1) |
Complete-case meta-analysis |
Omits incompletely reported effect
sizes due to which grand mean estimates are expected to exhibit lower
precision, i.e. larger confidence intervals. |
Missing values are not
MCAR. |
2) |
Unweighted meta-analysis |
Assigns equal weights to all effect
sizes (with reported SSs), disregarding the differences in their
precision. |
Effect sizes are related to effect size
precision. |
3) |
Sample-size-weighted meta-analysis |
Calculates approximate effect
size weights (eqn 1). Not applicable for Hedges’ d, whose
calculation is based on SSs (see Supplement S3). |
Effect sizes are
related to the unaccounted SDs in the log response ratio and Hedges’
d. |
Imputation of missing values |
Imputation of missing values |
Imputation
of missing values |
|
4) |
Mean value imputation |
Fills missing values with the mean of the
reported ones and thereby keeps the weights of the completely reported
effect sizes. |
Missing values are outside the range of the reported
values and/or not MCAR. |
5) |
Median value imputation |
Fills missing values with the median of
the reported ones and might be more suitable than mean value imputation
if SDs or SSs follow a skewed distribution. |
Missing values are outside
the range of the reported values and/or not MCAR. |
Multivariate imputation by chained equations
(with the R-package used)
|
Multivariate imputation by chained equations
(with the R-package used)
|
The following imputation techniques are applied multiple times to yield
separate imputed data sets with separate grand mean estimates which are
pooled to obtain meta-analysis estimates that incorporate the
uncertainty in the imputed values (illustrated in Fig. 2). Thereby, SDs
and SSs with missing values were treated as dependent variables. SDs and
SSs with complete data as well as mean values and correlation
coefficients were treated as predictor variables.
|
The following imputation techniques are applied multiple times to yield
separate imputed data sets with separate grand mean estimates which are
pooled to obtain meta-analysis estimates that incorporate the
uncertainty in the imputed values (illustrated in Fig. 2). Thereby, SDs
and SSs with missing values were treated as dependent variables. SDs and
SSs with complete data as well as mean values and correlation
coefficients were treated as predictor variables.
|
6) |
mice: Random sample |
Fills missing values via randomly
selecting one of the reported ones. |
Missing values are outside the
range of the reported values and/or not MCAR. |
7) |
mice: Linear regression |
Fills missing values with
predictions that are obtained from linear models. |
Missing values are
MNAR. |
8) |
mice: Predictive mean matching |
Estimates linear models and
fills missing values with those reported values that are closest to the
predictions. Imputed values are thereby restricted to a subset of the
reported ones. |
Missing values are outside the range of the reported
values and/or MNAR. |
9) |
mice: Classification and regression trees |
Implements a
machine-learning algorithm that seeks cutting points in the set of
supplied predictor variables in order to divide the meta-analysis
dataset into homogenous subsamples. Fills missing values with random
samples from the reported values that are assigned to the same subgroup
as the predictions ones. Like predictive mean matching, imputed values
are thereby restricted to a subset of the reported ones. |
Missing
values are outside the range of the reported values and/or
MNAR |