Message-ID: <LO2P265MB365900A816FECDB876551648DC1D9@LO2P265MB3659.GBRP265.PROD.OUTLOOK.COM>
Date: 2022-03-28T10:57:58Z
From: Olina Ngwenya
Subject: [R-meta] Dealing with missing data in bivariate analysis
In-Reply-To: <50d80e6cdac2422b9b05dd52a5cb3fae@UM-MAIL3214.unimaas.nl>
Thank you Wolfgang.
Regards
Olina
-----Original Message-----
From: Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl>
Sent: 28 March 2022 11:06
To: Olina Ngwenya <olina.ngwenya at manchester.ac.uk>; r-sig-meta-analysis at r-project.org
Subject: RE: Dealing with missing data in bivariate analysis
Dear Olina,
Depends a bit on what is missing. If values of predictor/moderator variables are missing, then one possibility is to use some kind of imputation technique. See, for example:
https://www.metafor-project.org/doku.php/tips:multiple_imputation_with_mice_and_metafor
Best,
Wolfgang
>-----Original Message-----
>From: R-sig-meta-analysis
>[mailto:r-sig-meta-analysis-bounces at r-project.org] On Behalf Of Olina
>Ngwenya
>Sent: Monday, 28 March, 2022 11:29
>To: r-sig-meta-analysis at r-project.org
>Subject: [R-meta] Dealing with missing data in bivariate analysis
>
>Dear R-sig-meta-analysts
>
>I have been doing bivariate meta-analysis using rma.mv(), but one of my
>outcomes has missing values and I am getting this message "Rows with
>NAs omitted from model fitting". My question is "Do we have other ways
>of dealing with missing data in meta-analysis instead of discarding rows with missing values".
>
>Thank you
>
>Olina Ngwenya
>Research Assistant
>Centre for Biostatistics | School of Health Sciences | Faculty of
>Biology, Medicine and Health | University of Manchester