Data from: Using multiple imputation to estimate missing data in meta-regression

1. There is a growing need for scientific synthesis in ecology and
evolution. In many cases, meta-analytic techniques can be used to
complement such synthesis. However, missing data is a serious problem for
any synthetic efforts and can compromise the integrity of meta-analyses in
these and other disciplines. Currently, the prevalence of missing data in
meta-analytic datasets in ecology and the efficacy of different remedies
for this problem have not been adequately quantified. 2. We generated
meta-analytic datasets based on literature reviews of experimental and
observational data and found that missing data were prevalent in
meta-analytic ecological datasets. We then tested the performance of
complete case removal (a widely used method when data are missing) and
multiple imputation (an alternative method for data recovery) and assessed
model bias, precision, and multi-model rankings under a variety of
simulated conditions using published meta-regression datasets. 3. We found
that complete case removal led to biased and imprecise coefficient
estimates and yielded poorly specified models. In contrast, multiple
imputation provided unbiased parameter estimates with only a small loss in
precision. The performance of multiple imputation, however, was dependent
on the type of data missing. It performed best when missing values were
weighting variables, but performance was mixed when missing values were
predictor variables. Multiple imputation performed poorly when imputing
raw data which was then used to calculate effect size and the weighting
variable. 4. We conclude that complete case removal should not be used in
meta-regression, and that multiple imputation has the potential to be an
indispensable tool for meta-regression in ecology and evolution. However,
we recommend that users assess the performance of multiple imputation by
simulating missing data on a subset of their data before implementing it
to recover actual missing data.