Consequences of multiple imputation of missing standard deviations and sample sizes in meta‐analysis

Stephan Kambach · Helge Bruelheide · Katharina Gerstner · Jessica Gurevitch · Michael Beckmann · Ralf Seppelt

78 citations0/0 files storedEcology and Evolution

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Meta-analysis and systematic reviews Statistical Methods and Bayesian Inference Statistical Methods in Epidemiology

Abstract

Abstract Meta‐analyses often encounter studies with incompletely reported variance measures (e.g., standard deviation values) or sample sizes, both needed to conduct weighted meta‐analyses. Here, we first present a systematic literature survey on the frequency and treatment of missing data in published ecological meta‐analyses showing that the majority of meta‐analyses encountered incompletely reported studies. We then simulated meta‐analysis data sets to investigate the performance of 14 options to treat or impute missing SDs and/or SSs . Performance was thereby assessed using results from fully informed weighted analyses on (hypothetically) complete data sets. We show that the omission of incompletely reported studies is not a viable solution. Unweighted and sample size‐based variance approximation can yield unbiased grand means if effect sizes are independent of their corresponding SD s and SSs . The performance of different imputation methods depends on the structure of the meta‐analysis data set, especially in the case of correlated effect sizes and standard deviations or sample sizes. In a best‐case scenario, which assumes that SDs and/or SSs are both missing at random and are unrelated to effect sizes, our simulations show that the imputation of up to 90% of missing data still yields grand means and confidence intervals that are similar to those obtained with fully informed weighted analyses. We conclude that multiple imputation of missing variance measures and sample sizes could help overcome the problem of incompletely reported primary studies, not only in the field of ecological meta‐analyses. Still, caution must be exercised in consideration of potential correlations and pattern of missingness.

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References

2010Conducting Meta-Analyses in R with the metafor Package 1979The file drawer problem and tolerance for null results.2018Meta-analysis and the science of research synthesis 2005Imputing missing standard deviations in meta-analyses can provide accurate results 2017Meta-evaluation of meta-analysis: ten appraisal questions for biologists 2014Uses and misuses of meta‐analysis in plant ecology 2001Testing and adjusting for publication bias 2006A systematic review identifies a lack of standardization in methods for handling missing variance data

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