Exploration of simulation results
A summary of the findings regarding the effects of different options to handle missing SDs and/or SS in meta-analysis data sets are listed in Table 2. As a general observation, the deviation introduced by the omission of studies with missing SDs and/or SSs (with regard to fully informed weighted analyses) mostly exceeded the deviation from all other options to treat those missing data. Unweighted analysis yielded grand means and confidence intervals similar to fully informed weighted analyses except for the case of a correlation between effect sizes and effect size precision. The same holds for the sample-size weighted analysis. Imputing missing data introduced the least deviation in the log response ratio dataset, followed by the correlation coefficient dataset and the strongest deviation in the Hedges’ d dataset. Missing SDs introduced larger deviations than missing SSs with regard to fully informed weighted analyses. Imputing data missing not at random (MNAR) in the Hedges’ d dataset lead to deviations that are similar to those from the omission of studies with missing SDs and/or SSs.
Compared to all other imputation methods, mean, median and random sample imputation yielded the largest deviation in grand mean estimate and Bayes predictive mean matching yielded the largest increase in the confidence interval. Imputation via bootstrap expectation maximization and additive regression and bootstrap predictive mean matching frequently failed above a threshold of ca. 60% of missing data.