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--- tips:multiple_imputation_with_mice_and_metafor [2021/10/22 14:45] – Wolfgang Viechtbauer
+++ tips:multiple_imputation_with_mice_and_metafor [2022/08/03 11:35] (current) – Wolfgang Viechtbauer
@@ Line 1: / Line 1: @@
 ===== Multiple Imputation with the mice and metafor Packages =====
-Meta-analytic data often looks like Swiss cheese -- there are lots of holes in it! For example, due to missing information, it may not be possible to compute the effect size estimates (or the corresponding sampling variances) for some of the studies. Similarly, it may not be possible to code certain moderator variables for some studies. Below, I illustrate how to use multiple imputation as a possible way to deal with the latter issue, using the [[https://cran.r-project.org/package=mice|mice]] package in combination with metafor.
+A meta-analytic dataset often looks like Swiss cheese -- there are lots of holes in it! For example, due to missing information, it may not be possible to compute the effect size estimates (or the corresponding sampling variances) for some of the studies. Similarly, it may not be possible to code certain moderator variables for some studies. Below, I illustrate how to use multiple imputation as a possible way to deal with the latter issue, using the [[https://cran.r-project.org/package=mice|mice]] package in combination with metafor.
 ==== Data Preparation ====
@@ Line 80: / Line 80: @@
 </code>
 <code output>
   1  3
   5  2
 </code>
 So, for 41 studies, the data are complete, but 5 studies have one missing value, while 2 studies have three missing values. Therefore, when fitting a meta-regression model with all moderators of interest included simultaneously, only the 41 studies with complete data will be included in the analysis.
@@ Line 114: / Line 114: @@
 Model Results:
           estimate      se     zval    pval    ci.lb   ci.ub
 intrcpt     0.2689  0.2154   1.2484  0.2119  -0.1533  0.6910
 length      0.0072  0.0078   0.9240  0.3555  -0.0081  0.0225
 wic        -0.0472  0.1097  -0.4308  0.6666  -0.2622  0.1677
 feedback    0.0677  0.1080   0.6265  0.5310  -0.1440  0.2793
 info       -0.2233  0.2029  -1.1006  0.2711  -0.6210  0.1744
 pers       -0.1137  0.1898  -0.5992  0.5490  -0.4857  0.2582
 imag        0.4106  0.1847   2.2233  0.0262   0.0486  0.7726  *
 meta        0.2010  0.1742   1.1537  0.2486  -0.1404  0.5424
 ---
@@ Line 192: / Line 192: @@
 </code>
 <code output>
 yi       vi   length      wic feedback     info     pers     imag     meta
 ""       ""    "pmm" "logreg" "logreg" "logreg" "logreg"       "" "logreg"
 </code>