dear Wolgang - thank you for the correction about syntax. I used the correct one rma(yi, sei=sei, data) in the analysis and was unsure about the weights assigned to each study. Thank you, Win On Thu, Jul 13, 2023 at 2:35?AM Viechtbauer, Wolfgang (NP) via
R-sig-meta-analysis <r-sig-meta-analysis at r-project.org> wrote:
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
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