Imputing variance estimates do not alter the conclusions of a meta-analysis with continuous outcomes: a case study of changes in renal function after living kidney donation

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. [Copyright &y& Elsevier]