![trim and fill comprehensive meta analysis trim and fill comprehensive meta analysis](https://media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs11065-021-09480-w/MediaObjects/11065_2021_9480_Fig5_HTML.png)
Trim and fill comprehensive meta analysis software#
Meta-analysts are recommended to perform the trim-and-fill method with great caution when using meta-analysis software programs.
![trim and fill comprehensive meta analysis trim and fill comprehensive meta analysis](https://static.wixstatic.com/media/cb7b6e_24a1bc639c4546a9a35476bbfd813fba~mv2.png)
Outliers and the pre-specified direction of missing studies could have influential impact on the trim-and-fill results. Also, P values produced by different estimators could yield different conclusions of publication bias significance. However, L0 and Q0 failed to converge in a few meta-analyses that contained studies with identical effect sizes. After adding imputed missing studies, the significance of heterogeneity and overall effect sizes changed in many meta-analyses. The estimators L0 and Q0 detected at least one missing study in more meta-analyses than R0, while Q0 often imputed more missing studies than L0. We carefully explored potential issues occurred in our analysis. Moreover, we applied the method to 29,932 meta-analyses from the Cochrane Database of Systematic Reviews, and empirically evaluated its overall performance. We also summarized available meta-analysis software programs for implementing the trim-and-fill method. A resampling method was proposed to calculate P values for all 3 estimators. We used a worked illustrative example to demonstrate the idea of the trim-and-fill method, and we reviewed three estimators (R0, L0, and Q0) for imputing missing studies. Based on real-world meta-analyses, this article provides practical guidelines and recommendations for using the trim-and-fill method. Simulation studies have been performed to assess this method, but they may not fully represent realistic settings about publication bias. The trim-and-fill method is a popular tool to detect and adjust for publication bias. Clinical studies with favorable results are more likely published and thus exaggerate the synthesized evidence in meta-analyses. Publication bias is a type of systematic error when synthesizing evidence that cannot represent the underlying truth.