Performance of selected imputation techniques for missing variances in meta-analysis

A common method of handling the problem of missing variances in meta-analysis of continuous response is through imputation. However, the performance of imputation techniques may be influenced by the type of model utilised. In this article, we examine through a simulation study the effects of the techniques of imputation of the missing SDs and type of models used on the overall meta-analysis estimates. The results suggest that imputation should be adopted to estimate the overall effect size, irrespective of the model used. However, the accuracy of the estimates of the corresponding standard error (SE) is influenced by the imputation techniques. For estimates based on the fixed effects model, mean imputation provides better estimates than multiple imputations, while those based on the random effects model responds more robustly to the type of imputation techniques. The results showed that although imputation is good in reducing the bias in point estimates, it is more likely to produce coverage probability which is higher than the nominal value.