Abstract
Objective
To assess how different imputation methods used to account for missing variance data
in primary studies influence tests of heterogeneity and pooled results from a meta-analysis
with continuous outcomes.
Study Design and Setting
Point and variance estimates for changes in serum creatinine, glomerular filtration
rate, systolic blood pressure, and diastolic blood pressure were variably reported
among 48 primary longitudinal studies of living kidney donors (71%–78% of point estimates
were reported, 8%–13% of variance data were reported). We compared the results of
meta-analysis, which either were restricted to available data or used four methods
to impute missing variance data. These methods used reported P-values, reported nonparametric summaries, results from other similar studies using
multiple imputation, or results from estimated correlation coefficients.
Results
Significant heterogeneity was present in all four outcomes regardless of the imputation
methods applied. The random effects point estimates and 95% confidence intervals varied
little across imputation methods, and the differences were not clinically significant.
Conclusions
Different methods to impute the variance data in the primary studies did not alter
the conclusions from this meta-analysis of continuous outcomes. Such reproducibility
increases confidence in the results. However, as with most meta-analyses, there was
no gold standard of truth, and results must be interpreted judiciously. The generalization
of these findings to other meta-analyses, which differ in outcomes, missing data,
or between-study heterogeneity, requires further consideration.
Keywords
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Article info
Publication history
Published online: October 25, 2006
Accepted:
June 2,
2006
Identification
Copyright
© 2007 Elsevier Inc. Published by Elsevier Inc. All rights reserved.
